Qlik QSDA2024 Qlik Sense Data Architect Certification Exam – 2024 Exam Practice Test

In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABC\PPP, has a specific entry in the security table that determines their access rights to the LEVEL field.

Understanding Section Access:

Section Accessis used to enforce security by restricting access to certain data based on the user's credentials.

In the security table provided, the USER role for ABC\PPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.

Outcome:

Since the OMIT field for user ABC\PPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.

Option D: The table is displayed without the LEVEL columnis the correct outcome.

Explanation: When user ABC\PPP opens the app, the LEVEL field is omitted from their view. Any table or visualization that uses the LEVEL field will have that field excluded from display. The rest of the data and columns in the table will be visible, but the LEVEL column will not be shown.

References:

Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.

When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to-date.

Explanation of Steps:

Load the existing data from the QVD:

This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.

Load new and updated data from the database. Concatenate with the table loaded from the QVD:

The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.

The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.

Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:

A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.

In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model—i.e., the last successfully loaded data model—remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.

When the script encounters an error:

The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.

The existing data model before the script was executed remains unchanged and is maintained.

No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.

Qlik Sense Data Architect ReferencesThis behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:

Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.

Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction.

In this scenario, the company generates 1 GB of ticketing data daily, accumulating up to 720 GB over two years. Business users mainly require trend analysis for the past two years and rarely need to access the transaction-level data. The objective is to load only the necessary data while ensuring the system remains performant.

Option Cis the optimal choice for the following reasons:

Efficiency in Data Handling:

By loading only aggregated data for the two years, the app remains lean, ensuring faster load times and better performance when users interact with the dashboard. Aggregated data is sufficient for analyzing trends, which is the primary use case mentioned.

On-Demand App Generation (ODAG):

ODAG is a feature in Qlik Sense designed for scenarios like this one. It allows users to generate a smaller, transaction-level dataset on demand. Since users rarely need to drill down into transaction-level data, ODAG is a perfect fit. It lets users load detailed data for specific dates only when needed, thus saving resources and keeping the main application lightweight.

Performance Optimization:

Loading only aggregated data ensures that the application is optimized for performance. Users can analyze trends without the overhead of transaction-level details, and when they need more detailed data, ODAG allows for targeted loading of that data.

References:

Qlik Sense Best Practices: Using ODAG is recommended when dealing with large datasets where full transaction data isn't frequently needed but should still be accessible.

Qlik Documentation on ODAG: ODAG helps in maintaining a balance between performance and data availability by providing a method to load only the necessary details on demand.

During the transition from QlikView to Qlik Sense, the analytics team has the opportunity to improve the overall reporting. To ensure a smooth migration while optimizing the new environment, the data architect needs to consider several key factors.

Option Cis the best choice because it encompasses the essential aspects of a migration project:

QlikView Archival:

Archiving QlikView applications is crucial to ensure that historical data and applications are preserved and can be referenced if needed in the future. This step is important to maintain continuity and provide a fallback option if required during the transition.

Source Data Architecture:

Understanding the existing source data architecture is critical to ensure that the new Qlik Sense applications can seamlessly connect to the data sources. This also helps in identifying opportunities to optimize or re-architect the data pipelines for better performance in Qlik Sense.

Load Script:

The load script from QlikView might need to be revised or optimized for Qlik Sense. It's important to ensure that the script is compatible and takes advantage of Qlik Sense's capabilities, such as improved data handling, better inline transformations, and enhanced scripting functions.

Data Model:

Reviewing and possibly redesigning the data model is essential during the migration. Qlik Sense's associative engine allows for more flexibility, and this is an opportunity to improve the data model for better performance, scalability, and user experience.

Business Use Case:

Understanding the business use case is vital to ensure that the new Qlik Sense applications meet the business requirements effectively. This includes making sure that the new reports and dashboards are aligned with the business goals and provide the necessary insights.

References:

Qlik Migration Guide: When migrating from QlikView to Qlik Sense, it's important to consider not just the technical aspects but also the business implications and opportunities for improvement.

Qlik Documentation on Data Modeling and Load Script Optimization: These resources provide best practices on how to optimize load scripts and data models during migration to ensure smooth operation and better performance in Qlik Sense.

In the provided scenario, the sales territories were realigned during the year, and it is necessary to track performance based on the date of the sale and the salesperson’s assignment during that period. The IntervalMatch function is the best approach to create a time-based relationship between the sales transactions and the sales territory assignments.

IntervalMatch: This function is used to match discrete values (e.g., transaction dates) with intervals (e.g., start and end dates for sales territory assignments). By matching the transaction dates with the intervals in the HR table, you can accurately determine which territory and manager were in effect at the time of each sale.

