The correct answer is A. 13.2%.

The total order conversion rate is the ratio of the total number of orders to the total number of clicks, expressed as a percentage. To calculate the total order conversion rate, we need to sum up the clicks and orders from all the channels, and then divide the orders by the clicks and multiply by 100.

Using the data from the table, we can do the following:

Total clicks = 580 + 800 + 1,200 + 300 + 620 = 3,500

Total orders = 55 + 100 + 220 + 60 + 85 = 520

Total order conversion rate = (520 / 3,500) x 100 = 14.857%

Rounding to one decimal place, we get 14.9%

Therefore, the total order conversion rate is 14.9%.

A tab-delimited spreadsheet file is a type of flat text file that uses tabs as delimiters to separate data values in a table. The file extension for a tab-delimited spreadsheet file is usually .tsv, which stands for tab-separated values. Therefore, the correct answer is C. References: [Tab-separated values - Wikipedia], [What is a TSV File? | How to Open, Edit & Convert TSV Files]

The answer is D. Data sources and attributes.

Short explanation: The data analyst should consider the data sources and attributes first when creating a dashboard, because they determine what kind of information can be included and how it can be displayed. The data sources and attributes define the origin, quality, format, and structure of the data that will be used for the dashboard.They also affect the data refresh rate, the consumer types, and the access permissions of the dashboard12

A. Data refresh rate is not the first thing to consider, because it depends on the data sources and attributes. The data refresh rate is how often the data in the dashboard is updated or refreshed to reflect the latest changes.The data refresh rate can vary depending on the type, frequency, and availability of the data sources1

B. Consumer types are not the first thing to consider, because they depend on the data sources and attributes. The consumer types are the intended audiences or users of the dashboard, who may have different needs, preferences, and expectations for the dashboard. The consumer types can influence the design, layout, and functionality of the dashboard.However, the consumer types cannot be determined without knowing what kind of data is available and relevant for them1

C. Access permissions are not the first thing to consider, because they depend on the data sources and attributes. The access permissions are the rules or policies that govern who can view, edit, or share the dashboard. The access permissions can protect the confidentiality, integrity, and availability of the data in the dashboard.However, the access permissions cannot be set without knowing what kind of data is involved and who needs to access it1

The option that is not considered an aggregate function is SELECT. An aggregate function is a function that performs a calculation on a set of values and returns a single value. Examples of aggregate functions are SUM, MIN, MAX, AVG, COUNT, etc. SELECT is not an aggregate function, but a SQL command that is used to select data from a table or a query. Reference: SQL Aggregate Functions - W3Schools

The rating variable in a survey where participants rate a company on a scale of one to ten is best described as ordinal. This is because the ratings are ranked in order, with each number representing a position on a scale of satisfaction or quality. The numbers are not just labels (which would be nominal), nor do they represent a continuous spectrum (which would be continuous). They also do not fit the definition of categorical, as that implies non-ordered groups or categories. In an ordinal scale, the order of the values is significant and meaningful12.

References:

Qualtrics explains that ordinal scales have answer sets that occur in a logical and systematic order, providing qualitative data.

Zonka Feedback describes a 1 to 10 rating scale survey, indicating that the numbers represent a ranking from most negative to most positive experience, which aligns with the characteristics of an ordinal scale.

Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. References: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples

The missing step in the Extract, Transform, Load (ETL) process is typically the cleaning step, which involves removing redundant data or deduplication. This step is crucial in the ETL process to ensure that the data loaded into the destination is accurate and not inflated by duplicate records. The other options, like validating data types and connecting to the data API, are important but do not fit into the standard ETL process steps as a cleaning operation. Normalizing the data is part of the 'Transform' step, which was already listed.

The action that must be done to the Genre column before this task can be completed is delimit. Delimit is a process of separating or splitting a string of text into multiple parts based on a delimiter, which is a character or a sequence of characters that marks the boundary between the parts. For example, a comma (,) or a semicolon (;) can be used as a delimiter. In this case, the Genre column contains multiple genres for each movie, separated by commas. To determine the most popular movie genre, the data analyst needs to delimit the Genre column by commas, so that each genre can be counted and compared separately. The other options are not relevant for this task, as they are related to combining or joining strings or tables, not separating them. Append is a process of adding or attaching one string or table to the end of another string or table. Merge is a process of combining or joining two or more tables into one table based on a common column or key. Concatenate is a process of joining or linking two or more strings together into one string. Reference: [How to Split Text in Excel - Exceljet]

Data aggregation is the process of compiling data from multiple sources and summarizing it into a single dataset. Data aggregation can be used to calculate statistics, such as averages, sums, counts, or percentages. In this case, the analyst must obtain the average daily sales for the following week, which is a statistic that can be calculated by aggregating the sales data from each day and dividing by the number of days. Data aggregation can be done using various tools and methods, such as spreadsheets, databases, or programming languages.

The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a betterunderstanding of the data and its quality, as well as prepare the data for further analysis.

A star schema is a type of database schema that consists of one fact table and multiple dimension tables. The fact table contains the measures or metrics of the business process, such as sales, orders, or transactions. The dimension tables contain the attributes or characteristics of the business entities, such as products, customers, or locations. The fact table is connected to the dimension tables by foreign keys that reference the primary keys of the dimension tables. The fact table is located at the center of the schema, while the dimension tables are located at the edges, forming a star-like shape1.

A star schema is an example of a denormalized schema, which means that the dimension tables are not normalized and may contain redundant or repeated data. This is done to improve the performance and simplicity of queries, as there are fewer joins and tables involved. A star schema is suitable for data warehouses and business intelligence applications that require fast and efficient data retrieval2.

The correct answer is B. Snowflake.

