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

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

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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.

To access Einstein Analytics (now known as Tableau CRM), having just an Einstein Analytics Platform license is not sufficient. Users also require specific permissions to use analytics functionalities. The critical permission is "Use Analytics," which should be granted via a permission set. Without this permission, users can't access or use Tableau CRM, even if they have the necessary license.

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.

For Universal Containers to automate the creation of datasets from their on-premise data warehouse, the best solution would be to utilize middleware with the Analytics External Data API. This approach enables the integration of on-premise data systems with Salesforce through a secure and scalable middleware, which can automate the data extraction and loading process into Tableau CRM, thus replacing the weekly manual process.

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.

The widget property that allows a consultant to restrict the view to a domain bounded by the values entered is "Add Custom Range." This property enables setting specific range limits on the data displayed in the widget, effectively controlling the scope of data visualization to ensure focus on relevant metrics.

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.

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.

The most straightforward and effective way to ensure that temperature data is easy to interpret on Einstein Analytics dashboards is to add a text widget to the dashboard indicating whether the temperature is in Fahrenheit or Celsius. This method provides clear, unambiguous context directly within the dashboard view, improving user understanding at a glance.

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

To replace null results in the YoY Growth calculation with a 100% value when there is no account in the 'Last Year' column, the correct function to use is:

• C: coalesce(). This function is used to return the first non-null value in a list of arguments. In the formula for calculating YoY Growth, it can be used to check for a null value in the 'Last Year' data and replace it with a default value (like 100), effectively handling cases where no comparison data exists.

To resolve the issue where selections in the List widget do not affect the Pie chart due to the use of different datasets in an Einstein Analytics dashboard, an Einstein Consultant can:

• A: Use 'Connect Data Sources' and create a connection to connect the two datasets. This approach establishes a relationship between the datasets, enabling interactions between widgets using these datasets to affect each other.
• B: Use selection binding in the filters section of the step "Step_pie_2." Selection binding will allow the Pie chart to dynamically adjust based on selections made in the List widget, by applying the selected filters directly to the Pie chart's dataset.

In Salesforce Analytics Query Language (SAQL), a keyword refers to specific commands or functions such as 'group', 'foreach', 'filter', and more. These keywords are essential for structuring queries and must be used in lowercase to be recognized by the system. Keywords control operations on data streams within the queries, facilitating complex data manipulations and analyses.

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 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.

The "Unexplained Bar" in the "Why it Happened" chart of Einstein Discovery represents the difference between the predicted outcome and the observed outcome. This metric is essential as it highlights the variance that the model's explanatory factors do not account for, providing insight into the limitations of the current model. This understanding aids in interpreting the accuracy and reliability of the model predictions.

In Salesforce Tableau CRM (Einstein Analytics), the visibility of data in a dataset like the Seed Bank Orders is controlled by the app manager within the context of the app to which the dataset has been added. Therefore, a project team member with the Viewer role in the Mosaic Seed Bank project team can see only the rows in the dataset that are designated by the App Manager. This access is configured to ensure data security and relevancy based on user roles and permissions. Reference: Salesforce Help - Dataset Security

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

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.

To analyze data from a CSV file in Einstein Discovery, the correct procedure involves importing the CSV file into an Einstein Analytics dataset and subsequently creating a story from this dataset. This method allows you to fully leverage Einstein Discovery's capabilities to analyze and derive insights from the data presented in the CSV file.

Among the listed options, which is not a method for controlling record-level access in Salesforce:

• A: Profiles. While profiles control what a user can do broadly within the Salesforce platform (such as read, create, edit, and delete permissions on objects), they do not directly control record-level access. Record-level access is specifically managed through Sharing Rules, Organization-Wide Defaults, and Role Hierarchy.

Before users can access Tableau CRM (Einstein Analytics), they must be assigned specific permissions. These include: A. An Analytics permission set license (PSL), which grants the user the license to use Analytics. B. A permission set configured with Analytics user permissions, which detail what aspects of Analytics the user can access. This setup ensures that both the licensing and access rights are correctly aligned to user roles and needs within the organization. It is essential to configure both aspects to enable functional and compliant use of the platform. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security.htm &type=5

The maximum number of concurrent API Analytics calls per organization in Salesforce Tableau CRM is 200. This limit helps to ensure that the system can handle multiple API requests efficiently without degrading the performance for users across the organization.

When clients express disappointment that Einstein Discovery only detected known patterns, an appropriate response is to highlight the efficiency benefits, specifically how Einstein significantly reduces the time to insight compared to traditional methods. This emphasizes the value of using Einstein Discovery to quickly validate known patterns and potentially explore nuances within those patterns more swiftly than through manual analysis.

The likely missed step in the deployment process of a Tableau CRM analytic app that includes a recipe and several datasets and dashboards, where the datasets aren't appearing post-deployment, is that the recipe needs to be run. Here's what should be done:

• Navigate to the Tableau CRM Analytics Studio.
• Locate the deployed recipe within the app.
• Manually initiate the recipe to run, or verify if it's scheduled to run automatically. If not, set up a schedule.
• Ensure that once the recipe runs, it processes and creates the datasets as designed.

This step is crucial because recipes are responsible for transforming data and generating the datasets needed for the dashboards.

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.

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.

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

When deploying models to an existing prediction in Tableau CRM, it is important to be aware of the limits imposed by the platform. Specifically, there is a limit of 10 models under a single Prediction Definition. This limit helps manage the complexity and ensures that the predictions remain manageable and efficient.

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

In Einstein Discovery stories, insights show how different variables, or combinations of variables, explain the variation of the outcome variable. The outcome variable is the target variable whose behavior and influencing factors are being analyzed and predicted by the model. Insights in the story will help understand how each explanatory variable and their interactions impact the outcome, which is central to predictive analytics and machine learning in Einstein Discovery. Reference: Salesforce Help - Einstein Discovery Insights

When you configure the story, you tell Einstein Discovery to maximize/minimize the variable. The variable is 'outcome variable' in your story

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.

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

An Analytics app in Salesforce is a collection of datasets, dashboards, lenses, and a dataflow. These components are bundled together within an app to provide a comprehensive analytics solution tailored to specific business needs or project requirements. The app facilitates organized access to related analytics assets, making it easier for users to navigate and utilize data for decision-making and insights. Reference: Salesforce Help - Understanding Analytics Apps

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.

For a dashboard that needs to visualize Closed Won opportunities by different time groupings (month, quarter, year) using a single chart, the appropriate action is: D. Use selection binding to update the grouping in the chart. This method allows the chart to dynamically change the grouping based on user selection, effectively switching between month, quarter, or year views within the same visual representation. Reference: Salesforce Documentation on Dynamic Dashboards

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

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

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.

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.

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.

In Salesforce Analytics Query Language (SAQL), the order of functions plays a crucial role in how data is processed. The 'filter' and 'order' functions can be used interchangeably depending on the specific needs of the query. However, 'offset' should always follow either 'filter' or 'order', and 'limit' must be specified after 'offset'. This order ensures that the query first narrows down the data set, then offsets into this subset, and finally limits the number of records returned.

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