Salesforce Certified Tableau CRM and Einstein Discovery Exam Dumps & Practice Test Questions

Question 1:

A Tableau CRM consultant is tasked with optimizing a dashboard’s loading speed. After investigation, most dashboard queries complete in under five seconds, except for the Opportunities Table, which takes 30 seconds. 

What is the best way to enhance the dashboard’s performance?

A Build a Visualforce page to display the Opportunities list view.
B Provide a link to download the Opportunities data as an Excel file.
C Add a new page to the dashboard and move the Opportunities Table there.
D Create a separate dashboard and place the Opportunities Table on it.

Correct answer: C

Explanation:

When optimizing a Tableau CRM (formerly Einstein Analytics) dashboard that suffers from slow load times caused by a particular widget—here, the Opportunities Table—the key goal is to improve user experience by reducing the initial page load duration while preserving access to all necessary data.

The best strategy is to separate the heavy-loading component onto its own dashboard page, which corresponds to Option C. By moving the Opportunities Table to a new page within the same dashboard, the main dashboard loads faster because the other elements render without waiting for the slow table to complete. Users can still view the table by navigating to the second page, maintaining full functionality with better performance.

Let’s consider the other options:

• Option A suggests using a Visualforce page for the table. While Visualforce allows for custom UI development, it introduces unnecessary complexity and additional maintenance overhead. It doesn’t improve dashboard performance directly and requires development outside of Tableau CRM’s native environment.

• Option B involves adding a download link for the Opportunities data as an Excel file. Although this allows offline access to the data, it does not resolve the dashboard’s loading issue and is more of a workaround than a performance optimization.

• Option D proposes creating a separate dashboard just for the Opportunities Table. While this also isolates the slow component, it fragments the user experience, forcing users to switch dashboards, which can be confusing and inefficient.

In summary, moving the slow component to a separate page within the same dashboard (Option C) provides a balanced solution that improves performance without complicating navigation or development. It optimizes loading time, enhances usability, and retains all dashboard functionality, making it the best choice.

Question 2:

A company has two datasets in Tableau CRM: "OpportunityWithAccount," containing Opportunity data enriched with Account info, and "CaseWithAccount," containing Case data enriched with Account info. They want to create a lens that displays the total number of Opportunities and Cases per Account.

How should the consultant approach this?

A Use the "Compare Table" widget to join and aggregate the datasets by AccountId.
B Write a SAQL query to join and group the datasets by AccountId, then aggregate the results.
C Use a Salesforce Direct query to join the two datasets.
D Link the datasets using Connect Data Sources in the Tableau CRM UI.

Correct answer: B

Explanation:

To build a lens that displays the total Opportunities and Cases per Account from two separate datasets, the most effective approach is to use SAQL (Salesforce Analytics Query Language). SAQL provides granular control over how datasets are joined, grouped, and aggregated, which is essential for combining data from multiple sources and producing a meaningful consolidated view.

Option B is correct because a SAQL query allows the consultant to explicitly join the "OpportunityWithAccount" and "CaseWithAccount" datasets on the "AccountId" field, group by this key, and then compute the aggregated counts of both Opportunities and Cases per Account. This method offers the flexibility needed to handle complex joins and aggregations within Tableau CRM and supports further customization if additional filtering or transformations are required.

Let’s analyze why the other options are less suitable:

• Option A, using the "Compare Table," is designed for visual side-by-side comparisons rather than joining and aggregating datasets. It cannot perform the grouping and aggregation necessary to combine Opportunity and Case counts effectively.

• Option C involves Salesforce Direct queries, which connect directly to Salesforce objects but are not meant for joining datasets already imported into Tableau CRM. This approach would be inefficient and technically inappropriate here.

• Option D, setting up Connect Data Sources, allows datasets to be linked for ease of access within Tableau CRM but does not enable the complex joining and aggregation required for this analysis. It’s more about data availability than data manipulation.

Therefore, leveraging SAQL for precise control over dataset joining and aggregation makes Option B the best choice for building a lens that accurately shows the total number of Opportunities and Cases per Account.

Question 3:

You have a dataset containing several boolean fields. On dashboards, each boolean field must display "Yes" in green when true and "No" in red when false. Which two methods will consistently apply this formatting?

A Add an XMD node in the dataflow to customize the labels and colors for these fields
B Use the "edit values" option in the explorer on the boolean fields to alter the values and colors
C Export the dataset’s XMD, edit the values and colors directly, then re-upload the file
D In the explorer, create a derived dimension referencing the boolean fields, and adjust its values and colors

Correct Answers: A, D

Explanation:

Ensuring uniform display of boolean fields as "Yes" in green for true and "No" in red for false across dashboards requires two effective techniques. The best approaches are adding an XMD node in the dataflow and creating a derived dimension in the explorer, corresponding to options A and D.