A snowflake schema is a type of database schema that features normalized dimension tables. A database schema is a way of organizing and structuring the data in a database. Adimension table is a table that contains descriptive attributes or characteristics of the data, such as product name, category, color, etc.A normalized table is a table that follows the rules of normalization, which is a process of reducing data redundancy and improving data integrity by organizing the data into smaller and simpler tables12

A snowflake schema is a variation of the star schema, which is another type of database schema that features denormalized dimension tables. A denormalized table is a table that does not follow the rules of normalization, and may contain redundant or duplicated data. A star schema consists of a central fact table that contains quantitative measures or facts, such as sales amount, order quantity, etc., and several dimension tables that are directly connected to the fact table.A snowflake schema differs from a star schema in that the dimension tables are further split into sub-dimension tables, creating a snowflake-like shape13

A snowflake schema has some advantages and disadvantages over a star schema. Some advantages are:

It reduces the storage space required for the dimension tables, as it eliminates the redundant data.

It improves the data quality and consistency, as it avoids the update anomalies that may occur in denormalized tables.

It allows more detailed analysis and queries, as it provides more levels of dimensions.

Some disadvantages are:

It increases the complexity and number of joins required to retrieve the data from multiple tables, which may affect the query performance and speed.

It reduces the readability and simplicity of the schema, as it has more tables and relationships to understand.

It may require more maintenance and administration, as it has more tables to manage and update13

The answer is A. Outlier.

Short explanation: An outlier is a data point that differs significantly from the rest of the data in a dataset. An outlier can indicate an error, an anomaly, or a rare event in the data.An outlier can affect the statistical analysis and visualization of the data, such as skewing the mean, variance, or distribution of the data. Therefore, data should be cleaned to identify and remove or correct any outliers.

The image below shows a box plot graph with a vertical axis labeled “Customer Calls” and a horizontal axis labeled “Churn”. The box plot is blue in color and the median value is around 2. There are 7 outliers above the box plot, ranging from 4 to 8.

image)

A box plot is a type of graph that can show the distribution of data values using five summary statistics: minimum, maximum, median, first quartile, and third quartile. The box represents the interquartile range (IQR), which is the difference between the first and third quartiles. The median is shown as a line inside the box. The whiskers extend from the box to the minimum and maximum values, excluding any outliers. Outliers are shown as dots or circles outside the whiskers.

In this graph, we can see that most of the customer calls are between 0 and 4, with a median of 2. However, there are 7 outliers that have more than 4 customer calls, up to 8. These outliers may indicate some customers who have more issues or complaints than others, or some errors or anomalies in the data collection or recording process. These outliers can affect the analysis and interpretation of the customer calls and churn relationship, such as making it seem that more customer calls lead to less churn, which may not be true for the majority of the customers. Therefore, data should be cleaned to investigate and handle these outliers appropriately.

Explanation

SPSS Modeler.

QuickSight, Tableau, and Power BI are all powerful analytics and reporting tools that can pull data from a variety of sources. SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise.

Explanation

INSERT

The INSERT command is used to add new records to a database table.

The SELECT command is used to retrieve information from a database. It's the most commonly used command in SQL because it is used to pose queries to the database and retrieve the data that you're interested in working with.

The UPDATE command is used to modify rows in the database.

The CREATE command is used to create a new table within your database or a new database on your server.

Explanation

The correct answer is B: Derived variables Derived variables are variables that you create by calculating or categorizing variables that already exist in your data set.

Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set. Data blending is incorrect.

Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis.

Data append is incorrect. A data append is a process that involves adding new data elements to an existing database.

A JSON (JavaScript Object Notation) file is a text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). JSON files are human-readable and can be interpreted by various programming languages, making them ideal for data interchange123.

JSON files typically contain an array of objects, with each object representing a record with a series of name-value pairs. This structured format is both easy to understand and write by humans and easy for machines to parse and generate4.

References:

JSON’s official definition and syntax rules1.

A beginner’s guide to JSON and its data types2.

Understanding the JSON file format3.

Detailed explanation of JSON as a structured data format4.

 Creating a new report that automatically calculates weekly totals would streamline the process for the recipient. By setting this report to run at the end of business on Friday, it would provide the recipient with the necessary information for the entire week in one consolidated document. This eliminates the need for manual calculations and combines the previous week’s data into one report, making it more efficient and less time-consuming.

References:

Best practices in business analytics suggest automating repetitive tasks and consolidating reports where possible to improve efficiency and reduce the potential for human error.

A data dictionary is a collection of metadata that describes the data elements in a database or dataset. It can help improve data acquisition by providing information about the data sources, formats, quality, and usage. It can also help remember specifics about data fields, such as their names, definitions, types, sizes, and relationships. Therefore, options B and D are correct.

Option A is incorrect because it is not a reason to create and maintain a data dictionary, but a benefit of doing so.

Option C is incorrect because specifying user groups for databases is not a function of a data dictionary, but a function of a database management system or a security policy.

Option E is incorrect because confining breaches of PHI data is not a function of a data dictionary, but a function of a data protection or encryption system.

Option F is incorrect because reducing processing power requirements is not a function of a data dictionary, but a function of a data compression or optimization system.

Sampling refers to the process of selecting a representative subset of data from a larger data set or repository. This technique is used when it is impractical or unnecessary to analyze the entire set of data. A representative sample should accurately reflect the characteristics of the larger population, allowing for analysis and inference about the population as a whole12.

Observation (A) generally refers to the act of monitoring or recording data. Delta load (B) is a term used in data warehousing to describe the process of loading only the changes since the last data extraction, rather than the entire data set. Web scraping © is the process of extracting data from websites.

References:

Understanding the importance of data sampling1.

The concept of a representative sample in statistics2.

Data repository management and usage3.

Benefits and methods of data sampling4.

This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:

=IF (condition, value_if_true, value_if_false)

The other data manipulation techniques are not examples of logical functions. Here is why:

WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:

AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:

BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is:

Data accuracy refers to the extent to which the data is correct, reliable, and free of errors. When different people manually type a series of handwritten surveys into an online database, there is a high chance of human error, such as typos, misinterpretations, omissions, or duplications. These errors can affect the quality and validity of the data and lead to incorrect or misleading analysis and decisions.

Data consistency refers to the extent to which the data is uniform and compatible across different sources, formats, and systems. When different people manually type a series of handwritten surveys into an online database, there is a high chance of inconsistency, such as different spellings, abbreviations, formats, or standards. These inconsistencies can affect the integration and comparison of the data and lead to confusion or conflicts.