Using IntervalMatch, you can generate point-in-time data that accurately reflects the dynamic nature of sales territory assignments, allowing both sales reps and regional managers to track performance over time.

In Qlik Sense, the TRACE statement is used to print custom messages to the script execution log. To output the value of a variable, particularly one that is dynamically assigned, the correct syntax must be used to ensure that the variable's value is evaluated and displayed correctly.

The variable vMaxDate is defined with the LET statement, which means it is evaluated immediately, and its value is stored.

When using the TRACE statement, to output the value of vMaxDate, you need to ensure the variable's value is expanded before being printed. This is done using the $() expansion syntax.

The correct syntax is TRACE #### $(vMaxDate) ####; which evaluates the variable vMaxDate and inserts its value into the log output.

Key Qlik Sense Data Architect References:

Variable Expansion:In Qlik Sense scripting, $(variable_name) is used to expand and insert the value of the variable into expressions or statements. This is crucial when you want to output or use the value stored in a variable.

TRACE Statement:The TRACE command is used to write messages to the script log. It is commonly used for debugging purposes to track the flow of script execution or to verify the values of variables during script execution.

In the script provided, the alt() function is used to handle various date formats. The alt() function in Qlik Sense evaluates a list of expressions and returns the first valid expression. If none of the expressions are valid, it returns the last argument provided (in this case, '31/12/2022').

Step-by-step breakdown:

The alt() function checks the Date field for three different formats:

YYYYMMDD

YYYY/MM/DD

DD/MM/YYYY

If none of these formats match the value in the Date field, the default date '31/12/2022' is assigned.

Values in the Date field:

20210131: Matches the first format YYYYMMDD.

2020/01/31: Matches the second format YYYY/MM/DD.

31/01/2019: Matches the third format DD/MM/YYYY.

9999: Does not match any of the formats, so the alt() function returns the default value '31/12/2022'.

In the scenario, two tables (OrderDetails and Customers) are being loaded into the Qlik Sense data model, and these tables are related via the fields CustomerID and CustomerKey. The goal is to ensure that the relationship between these two tables is correctly established in Qlik Sense without creating synthetic keys or data inconsistencies.

Option A:Renaming CustomerKey to CustomerID in the OrderDetails table ensures that the fields will have the same name across both tables, which is necessary to create the relationship. However, renaming is done using AS, which might create an issue if the fields in the original data source have a different meaning.

Option B and C:These options use AUTONUMBER to convert the CustomerKey and CustomerID to unique numeric values. However, using AUTONUMBER for both fields without ensuring they are aligned correctly might lead to incorrect associations since AUTONUMBER generates unique values based on the order of data loading, and these might not match across tables.

Option D:This approach loads the tables with their original field names and then uses the RENAME FIELD statement to align the field names (CustomerKey to CustomerID). This ensures that the key fields are correctly aligned across both tables, maintaining their relationship without introducing synthetic keys or mismatches.

The scenario describes an app that is experiencing long load times due to the need to pull all records, both new and historical, from three large databases. This situation puts a strain on both the Qlik environment and the source databases. Given that all data is required for analysis, a full reload each time can be inefficient and resource-intensive.

Implementingincremental loadis a widely recommended approach in such cases. Incremental loading allows you to load only new or changed data since the last reload, rather than reloading all the data every time. This significantly reduces the time and resources required for reloading, as only a subset of the data needs to be processed during each reload. QVD (QlikView Data) files are typically used to store the historical data, while only the new or updated records are fetched from the source databases.

This approach would help:

Reduce the load on the source databases.

Shorten the data reload window.

Maintain historical data efficiently while ensuring that all new data is captured.

In the given scenario, where the data architect is provided with five tables, the goal is to design the most optimal data model for use in Qlik Sense. The key considerations here are to ensure a proper star schema, minimize redundancy, and ensure clear and efficient relationships among the tables.

Option Dis the most optimal model for the following reasons:

Star Schema Design:

In Option D, the Fact_Gross_Sales table is clearly defined as the central fact table, while the other tables (Dim_SalesOrg, Dim_Item, Dim_Region, Dim_Customer) serve as dimension tables. This layout adheres to the star schema model, which is generally recommended in Qlik Sense for performance and simplicity.

Minimization of Redundancies:

In this model, each dimension table is only connected directly to the fact table, and there are no unnecessary joins between dimension tables. This minimizes the chances of redundant data and ensures that each dimension is only represented once, linked through a unique key to the fact table.

Clear and Efficient Relationships:

Option D ensures that there is no ambiguity in the relationships between tables. Each key field (like Customer ID, SalesID, RegionID, ItemID) is clearly linked between the dimension and fact tables, making it easy for Qlik Sense to optimize queries and for users to perform accurate aggregations and analysis.

