Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Questions and Answers

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: https://help.salesforce.com/s/articleView?id=sf.bi_security_overview.htm &type=5

Einstein Discovery presents insights based on their importance in explaining the variation in the outcome variable, in descending order. This approach prioritizes insights that have the most significant impact on the outcome, helping users to focus on the most influential factors first. By sorting insights in this manner, Einstein Discovery ensures that users can quickly identify and address the key drivers of their business metrics, facilitating more effective decision-making based on the most critical data points. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_story_overview.htm &type=5

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.

The REST API URL where a consultant can find current template apps in Salesforce Tableau CRM (formerly Einstein Analytics) is "/services/data/vXX.X/wave/templates". This endpoint is specifically designed for interacting with template apps, allowing consultants to extend, customize, or retrieve information about available templates.

The steps in the data exploration process in Salesforce Tableau CRM are as follows:

• View: Initially viewing the data to understand what is available and identify areas of interest.
• Explore: Digging deeper into the data to discover patterns, anomalies, and insights.
• Refine: Adjusting the exploration by filtering, sorting, or enhancing the data visualization for clearer insights.
• Save: Saving the state of your exploration to revisit or continue working on later.
• Share: Sharing your findings with others in the organization, either through direct dashboard access, downloadable reports, or URLs.

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

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.

When validating the "Why it Happened" Insights provided by Einstein Discovery, the most appropriate action for a consultant to take is to review the p-values and standard deviations associated with the insights. These statistical measures indicate the reliability of the insights and whether they are statistically significant. By examining these values, the consultant and customer can better understand the robustness and credibility of the insights.

Einstein Discovery in Salesforce functions like having a personal data scientist on staff. It utilizes advanced machine learning algorithms to analyze large volumes of data, identify patterns, and provide predictive insights and recommendations. This tool simplifies complex data analysis tasks and enables users to make informed decisions without the need for deep statistical knowledge or expertise in data science. Reference: Salesforce Einstein Discovery Overview

Einstein Discovery offers several types of insights, among which "What Happened" is considered descriptive. This type of insight focuses on describing the data, summarizing past events or outcomes without delving into causation or forecasting future trends. It's crucial for understanding the basic facts of a dataset before moving on to more complex analysis.

Data that has already been loaded into Einstein Analytics as datasets can be utilized directly in Einstein Discovery without requiring a separate load process. This integration within the Salesforce platform allows seamless transition of data from analytics to discovery, facilitating a streamlined workflow for data analysis and enhanced decision-making. Therefore, Danielle can use existing datasets in Einstein Analytics for her analysis in Einstein Discovery without needing additional permissions or import steps. Reference: Salesforce Help - Using Data in Einstein Discovery

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.

alesforce Tableau CRM uses two internal users—Integration User and Security User—to manage access to Salesforce data for analytics purposes. The Integration User is used to extract data from Salesforce objects and fields during dataflow jobs, ensuring that data movements are secure and compliant with the organization's access policies. The Security User's permissions are employed when querying datasets with row-level security based on the User object, allowing for secure and appropriate data access based on user roles and permissions.

To build a table in a Sales Manager dashboard and include a summary as the first line in a compare table:

• A: Use the Show Summary option in the compare table. This feature automatically calculates and displays a summary row at the top of the compare table, providing an aggregated view of the data points included in the table.

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 Salesforce Tableau CRM Einstein Discovery:

• B is correct because in the Story Settings, the consultant can view and evaluate the strength of each predictor field, helping to determine which fields are most influential in the model.

To open the Actions menu for a particular case shown in a dashboard in Salesforce Tableau CRM:

• B: Hover over the case number column of the values table widget and click the arrow that appears. This action will display the menu related to that specific case, offering various actions that can be performed directly from the dashboard.

Using Chart Markers in a bar chart is an effective way to dynamically emphasize certain values, making it easier for users to focus on key information. Chart markers can be configured to highlight data points based on specific conditions or thresholds, such as highlighting values above or below a certain number, or showcasing changes over time. This visual differentiation aids in

In Tableau CRM, the default number of rows displayed in a compare table is 1,000. This setting provides a balance between detailed data exploration and system performance, ensuring users can analyze significant amounts of data without overwhelming the interface or processing capabilities.

Regarding Einstein Analytics Encryption, the true statements are:

• A: When Einstein Analytics Encryption is enabled, all newly registered datasets are encrypted at rest. This ensures that any data stored within the platform is secured against unauthorized access, protecting sensitive information.
• C: The customer's org must have a Shield Platform Encryption tenant secret. This requirement is necessary for enabling encryption capabilities within Salesforce, which includes Einstein Analytics, ensuring data security complies with organizational and regulatory standards.