Therefore, to ensure data quality, it is important to have clear and consistent rules and procedures for data entry, validation, and verification. It is also advisable to use automated tools or methods to reduce human error and inconsistency.

The term ‘4Ac1’ is a combination of numbers and letters, which fits the definition of an alphanumeric string. Alphanumeric refers to a character set that contains both letters and numbers. In data analytics and programming, such a value is typically treated as a string, which is a sequence of characters. Strings can include letters, digits, and various other symbols.

A numeric data type would only include numbers, and a float is a specific kind of numeric data type that includes decimal points, neither of which applies to ‘4Ac1’. A boolean data type represents one of two values: true or false. Since ‘4Ac1’ does not represent a true or false value, it cannot be classified as boolean. Lastly, symbolic is not a standard data type in the context of programming and data analytics.

References:

Understanding Python 3 data types1.

Basic Data Types in Python2.

Java Data Types3.

A name is an example of personally identifiable information (PII), which is any data that can be used to identify someone, either on its own or with other relevant data. A name is a direct identifier, which means that it can uniquely identify a person without the need for any additional information. For example, a full name, such as John Smith, can be used to distinguish or trace an individual’s identity1.

Other examples of direct identifiers include:

Social Security Number

Passport number

Driver’s license number

Email address

Phone number

Data profiling is the process of examining data for statistics and information about the data, such as the structure, format, quality, and content of the data. Data profiling can help to understand the characteristics, patterns, relationships, and anomalies of the data, as well as to identify and resolve any errors, inconsistencies, or missing values in the data. Data profiling can be done using various tools and methods, such as spreadsheets, databases, or programming languages12.

Comprehensive and Detailed In-Depth Explanation:

A confidence interval provides a range within which we expect a population parameter to lie, based on sample data. Increasing the confidence level means we want to be more certain that the interval contains the parameter, which affects the margin of error.

Option A:The margin of error does not change.

Rationale:This is incorrect. Increasing the confidence level requires a wider interval to maintain the higher degree of certainty, which increases the margin of error.

Option B:The interval remains the same.

Rationale:This is incorrect. To achieve a higher confidence level, the interval must widen, altering its range.

Option C:The interval becomes narrower.

Rationale:This is incorrect. A higher confidence level necessitates a wider interval to ensure the parameter is captured within the range.

Option D:The margin of error increases.

Rationale:Correct. Increasing the confidence level from 90% to 95% means we are more confident that the interval contains the true parameter. To achieve this increased confidence, the margin of error must increase, resulting in a wider interval.

 In technical documentation, a glossary is the designated section where definitions for technical terms are provided. It serves as a reference point for readers to understand specialized or uncommon words used within the document. Including descriptions of technical terms in a glossary ensures that readers have a consistent resource to refer to, which can improve comprehension and reduce misunderstandings12.

A system diagram (Option B) is a visual representation of the system’s components and their interactions, not a place for defining terms. User requirements (Option C) outline what end-users expect from the system, and an index (Option D) is an alphabetical list of topics covered in the document, usually with page numbers, but not definitions.

References:

Creating effective technical documentation1.

Best practices when writing technical descriptions3.

Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patientsin a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.

Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

The table provided shows an inconsistency in the ‘Gender’ column, which lists three distinct values: Male, Female, and College. This is inconsistent because ‘College’ is not a gender category. The ‘Gender’ column should only have two distinct values, typically ‘Male’ and ‘Female’, to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection.

In data analysis, it’s crucial to ensure that categorical variables like gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.

References:

Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender1.

Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets2.

The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results3.

Explanation

Data encryption can be used to translate data into another form so it can only be read by a user who has a key or a password. Data encryption is a process of transforming data using an algorithm or a cipher to make it unreadable to anyone except those who have the key or the password to decrypt it. Data encryption is a common method of protecting data from unauthorized access, modification, or theft. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

The invalid data type is the best description for the issue in which character values are mixed with integer values in a data set column. Invalid data type means that the data does not match the expected or required format or structure for a given variable or attribute. For example, if a columnis supposed to store numerical values, but some rows contain text values, then those rows have an invalid data type. References: CompTIA Data+ Certification Exam Objectives, page 10

The correct answer is D. Continuous data.

Continuous data is a type of quantitative data that can take any value within a range and can be measured with infinite precision. Continuous data can be expressed as fractions, decimals, or percentages.Examples of continuous data are height, weight, temperature, time, speed, etc12

The duration of a phone call in milliseconds is an example of continuous data, because it can take any value within a range (from zero to infinity) and can be measured with infinite precision (up to milliseconds or even smaller units). The duration of a phone call in milliseconds can also be expressed as fractions, decimals, or percentages of a larger unit (such as seconds, minutes, or hours).

Ordinal data is not correct, because ordinal data is a type of qualitative or categorical data that can be ordered or ranked according to some criterion. Ordinal data can have a logical order, but the intervals between the values are not equal or meaningful.Examples of ordinal data are grades, ratings, ranks, etc12

Nominal data is not correct, because nominal data is a type of qualitative or categorical data that can be labeled or named without any order or ranking. Nominal data can have a finite number of categories or classes, but the categories have no intrinsic value or hierarchy.Examples of nominal data are gender, color, nationality, etc12

Boolean data is not correct, because boolean data is a type of binary data that can have only two possible values: true or false. Boolean data can be used to represent logical statements, conditions, or outcomes. Examples of boolean data are yes/no, on/off, 1/0, etc.

Explanation

Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it's a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve.

 A Pie chart is the most suitable for representing the average spending of active customers versus inactive customers. Pie charts are effective for comparing parts of a whole, which makes them ideal for visually displaying the proportion of spend between two distinct groups. They are widely used to depict percentage distributions and are straightforward, allowing immediate analysis of the active vs. inactive customer spending distribution at a glance.

Comprehensive and Detailed In-Depth Explanation:

When undertaking the task of revamping a long-standing company-wide dashboard, it's imperative to begin by understanding the specific needs and objectives of the stakeholders who requested the update. This ensures that the revamped dashboard aligns with current business requirements and provides relevant insights.

Option A:Talk to the group that made the request to determine the desired goal.