Option A involves using an XMD (Extended Metadata Definition) node within the dataflow pipeline. This advanced method modifies metadata and display characteristics at the data processing stage. It allows you to redefine how boolean values appear—mapping true to "Yes" with green coloring, and false to "No" in red—ensuring that this formatting applies consistently across all downstream visualizations. Since it operates directly in the dataflow, the transformation is applied before visualization, providing a scalable and centralized solution.

Option D suggests creating a derived dimension inside the explorer interface. By building a new dimension that references the original boolean fields, you can replace raw true/false values with custom labels and colors. This is a flexible and user-friendly approach, especially for users who prefer modifying metadata within the visualization tool itself rather than the data pipeline.

Options B and C are less optimal. The "edit values" feature in option B often lacks the scope and control to apply consistent formatting across multiple fields or dashboards and can become tedious when managing many fields. Option C, manually exporting and editing the XMD file, is prone to errors, requires advanced knowledge, and is less maintainable compared to using an XMD node within the dataflow.

In summary, options A and D offer robust, maintainable methods to achieve consistent display of boolean values on dashboards, with A handling it at the dataflow level and D handling it within the visualization layer.

Question 4:

A consultant has created a useful Tableau CRM app for Sales Operations and wants to share it with the entire Global Sales team without adding every member individually. They suggest extending the Sales Operations app and distributing it as a Tableau CRM template app. 

To do this, which API endpoint should they use to post the extension request?

A /services/data/v##.#/wave/templates
B /services/data/v##.#/analytics/projects
C /services/data/v##.#/analytics/wizard
D /services/data/v##.#/wave/apps

Correct Answer: A

Explanation:

This question revolves around the correct API endpoint to use when extending and distributing a Tableau CRM app as a template app. Template apps in Tableau CRM allow organizations to package reusable app configurations and share them widely without manually adding each user.

The correct endpoint is A: /services/data/v##.#/wave/templates. This endpoint is specifically designed for managing Tableau CRM template apps. Posting to this API creates or modifies templates, enabling the consultant to extend the Sales Operations app and distribute it across the Global Sales team efficiently. The mention of parameters like "folderSource" and specific Sales Operations IDs implies that the app is being repurposed or extended, making the templates endpoint the logical choice.

Option B, /services/data/v##.#/analytics/projects, manages analytics projects but isn’t meant for creating or distributing template apps. Projects organize datasets and metadata rather than apps or templates.

Option C, /services/data/v##.#/analytics/wizard, relates to tools for creating visualizations or dashboards but does not support app distribution or template management.

Option D, /services/data/v##.#/wave/apps, manages existing Tableau CRM apps but is focused on the apps themselves, not on creating reusable templates designed for broad distribution.

Therefore, option A is the correct and precise endpoint for creating and sharing template apps in Tableau CRM, facilitating wide distribution without the need to add individual users.

Question 5

Which three of the following statements correctly describe the behavior and requirements of sharing inheritance? (Select three.)

A Any dataset that uses sharing must also have an associated security predicate.
B If a user can access over 3,000 records on an object but lacks the "View All Data" permission, sharing inheritance does not apply; instead, the backup security predicate is enforced. This exception does not apply to the Opportunity object.
C When sharing inheritance is enabled, security predicates are no longer applied.
D Sharing inheritance relies on foreign keys to enforce security controls.
E Sharing inheritance is supported for standard objects like Account, Campaign, Case, Contact, Opportunity, Lead, Order, and User, but for custom objects, security predicates must be used.

Correct answers: A, B, E

Explanation:

Sharing inheritance and security predicates are foundational to Salesforce’s approach to data access and security, determining who can view or edit records. Understanding how these mechanisms operate is critical for setting up secure data models.

Option A is correct because when a dataset employs sharing inheritance, it must define a security predicate. A security predicate is a rule or filter that enforces access control on the dataset, ensuring that sharing settings effectively restrict data visibility. Without it, sharing inheritance alone cannot manage security properly.

Option B is also correct. Salesforce limits the use of sharing inheritance when a user can access more than 3,000 records on an object, but does not have the broad "View All Data" permission. In these cases, the system falls back to a backup security predicate to maintain performance and security. However, the Opportunity object is exempt from this limit, as it follows a different security model that allows sharing inheritance regardless of record count.

