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

In Tableau CRM (formerly Einstein Analytics), a dataset recipe allows users to combine data from different datasets, enriching the data through transformations such as joins, aggregations, and calculated fields. This functionality enables users to integrate varied data sources into a unified dataset that can be further analyzed and visualized within the platform. Recipes provide a user-friendly interface to manipulate and merge data without the need for complex coding, making it accessible to users with varying levels of technical expertise. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_prep_recipes.htm &type=5

In the formulas used within a Compare Table in Tableau CRM, you can refer to other columns by using their names. This method provides clarity and maintains consistency in the formula, making it easier to understand and modify if necessary. Referring to columns by their names, as opposed to numbers or letters, helps prevent errors and improves the manageability of complex analytics formulas. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboard_widgets_compare_table_advanced.htm &type=5

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.

In Einstein Analytics, the "sfdcDigest" node is used in a dataflow to extract data from a registered dataset. This node is specifically designed to query and ingest data from Salesforce objects and datasets into the dataflow for further transformations and analyses.

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.

To potentially improve an AUC score of .62 in a Tableau CRM story, consider the following strategies:

• A. Segment the training set: By segmenting the training set, you can ensure that the model trains on more homogeneously grouped data, which might help in improving the model’s predictive accuracy and, consequently, the AUC score.
• D. Add more rows of relevant data: Increasing the volume of relevant data can provide the model with more examples to learn from, which might enhance its ability to generalize and improve the AUC score.

Each prebuilt Analytics app has its own PSL. When you set up permissions for those apps, select the right PSL. For Service Analytics, select "Service Analytics Apps." For Event Monitoring, select "Event Monitoring Analytics Apps," and so on.

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.

To address the need for department-specific dashboard access while utilizing common datasets with the same row-level security, the best approach is to:

• C: Create one application for each department, put common datasets in the shared app, and use roles to share applications. This setup ensures that each department has its own tailored application containing relevant dashboards while still accessing shared datasets from a central application. The use of roles facilitates controlled access based on user roles within the organization.

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.

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.

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.

Predictive insights are primarily beneficial for:

• C: Drilling down into the underlying reasons behind a prediction. This capability enables users to understand not just what might happen in the future but why those outcomes are likely, based on the data being analyzed. This understanding is crucial for strategic decision-making and for optimizing processes based on predictive analytics.

An Einstein Discovery story in Salesforce is essentially a detailed analysis presentation consisting of charts, statistical data, and text. Its purpose is to help users understand the relationships between a single outcome variable and the various factors that influence it. The story uses data exploration and advanced analytics to reveal patterns and insights that can guide decision-making processes. Reference: Salesforce Help - Einstein Discovery Stories

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

For Benito to build and edit a dashboard using the Seed Bank dataset in the Einstein Analytics app, the admin needs to do the following two things:

• Grant Benito access to the specific app that contains the Seed Bank dataset. This ensures he can access the necessary dataset within the confines of the designated app.
• Assign him a permission set that includes the "Create and Edit Analytics Dashboards" permission. This permission is crucial for enabling Benito to not only view but also create and modify dashboards within the app.

This dual requirement ensures both data accessibility and the ability to perform required operations on the dashboard. Reference: Salesforce Help - Permission Sets

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.

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 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.

The Chi-square test in statistics is used to identify whether there is a significant association between categorical variables. It is especially useful in scenarios like in Tableau CRM where one might want to determine if the differences between observed and expected frequencies in one or more categories are just random variations or are statistically significant. This test helps in identifying categories that exhibit a pattern that deviates from the overall average, making it a valuable tool for data analysis in categorical data sets. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_statistical_tests.htm &type=5

The best connection mode for Universal Containers, given their need to sync updated and new records from the Order object weekly, including updates to formula fields, is Periodic Full Sync. This mode will:

• Periodically sync all records from the source object (in this case, weekly).
• Ensure that all fields, including formula fields that may calculate values based on other periodically updated fields, are current in the destination system.

This mode is especially suitable when data completeness and accuracy are crucial, and where changes in the dataset need to reflect consistently, including computed or derived fields.