Rationale:Engaging with the requesting group allows the analyst to gather detailed information about their expectations, the challenges they face with the current dashboard, and the specific metrics or data visualizations they require. This collaborative approach ensures that the final product is user-centric and meets the actual needs of its users.

Answer: C. 2019 goal data

To report how the sales team is performing to target, the sales analyst needs to compare the actual sales revenue with the expected or planned sales revenue for the same period. The 2019 goal data is the file that contains the expected or planned sales revenue for the year 2019, which is the target that the sales team is aiming to achieve. By comparing the 2019 goal data with the 2019 actual revenue, the sales analyst can calculate the performance attainment, which is the percentage of the goal that was met by the sales team.

Option A is incorrect, as 2018 goal data is not relevant for determining 2019 performance attainment. The 2018 goal data contains the expected or planned sales revenue for the year 2018, which is not the target that the sales team is aiming to achieve in 2019.

Option B is incorrect, as 2018 actual revenue is not relevant for determining 2019 performance attainment. The 2018 actual revenue contains the actual sales revenue for the year 2018, which is not comparable with the 2019 goal data or the 2019 actual revenue.

Option D is incorrect, as 2019 commission plan is not relevant for determining 2019 performance attainment. The 2019 commission plan contains the rules and rates for calculating and paying commissions to the sales team based on their performance attainment, but it does not contain the expected or planned sales revenue for the year 2019.

Comprehensive and Detailed In-Depth Explanation:

Data governance encompasses the overall management of data availability, usability, integrity, and security within an organization. It involves establishing policies and procedures to ensure that data is managed effectively and consistently across the organization.

Option A:Data administration

Rationale:Data administration focuses on the technical aspects of managing data assets, including database management and maintenance. While important, it does not encompass the broader policy-making scope of data governance.

Option B:Data stewardship

Rationale:Data stewardship involves overseeing the lifecycle of data, ensuring its quality and proper usage. Stewards implement the policies set forth by data governance but do not define those policies themselves.

Option C:Data ownership

Rationale:Data ownership assigns responsibility for specific data assets to individuals or departments. Owners are accountable for the data but do not establish the overarching policies and procedures.

Option D:Data governance

Rationale:Data governance is the framework that defines the policies and procedures for managing master data, ensuring consistency, quality, and protection across the organization.

For efficient data manipulation, the ideal order would be to first merge related tables to create a comprehensive set of records, then deduplicate to remove any redundant information. Lastly, appending additional data, such as from another source or table, ensures that all relevant data is included without redundancy before the final analysis. This order prevents unnecessary duplication of effort, such as deduplicating both before and after appending, which would be less efficient.

In the context of the tables provided, merging would likely involve combining customer information from the online and in-store transaction tables with the customer table. Deduplication would remove any redundant customer records that may exist across these tables. Finally, appending would involve adding any additional transaction records to the master file, ensuring a complete dataset for analysis.

This is because missing data is a type of data quality issue that occurs when data is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis or process. Missing data can be caused by various factors, such as human error, system error, or non-response. Missing data can be addressed by using various methods, such as replacing missing data, which means filling in or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression. The other methods are not used to address missing data. Here is why:

Remove duplicate data is a type of method that eliminates or reduces duplicate data, which is a type of data quality issue that occurs when data is repeated or copied in a data set. Removing duplicate data does not address missing data, but rather affects the quantity and validity of the data.

Replace redundant data is a type of method that eliminates or reduces redundant data, which is a type of data quality issue that occurs when data is unnecessary or irrelevant for the analysis or purpose. Replacing redundant data does not address missing data, but rather affects the efficiency and performance of the analysis or process.

Remove invalid data is a type of method that eliminates or reduces invalid data, which is a type of data quality issue that occurs when data is incorrect or inaccurate in a data set. Removing invalid data does not address missing data, but rather affects the validity and reliability of the analysis or process.

Comprehensive and Detailed In-Depth Explanation:

The RefNo column contains values that consist solely of digits without any alphabetical characters or special symbols, indicating a numeric data type.

Systematic sampling is a type of sampling where the sample is selected by following a fixed interval. For example, every 10th person in a list is chosen for the sample. In the image, the sample is selected by choosing every 3rd person in the line, starting from person number 1. This is an example of systematic sampling. References: Types of Sampling Techniques in Data Analytics You Should Know, Sampling Methods | Types, Techniques & Examples - Scribbr

Explanation

The correct answer is B: Data blending.

Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization and can then be processed or analyzed. Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question. Data append is incorrect. Data append is a process that involves adding new data elements to an existing database. An example of a common data append would be the enhancement of a company's customer files. A data append takes the information they have, matches it against a larger database of business data, allowing the desired missing data fields to be added. Normalize data is incorrect.

Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set.

This is because a bar chart is a type of chart that shows the value or the amount of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A bar chart can be used to display and analyze the comparison, ranking, or proportion among the categories or groups, as well as identify any differences, similarities, or outliers in the data. For example, a bar chart can show which site has the highest or lowest percentage of new customers, as well as show how much each site contributes to the total percentage of new customers. The other types of charts are not the best charts to display the data. Here is why:

A line chart is a type of chart that shows the change or the trend of a single variable over time, such as the percentage of new customers over months or years in this case. A line chart can be used to display and analyze the movement, cycle, or pattern of the variable, as well as identify any peaks, valleys, or fluctuations in the data. For example, a line chart can show how the percentage of new customers increases or decreases over time, as well as show if there are any seasonal or periodic variations in the data.

A pie chart is a type of chart that shows the proportion or the percentage of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A pie chart can be used to display and analyze the composition, distribution, or share of the variable, as well as identify any segments, slices, or fractions in the data. For example, a pie chart can show how much each site represents of the total percentage of new customers, as well as show if there are any dominant or minor sites in the data.

A scatter chart is a type of chart that shows the relationship between two variables for each observation or unit in a data set, such as the percentage of new customers and another variable for each site in this case. A scatter chart can be used to display and analyze the correlation, trend, or pattern among the variables, as well as identify any outliers or clusters in the data. For example, a scatter chart can show if there is a positive, negative, or no correlation between the percentage of new customers and another variable, such as salesrevenue or customer satisfaction.

