Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Exam Practice Test

The appropriate action when Einstein Discovery surfaces an unexpected pattern is to determine if the pattern is a data issue or a new insight. This approach involves verifying the integrity and accuracy of the data, and understanding whether the pattern represents a meaningful insight into the business operations or an anomaly caused by data issues.

In Salesforce Tableau CRM, with data sync enabled, the maximum number of dataflow definitions an organization can have is 25. This limit is designed to balance the need for comprehensive data operations with system performance and manageability.

The status of a CSV upload in Salesforce Analytics can be viewed in the Data Manager. This tool provides a centralized interface to manage data-related tasks, including uploads, dataflow operations, and dataset management. By accessing the Data Manager, users can track the progress and status of their CSV uploads, ensuring they have control and visibility over data integration processes. Reference: Salesforce Documentation - Data Manager

The strength of the model depicted in the chart cannot be determined conclusively from the metrics shown (GINI coefficient of 0.535, maximum accuracy of 0.7597, and threshold of 0.5163). These metrics indicate aspects of the model's performance, but they do not provide a complete picture of its effectiveness in real-world scenarios. For instance, the GINI coefficient suggests some degree of discriminative power, yet it is not high enough alone to declare the model very effective. Similarly, the maximum accuracy shows potential, but without context regarding the complexity of the prediction task or comparisons to baseline models, a definitive conclusion about the model's strength cannot be drawn.

In Einstein Analytics (Tableau CRM), the security options include:

• Inherited Security: Utilizes the existing Salesforce security model to control access.
• Row-level Security with Security Predicates: Offers granular control by restricting data visibility at the row level based on specific criteria.
• App Level Security: Manages access at the application level within Einstein Analytics.

Field-level security is not directly configurable within Einstein Analytics; instead, it's managed through the broader Salesforce security settings, affecting data visibility before it's imported into Einstein Analytics datasets. Reference: &type=5

When planning to enable data sync (replication) in Einstein Analytics, two specific limits that should be considered are:

• C: Maximum number of objects that can be enabled for data sync (replication). This limit affects how many Salesforce objects can be set up for regular synchronization without creating new dataflows.
• D: Timeout for ELT (Extract, Load, Transform) jobs that are scheduled but not yet executed. This limit impacts the maximum allowable time that data sync jobs can remain in the queue before execution, which is critical for maintaining up-to-date data.

The best practice to ensure Einstein Analytics dashboards are easily used across both desktop and mobile devices is to create multiple layouts and ensure the layout selectors match the device. Additionally, resizing and hiding widgets as necessary helps ensure that the content is appropriately displayed and interacted with on the device screen size.

In Tableau CRM (formerly Einstein Analytics), you can restrict access to data at the row level using security predicates. Security predicates are filter expressions added to datasets that dynamically restrict data access based on user attributes or record specifics. This method allows administrators to enforce data security policies directly within the dataset, ensuring that users only see data relevant and permitted to them, without altering the underlying data structure like removing rows manually or adding flags. Reference: &type=5

Predicate is a name for a filter condition that defines row-level access to records in a dataset Example: "rowLevelSecurityFilter":"'AccountOwner' == \"$User.Name\""

In Salesforce, access to data can be configured at several levels, including Organization, Records, and Objects. However, Page Layouts is not a level at which access to data is configured. Page Layouts determine the arrangement of fields, related lists, and other elements on a record's page but do not control data access or security settings. Access controls are managed through settings at other specified levels such as profiles, roles, and sharing rules. Reference: Salesforce Help - Data Security

For a Tableau CRM dashboard that utilizes data sync for replication:

Two benefits of designing using the "Progressive Disclosure" principle are:

• A: Improved ease of use for end users. By only displaying essential information initially and allowing users to access more detailed data as needed, dashboards become less cluttered and more user-friendly.
• D: Better dashboard performance. This design principle can also lead to better performance as less data is processed and rendered initially, speeding up load times and responsiveness.