In Tableau CRM, recipes allow users to create new datasets from various sources. The sources that can be utilized include:

• A. Existing datasets: Recipes can combine or transform data from datasets that have been previously created or imported into Tableau CRM.
• B. Records from a Salesforce object: Direct extraction of data from Salesforce objects into a new dataset via a recipe is supported, allowing users to leverage live Salesforce data for further analysis.

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.

A merge key in a dataset recipe is used to join related data from different sources within Tableau CRM. By specifying merge keys, users define the common fields between datasets that serve as the basis for combining records. This process is critical for creating comprehensive datasets that pull in relevant data from disparate sources, ensuring data accuracy and consistency across the joined datasets. The use of merge keys facilitates the alignment of data based on shared attributes, crucial for accurate analysis and reporting. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_prep_recipes_join_data.htm &type=5

The scenario described where Yasmine cannot see any rows in the Seed Bank Orders dataset while Hadiqa can, likely indicates that the dataset's security predicate was set up to restrict data visibility based on user roles or attributes. Security predicates in Salesforce Tableau CRM are used to implement row-level security, ensuring that users only see data pertinent to their role or permissions within the organization. This security setup is crucial for maintaining data privacy and compliance. Reference: Salesforce Help - Row-Level Security

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.

When discrepancies arise between the aggregate sum in a dataset and manual calculations in Salesforce, common issues include: C. Differences involving formula fields. Formula fields in Salesforce are calculated dynamically and may not always be calculated during data sync if they depend on other fields that aren't triggered by record updates. D. Differences involving trigger-updated fields. Fields that are updated by triggers might not have their latest values captured if the triggers were executed after the data was replicated. These scenarios can cause inconsistencies in data reflected in Einstein Analytics compared to what is stored directly in Salesforce. Reference: Salesforce Documentation on Data Sync and Replication

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 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\""

A Bar chart is best suited to track the progress of sales revenue that is realized versus the target. Bar charts are effective for comparing multiple categories of data, such as actual versus target sales, side by side. This visual comparison makes it clear to see how close the realized sales are to the targets, which is crucial for quick assessment and decision-making in sales performance monitoring.

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.

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.

On the Edit Field Attributes page when uploading a CSV file to Analytics (Tableau CRM), you can perform several modifications to ensure the data is properly formatted and structured for analysis:

• Change a field's name: Adjust the names of fields to match your analytics schema or to make them more understandable.
• Change a field's format: Modify the format especially for date, number, or any specific custom format required for your analysis.
• Change a field's type: Alter the data type (e.g., from string to integer or vice versa) to suit the data operations you plan to perform. These capabilities allow for the customization and preparation of data directly within the platform, streamlining the process from data upload to insight generation. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_datasets_upload.htm&type=5

In Salesforce Tableau CRM, dashboards can be shared with others within your organization using the "Share" icon located on the dashboard itself. This feature allows users to:

• Set up specific sharing options to control who can view or edit the dashboard.
• Post a screenshot of the dashboard directly to Chatter to facilitate discussion or collaboration.
• Obtain a URL that can be shared with other users who have the necessary permissions to access the dashboard.
• Download a screenshot of the dashboard for use in presentations or reports outside of Salesforce.

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 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.

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.

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.

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.

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, selecting the correct root object when using the dataset builder is crucial because the root object determines what related objects can be added to the dataset. The root object sets the base or primary focus of your dataset, and its relationships define the scope of additional data that can be included through joins and links to other Salesforce objects. Selecting an inappropriate root object might restrict the range of data that can be effectively utilized or require complicated workarounds to incorporate necessary related data. Reference: Salesforce Help - Selecting the Right Root Object

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.

In Tableau CRM, each run of a recipe counts towards the 24-hour rolling recipe run limit, regardless of the duration of each recipe. Therefore, if all three recipes (myRecipeOne, myRecipeTwo, and myRecipeThree) are executed, all three count towards the limit. This is crucial for managing system resources and ensuring optimal performance of the analytics environment.

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 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

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

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.

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.