The date on which the report was run. This is because the time period the report covers and the date on which the report was run are two components that need to be added to ensure the report is point-in-time and static, which means that the report shows the data as it was at a specific moment or interval in time, and does not change or update with new data. By adding the time period the report covers and the date on which the report was run, the analyst can indicate when and for how long the data was collected and analyzed, as well as avoid any confusion or ambiguity about the currency or validity of the data. The other components do not need to be added to ensure the report is point-in-time and static. Here is why:

A control group for the phrases is a type of group that serves as a baseline or a reference for comparison with another group that is exposed to some treatment or intervention, such as a target phrase in this case. A control group for the phrases does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a control group for the phrases could be useful for evaluating the effectiveness or impact of the target phrases on customer satisfaction or retention.

A summary of the KPIs is a type of document that provides an overview or a highlight of the key performance indicators (KPIs), which are measurable values that indicate how well an organizationor a process is achieving its goals or objectives. A summary of the KPIs does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a summary of the KPIs could be useful for communicating or presenting the main findings or insights from the report.

Filter buttons for the status are a type of feature or function that allows users to select or deselect certain values or categories in a column or a table, such as ticket statuses in this case. Filter buttons for the status do not need to be added to ensure the report is point-in-time and static, because they do not affect the time frame or the stability of the data. However, filter buttons for the status could be useful for exploring or analyzing different aspects or segments of the data.

Two reasons to conduct data cleansing are:

To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable.This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12

To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the data.This can help to identify and measure the patterns, changes, or relationships in the data over time13

Filter on any of the responses that do not say “January” and update them to “January”. This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say “January” and updating them to “January”, the analyst can make sure that all the responses for the month of January are written in the same way. The other steps arenot appropriate for ensuring data consistency. Here is why:

Deleting any of the responses that do not have “January” written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.

Replacing any of the responses that have “01” would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “Jan” and “January”. This could cause confusion and errors in the analysis.

Sorting any of the responses that say “Jan” and updating them to “01” would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “01” and “January”. This could also cause confusion and errors in the analysis.

The correct answer is B. Recoding.

Recoding is a data manipulation technique that involves changing the values or categories of a variable to make it more suitable for analysis.Recoding can be used to simplify or group the data, to correct errors or inconsistencies, or to create new variables from existing ones12

In the example, the analyst used recoding to change the values of Var001, Var002, Var003, and Var004 from numerical to textual form. The analyst also used recoding to assign meaningful labels to the values, such as “Absent” for 0, “Present” for 1, “Low” for 2, “Medium” for 3, and “High” for 4. This makes the data more understandable and easier to analyze.

The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula:

Celsius = (Fahrenheit - 32) * 5/9

This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by 5/9. For example, if the temperature is 68°F, the rescaled value in Celsius is:

Celsius = (68 - 32) * 5/9 Celsius = 20°C

Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales.Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.

Explanation

Cross-site Scripting (XSS) is a security vulnerability usually found in websites and/or web applications that accept user input.

XSS is a client-side vulnerability that targets other application users, while SQL injection is a server-side vulnerability that targets the application's database. How do I prevent XSS in PHP? Filter your inputs with a whitelist of allowed characters and use type hints or type casting.

Explanation

While this scenario describes a system integration challenge that can be solved with ETL or ELT, Angela is facing a Record linkage issue.

Comprehensive and Detailed In-Depth Explanation:

When selecting a tool to create automatically refreshing reports accessible to a broad audience, it's essential to consider features such as user-friendly interfaces, robust data visualization capabilities, and ease of sharing.

Option A:R

Rationale: R is a powerful statistical programming language used for data analysis and visualization. While it offers extensive capabilities, creating interactive, automatically refreshing reports requires additional packages and considerable programming expertise. Moreover, sharing R-based reports with non-technical users can be challenging, as it may necessitate specialized software or environments.

Option B:Excel

Rationale: Microsoft Excel is widely used for data analysis and offers features like pivot tables and basic charting tools. However, setting up automatic data refreshes in Excel can be complex, especially when dealing with large datasets or multiple data sources. Additionally, sharing Excel files across a large organization can lead to version control issues and may not provide the level of interactivity desired.

Option C:Tableau

Rationale: Tableau is a leading data visualization tool designed to create interactive and shareable dashboards. It supports automatic data refreshing and allows users to publish dashboards to Tableau Server or Tableau Online, making them easily accessible to the entire sales organization. Tableau's user-friendly interface enables analysts to develop complex visualizations without extensive programming knowledge.

Explanation

Performance analysis is the technique of studying or comparing the performance of a specific situation in contrast to the aim and yet executed. In Human Resources, performance analysis can help to review an employee's contribution towards a project or assignment, which they allotted him or her.

A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

ETL is a process that is used during data integration to collect, blend, and load data. ETL stands for extract, transform, and load, which are the three main steps involved in moving data from different sources to a common destination, such as a data warehouse or a data lake. ETL helps to consolidate and standardize data for analysis and reporting purposes. References: CompTIA Data+ Certification Exam Objectives, page 12

This is because data encryption is a type of control measure that prevents a data breach, which is an unauthorized or illegal access or use of data by an external or internal party. Data encryption can prevent a data breach by protecting and securing the data using a code or a key that scrambles or transforms the data into an unreadable or incomprehensible format, which can only be decoded or restored by authorized users who have the correct code or key. For example, data encryption can prevent a data breach by encrypting the data in transit or at rest, such as when the data is sent over a network or stored in a device. The other control measures are not used for preventing a data breach.Here is why:

Data transmission is a type of process that transfers and exchanges data between different sources or systems, such as databases, cloud services, or web applications. Data transmission does not prevent a data breach, but rather exposes the data to potential risks or threats during the transfer or exchange. However, data transmission can be made more secure and less vulnerable to a data breach by using encryption or other methods, such as authentication or authorization.

Data attribution is a type of feature or function that assigns and tracks the ownership and origin of the data, such as the creator, modifier, or source of the data. Data attribution does not prevent a data breach but rather provides information and evidence about the data provenance and history. However, data attribution can be useful for detecting and responding to a data breach by using audit logs or metadata to identify and trace any unauthorized or illegal access or use of the data.