Insights categorized as 'Unrelated' in Einstein Discovery are significant because they highlight factors or variables that, while not directly related to the primary variables of interest, may still influence the outcome through broader or global effects. These insights help in understanding the comprehensive landscape of the data, acknowledging that some influencing factors might be external or less obvious yet impactful. This broader view aids in a more holistic analysis and prevents oversight of potential influences outside the immediate dataset. Reference: &type=5

Before users can access Analytics in Salesforce, they need two things: an Analytics permission set license (PSL) and a permission set containing at least one Analytics user permission. The PSL enables access to the Analytics functionality at a license level, while the permission set defines what specific actions the user is authorized to perform within Analytics.

Before releasing an updated dashboard to production, the best practice is to remove redundant steps to ensure dashboard performance. This optimization helps in minimizing the load time and resource consumption, particularly important after iterative modifications such as adding or removing charts.

Upon receiving a data alert from Einstein Discovery suggesting the presence of outliers in a dataset used to maximize daily sales quantity, appropriate actions include: B) Removing the outliers as suggested and then verifying if the removal has led to an improvement in model metrics and story insights. This ensures that the model's predictive accuracy is not adversely affected by extreme values. C) Engaging the client in a discussion about the occurrence of extreme values (both low and high) to determine if such cases are anomalies that should be excluded from analysis, ensuring that the model reflects typical sales scenarios.

The Event Monitoring Analytics app in Salesforce is specifically designed to analyze Event Log File data. This app provides insights into user behavior and system performance, helping organizations understand how their Salesforce environments are being used and how they can be optimized. The app includes prebuilt dashboards and metrics that focus on this specific type of data.

In Tableau CRM, a Compare Table allows you to segment data into different groupings for comparison. You can have up to four groupings in a Compare Table. This feature enables users to analyze and visualize data by comparing different segments side by side within the same table, facilitating a clearer understanding of how various groups perform against each other across multiple metrics. Reference: &type=5

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Within Salesforce, permissions for permission sets are specifically designed to grant access to various features and functionalities. "Access Sales Cloud Analytics Templates and Apps" is a valid permission which enables users to access predefined templates and apps that are specific to Sales Cloud within Tableau CRM. This permission is crucial for users who need to utilize advanced analytical capabilities provided by Sales Cloud analytics solutions, including the use of specialized dashboards and data models designed to enhance sales performance and insights. Reference: &type=5

In the configuration wizard of Tableau CRM apps, required fields or questions without default values are typically indicated by the system to guide the setup process. By proceeding with the configuration ("Looks good, next"), the wizard will highlight any mandatory questions that have not been answered, usually by marking them in red. This visual cue allows users to easily identify and address the required inputs that are missing defaults, ensuring that essential configurations are not overlooked during setup. Reference: &type=5

When developing a mobile layout for an Einstein Analytics dashboard, the consultant should consider:

• A: If no layouts are eligible for the mobile device, the first defined layout is used. This ensures that there is always a fallback layout if none of the other defined layouts meet the device properties.
• C: If more than one layout is eligible, the one with the most device properties set is used. If there is a tie, the most recently defined layout is used. This helps in choosing the most optimized layout for the device, enhancing user experience.
• D: A layout for mobile is eligible for use when the device meets all the device properties set in the Layout panel. This consideration is crucial for ensuring that the layout displayed is suitable for the device’s capabilities and screen size.

To add text, numerical values via a case statement, and dates via SAQL in an existing dataflow, the correct compute option is "computeExpression." This function allows for the creation and manipulation of new fields based on expressions applied to existing data in the dataflow, suitable for the required transformations.

In a scenario where Recipe3 depends on Dataset1 created by Recipe1, while Recipe1 and Recipe2 have no dependencies, the optimal scheduling strategy is to make Recipe1 and Recipe2 time-based and Recipe3 event-based, triggered by the successful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

To display boolean fields in a dataset as "Yes" in green for true and "No" in red for false across all dashboards:

• A: Download the XMD for the dataset and change the values and color for the fields and then upload to the dataset. Modifying the Extended Metadata (XMD) allows for comprehensive customization of how data values are displayed across all views that utilize the dataset.
• D: In the explorer, select the boolean fields, then create a derived dimension that references the boolean fields, and then modify the values and colors of the derived dimension. This approach allows for custom display settings to be applied directly within the explorer tool, tailoring the presentation of boolean values on a per-dashboard basis.