The correct set of statements that generate a monthly amount on a cumulative basis annually is Option B. This option likely includes cumulative sum functions applied within a grouped statement, typically done with a grouping by 'Month' and an annual reset. Such SQL-like syntax is commonly used in Tableau CRM for time-based cumulative calculations.

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.

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: https://help.salesforce.com/s/articleView?id=sf.bi_security_predicates.htm &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\""

To properly analyze the Opportunity win rate using Einstein Discovery, it's crucial to:

• D: Change the grain by removing the line level detail as it will skew the win rate. This modification ensures that the analysis focuses on the opportunities as a whole rather than the potentially misleading details of individual line items, providing a more accurate view of the overall win rate.

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 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.

In the context of Einstein Discovery in Tableau CRM, "Second-order analysis" refers to the type of insight that examines how one variable explains variation of the outcome variable. This analysis delves deeper into understanding the relationships between variables, particularly how they interact to influence the outcome. Second-order analysis is more advanced than first-order, which typically looks at direct relationships without considering interactions between multiple variables. This level of insight helps in identifying underlying patterns that might not be apparent from simple direct analysis. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_understanding_analysis.htm &type=5

In a Compare Table within Tableau CRM, when you are working with formula columns, you can change both the formula itself and the name of the column. This allows you to tailor the calculations and the labeling to better fit the analysis needs and to make the table more understandable for users.

In Salesforce Analytics, the basic actions within a query that are fundamental to building meaningful visualizations and data summaries include:

• A. Aggregate by a measure: This involves summarizing data using statistical measures like sum, average, maximum, etc., which help in understanding the distribution and trends of data.
• C. Filter to narrow down your results: Filtering allows users to limit the data shown in the dashboard to only those records that meet certain criteria, thus focusing on more relevant subsets of data.
• D. Grouping by a dimension: Grouping involves categorizing data based on specific fields (dimensions), which helps in analyzing the data across different segments or categories.

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.

To build a lens showing the total number of Opportunities and Cases per Account using two separate datasets, the consultant can:

• D: Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results. This method involves writing a SAQL query that groups the data by Account and aggregates the number of Opportunities and Cases, providing a unified view of the data across both datasets.

The correct timeseries statement to fix the SAQL query for predicting sales in each sales region is:

• C: q = timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Year', 'Date_Quarter', "Y-Q"), partition='Region', seasonality=4). This statement correctly specifies the required parameters for a timeseries analysis, including the date columns, the partition by 'Region', and the seasonality factor, which is crucial for accurate forecasting in time series analysis.

Based on the "Why it Happened" graphic: A. Germany performs lower than average but better when the product is Call Center. This interpretation is supported by the presence of the Call Center product in Germany as a factor that increases the probability of Opportunity Won, contrary to its general performance. D. Germany sells more Call Center product and that helps them increase their win rate. The graph indicates that Call Center products have a positive impact in Germany on the win rate, suggesting that the higher sales volume of this product helps improve overall performance in the region.These insights are derived from understanding the visualization in the context of what contributes positively and negatively to the outcomes, which is a key feature of Einstein Analytics. Reference: Einstein Discovery Insights

In Tableau CRM's Insights list, insights are ranked according to their statistical significance. This means that the insights considered most impactful or relevant based on the underlying statistical analysis are presented first. This ranking helps users quickly identify the most critical insights derived from their data.

In statistical terms, the standard deviation measures the amount of variation or dispersion in a set of values. A higher standard deviation indicates that the values are more spread out from the mean (average). Among the options provided, a standard deviation of 16 suggests the highest degree of spread away from the average compared to the other values.

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.

The Actions menu in a chart within Salesforce Tableau CRM (Einstein Analytics) typically includes options that enhance interactivity and integration with Salesforce features. One of the key functionalities offered by the Actions menu is the ability to post to Chatter directly from a chart. This allows users to share insights, collaborate on data visualizations, and engage with team members directly within the flow of analytics. Reference: Salesforce Help - Chatter in Dashboards

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.

To optimize data loads by extracting Salesforce objects ahead of time, the correct construct to use is "Data Sync (Replication)." This feature in Salesforce Einstein Analytics allows for the periodic refresh of datasets from Salesforce objects without needing to rerun the entire dataflow. This optimization helps in managing large datasets more efficiently by keeping the data up-to-date with less processing time and system load.

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: https://help.salesforce.com/s/articleView?id=sf.bi_apps_configure.htm &type=5