Data retention is a type of policy or standard that specifies and regulates the storage and preservation of the data, such as the duration, location, or format of the data. Data retention does not prevent a data breach, but rather affects the availability and accessibility of the data for future use or reference. However, data retention can be optimized and aligned with the legal and ethical requirements and standards of the industry or the organization to reduce the risk or impact of a data breach.

Merging tables to create a master file that includes all transactions for both online and in-store sales is best described as data consolidation. This process involves combining data from various sources into a single, unified dataset. Data consolidation is essential for providing a comprehensive view of all transactions, which can be used for analysis, reporting, and decision-making purposes.

References: The answer is based on standard data management practices and the definition of data consolidation. No specific external documents were referenced for this response.

The HTML commands that would help the developer to complete the task successfully are

Upload Your Short Videos

tag defines a heading level 1, which is the largest and most important heading on a webpage. The tag defines a clickable button that can perform some action when clicked. The other options are not suitable for the task, as they either use the wrong tags or do not create a clickable button. The tag defines a section of text with no specific meaning or formatting. The

tag defines a paragraph of text. The tag does not exist in HTML. Reference: HTML Tags - W3Schools

Customer segmentation is a type of cluster analysis, which is a method of grouping data points based on their similarities or differences. Cluster analysis can help identify patterns and trends in the data, as well as target specific groups of customers for marketing purposes. One common technique for customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value. This technique assigns a score to each customer based on how recently they bought the product, how often they buy the product, and how much they spend on the product. These scores can then be used to create clusters of customers with different characteristics and preferences. Therefore, the correct answer is D. References: Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate Guide for Customer Segmentation

Comprehensive and Detailed In-Depth Explanation:

When designing a database field to capture a binary preference, such as whether a customer likes vanilla ice cream, the most appropriate data type is:

Option B:Boolean

Rationale:A Boolean data type is used to represent binary values, typically TRUE or FALSE. In this context, it efficiently captures whether a customer likes (TRUE) or does not like (FALSE) vanilla ice cream.

Option A:Integer

Rationale:While integers represent whole numbers, using them to denote binary choices (e.g., 1 for "likes" and 0 for "dislikes") is less intuitive and can lead to ambiguity without proper context.

Option C:Categorical

Rationale:Categorical data types are used for fields that can take on one of a limited set of values, representing different categories. While "likes" and "dislikes" could be categories, a Boolean is more efficient for binary choices.

Option D:Numeric

Rationale:Numeric data types encompass both integers and floating-point numbers. Using a numeric type for a binary preference is unnecessary and could lead to data integrity issues.

Comprehensive and Detailed In-Depth Explanation:

When analyzing relationships betweentwo numeric variables, the best visualization is ascatter plot, as it helps to:

Identifycorrelations(positive, negative, or no correlation).

Detectclustering patternsin data points.

Spotoutliersthat deviate significantly from the general trend.

Option A (Heat map):Incorrect. Heat maps are used for visualizing intensity differences across categories, not for identifying patterns between two numeric variables.

Option B (Tree map):Incorrect. Tree maps visualize hierarchical relationships, not scatter patterns.

Option C (Scatter plot):Correct.Scatter plots are best suited for examining relationships and identifying clustering patterns and outliers.

Option D (Stacked chart):Incorrect. Stacked charts are used to display the composition of multiple data series, not relationships between two numeric variables.

Explanation

A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.

What is the mode?

The mode is the most commonly occurring value in a distribution.

The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.

Data governance refers to the overarching management of data’s availability, usability, integrity, and security within an organization. It involves setting policies and standards that govern data usage, determining data ownership, implementing data security measures, and ensuring that data is accessible for business insights while maintaining its quality. The goal of data governance is to ensure that data is consistent, trustworthy, and not misused, supporting compliance with data privacy regulations and enabling effective data analytics to optimize operations and drive business decision-making.

References:

Understanding Data Governance and Its Importance1.

The Role of Data Governance in Data Management2.

Defining Data Governance and Its Business Value3.

Explanation

Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses.

Answer: C. Review the business questions to understand the scope.

The first step an analyst should perform upon receiving a business request for analysis is to review the business questions to understand the scope of the problem, the objectives, and the expected outcomes. This will help the analyst to define the analytical approach, identify the data needs and sources, and plan the analysis process.Reviewing the business questions will also help the analyst to communicate with the stakeholders and clarify any assumptions or ambiguities1.

Option A is incorrect, as determining the data needs and sources for analysis is not the first step, but rather a subsequent step that depends on the business questions and the analytical approach.

Option B is incorrect, as initiating the analysis for exploratory data analysis is not the first step, but rather a part of the analysis process that involves examining and summarizing the data, identifying patterns and outliers, and testing hypotheses.

Option D is incorrect, as finalizing the methodology to solve the problem is not the first step, but rather a later step that involves selecting and applying the appropriate analytical techniques, tools, and models to answer the business questions.

The correct answer is D. Spearman’s rank correlation.

Spearman’s rank correlation is a non-parametric test that measures the strength and direction of the relationship between two variables that are ranked (ordinal) or continuous. Spearman’s rank correlation does not assume that the data follows a normal distribution or that the variables are linearly related.Spearman’s rank correlation is based on the ranks of the data rather than the actual values12

A. One-sample t-test is not correct, because it is a parametric test that compares the mean of a sample to a specified value.One-sample t-test assumes that the data follows a normal distribution and has a known population standard deviation34

B. Two-way ANOVA is not correct, because it is a parametric test that compares the means of two or more groups that are influenced by two independent factors. Two-way ANOVA assumes that the data follows a normal distribution, has homogeneous variances, and has independent observations.

C. Correlation coefficient is not correct, because it is a parametric test that measures the strength and direction of the linear relationship between two continuous variables. Correlation coefficient assumes that the data follows a bivariate normal distribution and has no outliers.

Comprehensive and Detailed In-Depth Explanation:

Access control is a critical aspect of database security. The best method for controllingwho can access financial databased onjob rolesisRole-Based Access Control (RBAC).