Salesforce Tableau CRM (formerly Einstein Analytics) provides specific prebuilt permission sets that allow various levels of access and capabilities within the platform. The two prebuilt permission sets that are relevant here are:

• Einstein Analytics Platform Admin: This permission set grants administrative privileges within Einstein Analytics, allowing users to manage users, permissions, and overall platform settings.
• Einstein Analytics Platform User: This permission set enables users to access and use Einstein Analytics for data exploration and dashboard creation, but without the administrative privileges.

These permission sets are designed to simplify user management by providing role-based access controls that can be easily assigned to different users depending on their needs within the platform. Reference: Salesforce Documentation - Einstein Analytics Permission Sets

If an administrator is unable to upload a custom geoJSON file via the user interface, the recommended approach is:

• C: Upload the geoJSON via the API because it is not a function in the UI. This method allows for the integration of custom geoJSON maps into Salesforce dashboards, even when UI support is not available.

In Tableau CRM, the default number of rows shown in a values table is 50. This default setting helps manage display and performance efficiently while providing an adequate snapshot of data for initial analysis.

The correct operation to use with the Analytics External Data API to upload monthly target data, which includes replacing existing entries with new data each month, is:

• C: Overwrite. This operation ensures that each upload replaces the existing dataset entirely, which is necessary for scenarios where the target data changes every month, and historical precision needs to be maintained.

The configuration wizard in Salesforce Tableau CRM Einstein Discovery asks questions about various measurements that are critical for understanding and analyzing customer interactions and service effectiveness, which include:

• A: Case duration
• B: Customer geography
• C: Customer satisfaction
• D: Case resolution
• E: All of the above

To load data from an external, on-premise Oracle database into Einstein Analytics, the recommended options are:

• Use the Analytics External Data API: This API facilitates the integration of external data sources with Einstein Analytics, allowing for automated data uploads.
• Load the data as a CSV file: This method involves extracting data from the Oracle database into a CSV format and then uploading it to Einstein Analytics.

For Einstein Discovery in Salesforce Tableau CRM, the maximum number of new stories that can be created per organization per day is capped at 20. This limit helps manage the load on the system, ensuring stability and performance across the platform.

A Metric Radar chart is most suitable for rendering five measures in a lens visualization within Salesforce Tableau CRM. This chart type can effectively display multiple metrics at once, providing a comprehensive overview where each measure is represented as a point along a spoke, which is ideal for comparing multiple measures in a single visualization.

The "Progressive Disclosure" design principle in dashboard design means: A. Only provide the user with the level of detail they need to see, with the option to drill down deeper into more details. This approach helps to keep the user interface simple and clean, avoiding information overload while still offering more detailed data as needed. This principle ensures that dashboards are user-friendly and efficient, focusing on user engagement and information relevance. Reference: Salesforce Documentation on Progressive Disclosure

In the context of Einstein Analytics, when creating a template app from an existing app, the JSON file that manages the template metadata and all other template elements is: D. template-info.json. This file contains essential metadata about the template, including definitions of the template's properties and behaviors, facilitating the reuse and customization of the app as a template. This file is crucial for managing and distributing template apps within Einstein Analytics. Reference: Salesforce Documentation on Analytics Templates

To meet the security requirements of allowing only a subset of users to access the dataset and ensuring they only see data relevant to their region, the consultant can:

• Use a security predicate in the dataset so users can see only rows belonging to their respective regions (B): This involves configuring row-level security using security predicates based on the UserRegion field, which filters data dynamically based on the user's region.
• Save the dataset under a new application and give "viewer" access to a public group containing the list of users allowed to see the dataset (D): This method segregates access at the application level, allowing only designated users to view the dataset within that application.

Two advantages of using layout templates for dashboard development in Salesforce Tableau CRM are:

• A: Layout templates are equipped with design best practices, such as optimally positioned KPIs and filters. This feature saves time and ensures that dashboards adhere to visual and functional standards that enhance user experience and data readability.
• C: Layout templates offer a consistent end-user experience. Consistency across dashboards improves user adoption and reduces learning curves as users navigate various analytics tools within the organization.