Option C is false. Security predicates are integral even when sharing inheritance is enabled; they are not ignored but work alongside sharing inheritance to define data access.

Option D is incorrect because sharing inheritance does not rely on foreign keys for security enforcement. Instead, it leverages sharing rules and security predicates.

Option E is true. Sharing inheritance works with specific standard objects but does not apply to custom objects. For custom objects, security predicates must be explicitly created to control data access.

In conclusion, A, B, and E accurately represent how sharing inheritance operates, the limitations around record counts, and the distinction between standard and custom objects in Salesforce security.

Question 6:

Which widget property enables a consultant to limit the viewable data to a domain defined by specific entered values?

A Add Filter
B Add Custom Range
C Add Custom Domain
D Filter by Range

Correct answer: C

Explanation:

In data visualization platforms and dashboard tools, widgets often come with properties or settings that control which data is displayed. Consultants frequently need to restrict the data view to focus analysis or reporting on a specific subset of values, and the correct widget property is essential to achieve this.

Option A, Add Filter, typically applies broad filters to reduce data by categories or conditions. While filters help narrow down datasets, they don’t always allow precise bounding of data within a specific domain or range.

Option B, Add Custom Range, generally allows users to define a range within a certain attribute, such as numeric or date values. Although it limits data somewhat, it may not define a full domain bounded explicitly by both minimum and maximum values or customized boundaries.

Option C, Add Custom Domain, is the correct choice because it lets the user explicitly define the exact boundaries of a domain. This means the consultant can specify both ends of a range or domain, thereby restricting the data view strictly to the values within those bounds. This property is particularly useful when a precise scope is needed for analysis or visualization.

Option D, Filter by Range, is similar to a custom range filter but typically less flexible. It usually focuses on filtering data within a numerical or temporal range without the ability to customize domain boundaries as comprehensively as a Custom Domain property.

In summary, the Add Custom Domain property provides the greatest control by bounding the dataset view to the specific values entered, making Option C the most suitable property for consultants who want to restrict data visibility to a defined domain.

Question 7:

Which type of node should a consultant select within a Tableau CRM Dataflow to compute and add a SalesTax field to the dataset?

A. computeExpression
B. augment
C. computeRelative
D. append

Correct Answer: A

Explanation:

When working with Tableau CRM (formerly Einstein Analytics) Dataflows, selecting the appropriate node for dataset transformations is crucial to achieving the desired results efficiently. In this scenario, the consultant needs to calculate the SalesTax for each record and add it as a new field in the dataset. Let’s analyze the options carefully:

• The computeExpression node (A) is specifically designed for calculating new fields based on expressions applied to existing data. For example, calculating SalesTax might involve multiplying a SubTotal field by a CountyTax rate. This node lets you define such formulas and seamlessly inserts the calculated results as a new field in your dataset. Because it focuses on field-level calculations, this node is the best fit for this task.

• The augment node (B) is used to enrich a dataset by joining it with another dataset, effectively adding additional rows or columns from an external source. This node is useful when merging or augmenting datasets but does not perform calculations on existing data fields within a single dataset. Therefore, it doesn’t suit the requirement to calculate SalesTax based on internal fields.

• The computeRelative node (C) handles calculations relative to other rows, such as rankings, running totals, or groupings. While powerful for comparative calculations, it’s not intended for straightforward arithmetic operations like multiplying two fields to create a new calculated field.

• The append node (D) combines records from two datasets by appending rows from one dataset to another. This operation is about merging datasets vertically and does not involve computation or creating new calculated fields.

In conclusion, the computeExpression node is the ideal choice for this scenario as it directly supports creating calculated fields within a dataset by applying expressions to existing data, precisely what’s needed for calculating and inserting SalesTax.

Question 8:

Universal Containers notices that selections made in a List widget do not affect the Pie chart in their Tableau CRM dashboard. Both widgets use different datasets as shown in the accompanying graphic. 

What two actions should a Tableau CRM Consultant take to resolve this issue? (Choose two.)

A. Apply selection binding in the filters section of the List widget’s query ("Type_1")
B. Apply selection binding in the filters section of the Pie chart’s query ("Step_pie_2")
C. Use "Connect Data Sources" to create a connection between the two widgets
D. Use "Connect Data Sources" to create a connection between the two datasets

Correct Answers: B, D

Explanation:

In Tableau CRM dashboards, widget interaction—such as filtering a Pie chart based on a selection in a List widget—requires proper linkage between the data driving these visual components. When the widgets rely on different datasets, this interaction does not happen automatically. The consultant must configure the dashboard to enable communication between these datasets.