Option A (Role-based):Correct.RBAC assigns permissions based on a user’s role within the organization (e.g., accountants can access financial data, but sales representatives cannot).

Option B (Rule-based):Incorrect. Rule-Based Access Control (RuBAC) enforces policies based on rules, such as time restrictions, rather than user roles.

Option C (Discretionary):Incorrect. Discretionary Access Control (DAC) allows individual users to grant permissions, which can lead to security risks in financial systems.

Option D (Group-based):Incorrect. Group-Based Access Control (GBAC) assigns permissions based on user groups, but RBAC provides finer control for financial functions.

Comprehensive and Detailed In-Depth Explanation:

Handling missing datais crucial for maintaining the integrity of an analysis. Since the missing value belongs to anemployedindividual, the most appropriate method is toimpute the mean income of employed citizens.

Option A (Replace the missing value with the average of unemployed citizens):Incorrect. The missing income is for anemployedindividual, so it would be inappropriate to use the unemployed citizens' average.

Option B (Insert 0):Incorrect. Assigning 0 would be misleading since it does not reflect the income distribution for employed citizens.

Option C (Impute the mean of the other citizens' incomes):Correct.A common practice in data analytics ismean imputation, where missing values are replaced with the mean of similar cases (in this case, other employed citizens).

Option D (Exclude employed citizens from the analysis):Incorrect. This would remove valuable data and lead to biased results.

The best answer is A. JSON is quicker to read and write.

JSON (JavaScript Object Notation) is a lightweight data-interchange format that is based on the JavaScript programming language and easy to understand and generate. JSON uses a simple syntax that consists of name-value pairs and arrays, and does not require any end tags or attributes. JSON is quicker to read and write than XML (Extensible Markup Language), which is a markup language that uses a tag structure to represent data items.XML has a more complex and verbose syntax that requires end tags, attributes, and namespaces123

Comprehensive and Detailed In-Depth Explanation:

In the context of financial systems and reporting tools, ensuring high data quality is paramount. The most critical dimensions of data quality in this scenario include:

Option B:Data accuracy

Rationale:Data accuracy refers to the correctness and precision of the data. In financial systems, inaccurate data can lead to erroneous reports, affecting decision-making and potentially leading to financial losses. Ensuring that data accurately reflects real-world values is essential.

Option E:Data completeness

Rationale:Data completeness ensures that all necessary data is present and accounted for. Missing data can result in incomplete analyses and reports, which can misinform stakeholders and lead to suboptimal decisions.

Option F:Data consistency

Rationale:Data consistency involves maintaining uniformity and coherence of data across different systems and reports. Inconsistent data can cause discrepancies in financial reporting, undermining trust in the data and leading to confusion.

Comprehensive and Detailed In-Depth Explanation:

When incorporating new customer information from a survey into an existing customer contacts database, the appropriate data manipulation technique is:

Option B:Append

Rationale:Appending involves adding new records to the end of an existing dataset without altering the existing data. In this scenario, the analyst adds the new customer information as additional records in the database.

Option A:Join

Rationale:Joining combines data from two or more tables based on a related column, creating a new dataset with combined information. This is not necessary when simply adding new records.

Option C:Transform

Rationale:Transformation involves changing the format, structure, or values of data. While important in data processing, it doesn't pertain to adding new records.

Option D:Blend

Rationale:Data blending combines data from different sources into a single dataset, often used in analysis rather than updating a database with new records.

This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:

The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:

Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.

Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.

Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.

It is structured in nature. This is because a relational database is a type of database that organizes data into tables, which consist of rows and columns. A relational database is structured in nature, which means that the data has a predefined schema or format, and follows certain rules and constraints, such as primary keys, foreign keys, or referential integrity. A relational database can be used to store, query, and manipulate data using a structured query language (SQL). The other characteristics are not true for a relational database. Here is why:

It utilizes key-value pairs. This is not true for a relational database, because key-value pairs are a way of storing data that associates each value with a unique key, such as an identifier or a name. Key-value pairs are typically used in non-relational databases, such as NoSQL databases, which do not have tables, rows, or columns, but rather store data in various formats, such as documents, graphs, or columns.

It has undefined fields. This is not true for a relational database, because fields are another name for columns in a table, which define the attributes or properties of each row or record in the table. Fields have defined names, types, and lengths in a relational database, which specify the format and size of the data that can be stored in each field.

It uses minimal memory. This is not true for a relational database, because memory is the amount of space or storage that is used by a database to store and process data. Memory usage depends on various factors, such as the size, complexity, and number of tables and queries in a relational database. A relational database can use a lot of memory if it has many tables with many rows and columns, or if it performs complex or frequent queries on the data.

The best answer is C. Microsoft Power BI.

Microsoft Power BI is a business analytics and business intelligence service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Power BI can connect to multiple data sources, clean and transform data, create custom calculations, and visualize data through charts, graphs, and tables.Power BI can be accessed through a web browser, mobile device, or desktop application and integrated with other Microsoft tools like Excel and SharePoint12

Python is not correct, because Python is a general-purpose programming language that can be used for various applications, including data analysis and visualization. However, Python is not a dedicated business analytics tool, and it requires coding or programming skills to create reports and dashboards.

R is not correct, because R is a programming language and software environment for statistical computing and graphics. R can be used for data analysis and visualization, but it is not a specialized business analytics tool, and it requires coding or programming skills to create reports and dashboards.

SAS is not correct, because SAS is a software suite for advanced analytics, business intelligence, data management, and predictive analytics. SAS can provide interactive visualizations and business capabilities, but it does not have an interface that is simple enough for end users to create their own reports and dashboards. SAS also requires coding or programming skills to use its features.

An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.

To perform an INNER JOIN, we can use the following SQL query:

SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 9 rows of data, as shown below:

customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09

To perform a LEFT JOIN, we can use the following SQL query:

SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 15 rows of data, as shown below:

customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL

As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.

Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

Comprehensive and Detailed In-Depth Explanation:

Database views are virtual tables that provide a specific representation of data from one or more tables. They are powerful tools for simplifying complex queries and enhancing data security.