The Einstein Analytics Dashboard Inspector is a tool used to analyze and improve dashboard performance. It allows users to: B. Get a list of recommendations on how to improve the performance of the dashboard. This includes suggestions on query optimizations and step adjustments. C. View all queries and the time it took to run each one. This feature helps in identifying slow-running queries that may need optimization. D. View the total time required to run all queries. This gives an overall view of the dashboard's performance, which is crucial for troubleshooting and performance enhancement. These tools help in maintaining optimal dashboard performance and user experience. Reference: Salesforce Documentation on Dashboard Inspector

To convert the "CloseDate_Month" numerical field into a month name in the dataset, the consultant should use:

• C: computeExpression. This Dataflow node allows the transformation of existing data fields using expressions, such as converting a month number into a month name. By applying a suitable expression, the 'CloseDate_Month' can be transformed into a string representing the month’s name, enhancing readability and usability in filters.

When migrating an Einstein Analytics dashboard from a sandbox to production, several options are available:

• Ant Migration Tool (A): This tool can be used for deploying metadata (including dashboards) between Salesforce environments.
• Analytics REST API (C): This API can programmatically handle the deployment of dashboards from one environment to another.
• Change sets (D): Change sets can include Einstein Analytics assets such as dashboards for migration between connected Salesforce environments.

In Salesforce Tableau CRM (Einstein Discovery), one of the straightforward ways to get data into Einstein Discovery for analysis is by using an Einstein Analytics dataset. These datasets can be directly used to create stories in Einstein Discovery, allowing for deep analysis and predictive modeling based on the data already collected and structured within the Salesforce environment. This method leverages the integrated nature of Salesforce products to provide a cohesive and efficient analytics experience. Reference: Salesforce Help - Data Preparation for Einstein Discovery

Creating a regression timeseries in Salesforce Tableau CRM involves the use of the SAQL (Salesforce Analytics Query Language). Specifically, the timeseries function is used within SAQL to model and forecast data points across time. Here are the required steps:

• Partition: This argument in the timeseries function specifies how the data should be grouped. In most cases, this will be based on a time dimension such as date or month.
• Seasonality: Setting this parameter to 12 is typical for capturing yearly patterns and trends in the data.

In designing a dashboard for a Senior Sales Business Analyst that involves fiscal year product opportunities: D. Use charts to help users ask questions, not illustrate a conclusion. This approach encourages interactive exploration and deeper analysis of the data, enabling users to discover insights rather than just viewing predetermined outcomes. It supports a more analytical approach to data interpretation, which is essential in complex scenarios like fiscal year analysis. Reference: Salesforce Documentation on Effective Dashboard Design

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In Salesforce Tableau CRM, the number of concurrent queries that a user can execute is capped at 20. This limit is designed to manage the load on the analytics platform effectively, ensuring stable and responsive performance across the user base.

Setting security predicates for a dataset in Salesforce can be done in several ways, but not directly on each row before uploading. Security predicates are typically set in the metadata file associated with the external data file or in the transformation node of a dataflow. This is essential for managing data visibility at a granular level and ensuring compliance with organizational security policies.

When you select one variable in an Einstein Discovery story and examine its insights, you are comparing the impact of that selected variable with all other data in the story. This involves analyzing how much the selected variable influences the outcome variable relative to other variables in the dataset. The process evaluates the contribution of the selected variable to the overall variance in the outcome, offering insights into its significance and relationship within the broader data context. Reference: Salesforce Einstein Discovery Story Insights

To enhance the performance of data synchronization in Tableau CRM, after removing unused objects and fields, the next logical step is to evaluate the connection mode for each connected object. This involves analyzing whether the current connection settings (e.g., full or incremental sync) are optimized based on the data volume and update frequency. Adjusting these settings can significantly improve sync performance by reducing unnecessary data transfer and processing.

In the context of Tableau CRM (formerly Einstein Analytics), a lens/exploration is composed of two main parts: Visualization and Grouping. Visualization refers to how the data is presented visually, such as charts or tables. Grouping involves how data fields are categorized or segmented for analysis, allowing users to drill down into specifics or see summaries based on certain criteria.

The Tableau CRM Dashboard Inspector tool is designed to help optimize and troubleshoot dashboard performance by providing:

• A. Recommendations on how to improve the performance: It offers actionable suggestions to enhance dashboard efficiency.
• B. The total time required to run all queries: It aggregates the execution times of all queries involved in the dashboard, giving an overview of the dashboard’s performance.
• D. View all queries and the time it took to run each one: It allows detailed inspection of each query’s execution time, helping identify potential bottlenecks.