• Applying selection binding is a common method for linking widget selections to filters on other widgets. However, the binding must be done on the widget that needs to respond to the selection—in this case, the Pie chart. Therefore, applying selection binding in the Pie chart’s query filters (B) connects the List widget’s user selection with the Pie chart’s data, allowing the Pie chart to dynamically update based on the List selection.

• Because the List widget and the Pie chart are based on different datasets, these datasets must be connected so selections can propagate between them. The Connect Data Sources feature in Tableau CRM allows a consultant to create a relationship between datasets, making cross-filtering possible. Hence, creating a connection between the two datasets (D) is essential for enabling interaction across the widgets.

• Applying selection binding to the List widget’s query (A) will not resolve the issue because the List widget is the source of the selection, and it does not need to respond to itself.

• Creating a connection between widgets (C) instead of datasets is ineffective because widgets themselves are display elements; interaction is driven by the data behind them. Without connecting datasets, the widgets cannot share filter states properly.

By configuring selection binding on the Pie chart and connecting the two datasets, the consultant ensures that selections in the List widget properly influence the Pie chart, restoring expected dashboard interactivity.

Question 9:

Which three functions can the Tableau CRM Dashboard Inspector perform? (Select three.)

A. Automatically eliminate bottlenecks to speed up queries.
B. Provide a list of suggestions to improve dashboard performance.
C. Display the final query and its results for each executed query.
D. Show all queries executed and the time each took to run.
E. Show the total time required to execute all queries.

Correct Answer: B, D, E

Explanation:

The Tableau CRM Dashboard Inspector is a diagnostic tool that helps analyze and optimize the performance of dashboards within Tableau CRM. Its primary role is to give users insights into how dashboards are functioning, particularly by providing detailed information on query execution and offering ways to improve speed and efficiency.

Let’s examine the options:

A. Automatically eliminate bottlenecks to speed up queries: This is incorrect because the Dashboard Inspector does not perform automatic fixes. It highlights where bottlenecks or delays exist but leaves it to users or administrators to make adjustments based on the insights.

B. Provide a list of suggestions to improve dashboard performance: This is a key feature. The Inspector generates recommendations that guide users on how to enhance the dashboard’s efficiency, such as optimizing queries or reducing unnecessary data loads.

C. Display the final query and its results for each executed query: While the tool shows query execution details, it does not display the final data results returned by the queries. It focuses on performance metrics rather than query output.

D. Show all queries executed and the time each took to run: This is a core function. By showing the execution time for each query, users can identify which queries consume the most time and may need optimization.

E. Show the total time required to execute all queries: The Inspector aggregates the total query execution time, providing a holistic view of dashboard performance. This helps users understand the overall time spent processing queries.

In summary, the Tableau CRM Dashboard Inspector supports dashboard optimization by providing actionable recommendations (B), detailed timing for each query (D), and total execution time (E). Therefore, the correct choices are B, D, and E.

Question 10:

A customer is examining a report that aims to maximize daily sales quantities of consumer products in stores. The report includes a chart showing the impact of promotional activities in San Francisco. 

What is the most accurate conclusion drawn from this chart?

A. Display is the most effective promotion type overall.
B. Promotions increase sales more in San Francisco than in other cities.
C. The “Other stores” (represented by gray bars) show no meaningful data related to sales promotions.
D. Among all promotion types in San Francisco, Display promotions generated the highest sales.

Correct Answer: D

Explanation:

In this scenario, the customer is reviewing a visualization focused on how different types of promotions affect sales specifically in San Francisco. The question asks for the best conclusion based on the chart data related to promotional effectiveness in that city.

Analyzing the options:

A. Display is the most effective promotion type overall: This option suggests a general conclusion across all regions or stores, which is not supported here since the data provided only relates to San Francisco. Without a broader comparison, this statement cannot be confirmed.

B. Promotions increase sales more in San Francisco than in other cities: To make this claim, data comparing San Francisco’s sales lift to other cities would be necessary. However, the chart and context focus solely on San Francisco, so this conclusion cannot be drawn.

C. The “Other stores” (gray bars) show no significant sales promotion data: While “Other stores” are shown, the information provided doesn’t conclusively prove the insignificance of their promotional sales. Without clear evidence, this assumption could be misleading.

D. Among all promotion types in San Francisco, Display promotions generated the highest sales: This is the most accurate interpretation given the scenario. The insight focuses on San Francisco and identifies that Display promotions yielded the greatest sales compared to other promotional types within that city.

In summary, the chart’s key takeaway is that Display promotions in San Francisco resulted in the highest sales among promotional strategies. Thus, D is the correct conclusion based on the provided information.