Hierarchical Relationships and Data Integrity:

This model effectively represents the hierarchical relationships inherent in the data. For example, each customer belongs to a region, each salesperson is associated with a sales organization, and each sales transaction involves an item. By structuring the data in this way, Option D maintains the integrity of these relationships.

Flexibility for Analysis:

The model allows users to group and filter data efficiently by different attributes (such as salesperson, region, customer, and item). Because the dimensions are not interlinked directly with each other but only through the fact table, this setup allows for more flexibility in creating visualizations and filtering data in Qlik Sense.

References:

Qlik Sense Best Practices: Adhering to star schema designs in Qlik Sense helps in simplifying the data model, which is crucial for performance optimization and ease of use.

Data Modeling Guidelines: The star schema is recommended over snowflake schema for its simplicity and performance benefits in Qlik Sense, particularly in scenarios where clear relationships are essential for the integrity and accuracy of the analysis.

When retrieving data from a REST API, particularly when the dataset is large or the data is segmented across multiple pages (which is common in REST APIs), the REST Connector in Qlik Sense needs to be configured to handle pagination.

Pagination is the process of dividing the data retrieved from the API into pages that can be loaded sequentially or as required. Qlik Sense's REST Connector supports pagination by allowing the dataarchitect to set parameters that will sequentially retrieve each page of data, ensuring that the complete dataset is retrieved.

Key Steps:

REST Connector Setup: Configure the REST connector in Qlik Sense and specify the necessary API endpoint.

Pagination Mechanism: Use the built-in pagination mechanism to define how the connector should retrieve the subsequent pages (e.g., by using query parameters like page or offset).

In the scenario, the data architect needs to load data from two different databases, and additional data is located in a folder containing QVDs, text files, and Excel files.

Minimum Number of Data Connections Required:

Database Connections:

Each database requires a separate data connection. Therefore, two data connections are needed for the two databases.

Folder Connection:

A single folder data connection can be used to access all the QVDs, text files, and Excel files in the specified folder. Qlik Sense allows you to create a folder connection that can access multiple file types within that folder.

Total Connections:

Two Database Connections: One for each database.

One Folder Connection: To access the QVDs, text files, and Excel files.

Therefore, the minimum number of data connections required istwo.

In this scenario, the data architect needs to conform the revenue data from Company B to match the data standard of Company A, where $0 is explicitly used to represent journeys without revenue.

Explanation of the Correct Script:

Option A:money(replace(Revenue, chr(32), 0)) AS [Revenue Conformed]

replace(Revenue, chr(32), 0):This part of the expression replaces any spaces (ASCII character code 32) in the Revenue field with 0.

money(...):This function formats the resulting value as currency. Since Company B may have either null values or spaces where 0 should be, this script ensures that any blanks are replaced with 0 and then formatted as currency.

Why Option A is Correct:

Handling Spaces:The replace() function is effective in replacing spaces with 0, conforming to Company A's standard of using $0 for non-revenue journeys.

Handling NULL Values:The money() function is used to ensure the final output is formatted as currency. However, it's important to note that NULL values are not directly handled by the replace() function, which is why it is applied before money() to deal with spaces.

In the provided scenario, the data architect needs to create a data model that supports various analyses, including total sales for each customer, total sales by state, identifying customers with no sales, and displaying the names of salespersons and regional account managers.

Here’s whyOption Cis the correct choice:

Loading the Sales Table:The Sales table contains key information related to sales transactions, including SaleID, CustomerID, Amount, SaleDate, SalesPersonID, and RegionalAcctMgrID. This table must be loaded first as it will be central to the analysis.

Loading the Customers Table:The Customers table includes customer details such as CustID, CustName, Address, City, State, and Zip. Loading this table and linking it to the Sales table via the CustomerID field allows you to perform analyses such as total sales per customer and total sales by state. Importantly, loading the customers separately will also allow the identification of customers without any sales.

Loading the Employees Table Twice:The Employees table must be loaded twice because it is used to look up two different roles in the sales process: the SalesPersonID and the RegionalAcctMgrID. When loading the table twice:

The first instance of the Employees table will be used to map the SalesPersonID to EmployeeName.

The second instance will be used to map the RegionalAcctMgrID to EmployeeName.

Aliasing the EmployeeID field appropriately in each instance is crucial to prevent creating synthetic keys and to ensure the correct association with the roles in the sales process.

This approach ensures that the data model will correctly support all the required analyses, including identifying customers without sales, which is crucial for meeting the business requirements.

Option AandOption Bpropose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.

Option Dinvolves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.

Thus,Option Cis the correct answer as it best meets the requirements while maintaining a clear and functional data model.