Option A:Views reduce the need for repetitive, complex data joins.

Rationale: Views can encapsulate complex JOIN operations and present the result as a single table. This abstraction allows users to retrieve the necessary data without repeatedly writing intricate SQL queries, promoting efficiency and reducing the potential for errors.

This is because a chi-squared test is a type of statistical method that tests the association or independence between two or more categorical variables, such as gender, race, or occupation. A chi-squared test can be used to compare the observed frequencies of the categories with the expected frequencies under the null hypothesis of no association or independence. For example, a chi-squared test can be used to determine if there is a relationship between smoking and lung cancer. The other statistical methods do not require two or more categorical variables. Here is why:

Simple linear regression is a type of statistical method that models the relationship between a continuous dependent variable and a continuous or categorical independent variable, such as height, weight, or education level. A simple linear regression can be used to estimate the slope and intercept of the best-fitting line that describes how the dependent variable changes with the independent variable. For example, a simple linear regression can be used to predict the weight of a person based on their height.

Z-test is a type of statistical method that tests the significance of the difference between a sample mean and a population mean, or between two sample means, when the population standard deviation or the sample sizes are large enough. A z-test can be used to compare the average scores of two groups of students on a standardized test.

Two-sample t-test is a type of statistical method that tests the significance of the difference between two sample means when the population standard deviation is unknown or the sample sizes are small. A two-sample t-test can be used to compare the average salaries of two groups of employees in different departments.

The best deliverable that would suit the site reliability team’s needs is A. A self-serve dashboard of website performance that updates in real time.

A self-serve dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance. A self-serve dashboard of website performance that updates in real time would allow the site reliability team to easily and quickly access the information they need about the stability of their website, such as uptime, response time, error rate, traffic volume, etc. A self-serve dashboard would also enable the team to proactively diagnose issues when they occur, by providing alerts, notifications, or drill-down options. A self-serve dashboard would also be more interactive and engaging than a report or an email.

A weekly log report of site visits and user actions would not be a good deliverable for the site reliability team’s needs, because it would not provide timely or relevant information about the stability of their website. A weekly log report would be too infrequent and delayed to monitor and diagnose issues when they occur. A weekly log report would also focus on the behavior and actions of the users, rather than the performance and functionality of the website.

A portal that is refreshed daily and reports errors classified by type would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or comprehensive information about the stability of their website. A portal that is refreshed daily would be too slow and outdated to monitor and diagnose issues when they occur. A portal that reports errors classified by type would be too narrow and limited to capture the full picture of the website performance.

A daily summary email indicating website outages for the previous day would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or actionable information about the stability of their website. A daily summary email would be too late and retrospective to monitor and diagnose issues when they occur. A daily summary email indicating website outages would also be too passive and generic to help the team resolve or prevent issues in the future.

star schema is a type of database schema that consists of one fact table that is composed of multiple dimensions. A fact table contains quantitative measures or facts that are related to a specific event or transaction. A dimension table contains descriptive attributes or dimensions that provide context for the facts. A star schema is called so because it resembles a star, with the fact table at the center and the dimension tables radiating from it. A star schema is a type of dimensional schema, which is designed for data warehousing and analytical purposes. Other types of dimensional schemas include snowflake schema and galaxy schema. A snowflake schema is similar to a star schema, except that some or all of the dimension tables are normalized into multiple tables. A galaxy schema consists of multiple fact tables that share some common dimension tables. A transactional schema is a type of database schema that is designed for operational purposes, such as recording day-to-day transactions and activities. A transactional schema is usually normalized to reduce data redundancy and improve data integrity. A non-relational schema is a type of database schema that does not follow the relational model, which organizes data into tables with rows and columns. A non-relational schema can store data in various formats, such as documents, graphs, key-value pairs, etc.

This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:

Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number.

Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list.

Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element. Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.

When consolidating data from different sources, it is crucial to standardize field names to ensure consistency across datasets. This process involves aligning the field names so that they are the same in both tables, which simplifies the merging of data and subsequent analysis. Standardizing field names helps in maintaining data integrity and avoids confusion that may arise from having different names for the same data point.

Recode the data values (B) would not be necessary unless the data values themselves are inconsistent or in different formats. Overwriting the field names in one of the tables © could lead to loss of information or confusion. Editing the field names in the data dictionary (D) is helpful, but it does not address the immediate need to harmonize the field names in the actual datasets.

References:

Best practices in data management.

Principles of data integration and consolidation.

This is because a scatter plot is a type of chart that shows the relationship between two variables for each observation or unit in a data set, such as study hours and exam scores for each student in this case. A scatter plot can be used to display and analyze the correlation, trend, or pattern among the variables, as well as identify any outliers or clusters in the data. For example, a scatter plot can show if there is a positive, negative, or no correlation between study hours and exam scores, as well as show if there are any students who have unusually high or low exam scores compared to their study hours. The other charts are not the best charts to represent the relationship between the variables. Here is why:

A histogram is a type of chart that shows the frequency or the count of values in a single variable for different intervals or bins, such as exam scores for different ranges in this case. A histogram can be used to display and analyze the distribution, shape, or spread of the variable, as well as identify any gaps, peaks, or skewness in the data. For example, a histogram can show if most students have high, low, or average exam scores, as well as show if there are any intervals that have no students at all.

A heat map is a type of chart that shows the intensity or the magnitude of values in two variables for different categories or groups, such as exam scores and study hours for different student names in this case. A heat map can be used to display and analyze the variation, contrast, or comparison among the categories or groups, as well as identify any hot spots, cold spots, or gradients in the data. For example, a heat map can show which students have higher or lower exam scores and study hours than others, as well as show if there is a color pattern that indicates a relationship between exam scores and study hours.

A bar chart is a type of chart that shows the value or the amount of a single variable for different categories or groups, such as exam scores for different student names in this case. A bar chart can be used to display and analyze the comparison, ranking, or proportion among the categories or groups, as well as identify any differences, similarities, or outliersin the data. For example, a bar chart can show which students have higher or lower exam scores than others, as well as show if there are any students who have exceptionally high or low exam scores.