Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant(SP23) Questions and Answers

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.

To address the performance issue with data sync (replication) caused by unnecessary data being synced, the consultant should:

• D: Update the existing data sync (replication) process by removing unused fields with a filter to extract relevant data. This approach minimizes the data volume being synchronized by focusing only on relevant fields and records, thereby improving the performance of the data sync processes.

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.

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.

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 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.

When extending the Sales Operations app to distribute it as an Einstein Analytics template app, the correct endpoint to post the folderSource ID (as shown in the JSON snippet provided) is "/services/data/vXX.X/wave/templates". This endpoint is used for managing template apps in Einstein Analytics, allowing the consultant to package and distribute standardized analytical content across different Salesforce instances or to different user groups within the organization.

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.

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

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 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.

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.

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.

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

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.

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

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.

From the description of the tooltip and the insight provided:

• A is correct because it specifically compares the average daily sales in San Francisco to a global average, showing a significant difference.

To ensure a user can view dashboards within a designated app without the ability to create and save lenses, the correct action is to assign the Viewer role to the user. This role allows the user to access and interact with the dashboards and apps but restricts the ability to modify, create, or save anything within the app, aligning perfectly with the requirement specified.

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.

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

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.

To increase adoption among account managers who need comprehensive account overviews, embedding a dashboard onto the account record page with filters that dynamically adjust to show relevant data about active cases, opportunities, and product whitespace is most effective. Here’s how to implement this:

• Design a comprehensive dashboard in Tableau CRM that includes all the necessary lenses and widgets to show key details such as active cases, opportunities, and potential product sales opportunities.
• Ensure the dashboard can dynamically filter data based on the account being viewed. This typically involves using dynamic dashboard filters linked to the account record page.
• Embed the dashboard onto the Salesforce account record page. This integration allows account managers to have all the necessary information directly within the context of the account they are viewing, enhancing usability and relevance.

This approach promotes better user engagement by providing relevant data directly within the user's workflow.

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

In Salesforce Tableau CRM, the timeout for ETL (Extract, Transform, Load) jobs that have been scheduled but not executed is 15 minutes. This timeout setting helps manage system resources efficiently by ensuring that jobs do not indefinitely occupy system capacity if they fail to initiate.

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

https://help.salesforce.com/articleView?id=bi_dashboard_build_tips.htm &type=5

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.

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.

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.

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

Faceting in Tableau CRM (formerly Einstein Analytics) is a powerful feature that allows different charts and components within a dashboard to interact with each other. When you make a selection in one chart, it automatically applies that filter across other charts within the same dashboard. This interconnected filtering enhances the analysis by allowing users to explore data across multiple dimensions simultaneously, providing a more holistic view of the data set. Faceting ensures that all visual components are contextually relevant, improving the user's ability to draw insights from the data. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboards_faceting.htm &type=5

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.

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.

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.

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

* template-info.json manages all elements of your template, including metadata information about the template, the Analytics objects that define dashboards and lenses, and the other files that are part of the template.* ui.json manages the configuration wizard that drives app creation. It defines the number of wizard pages, the order of wizard questions, and any messages you want the user to see.* variables.json contains all template variables, including text for wizard questions and specifications for the answers. Variables also define conditional questions. For example, you may want some questions to appear in the wizard only if an org contains certain data. Or you might want to add more specific questions based on the answers to more general wizard questions.* template-to-app-rules.json defines the rules the template follows. For example, you could define a rule that specifies that if an org doesn’t use certain Salesforce objects, the app won’t refer to them in dashboards or include them in the dataflow. Rules also define how variables are handled. For example, if the wizard asks which fields to include in filters for accounts, template-to-app-rules.json determines how that choice is reflected in dashboards.

XMD stands for Extended Metadata. This component in Salesforce Tableau CRM allows users to customize datasets and dashboards further. With XMD, users can define label overrides, specify numeric formats, set up color coding, and more, enhancing the visualization and interpretation of data within the analytics environment.

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.

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 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.

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

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 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.

Posting to Chatter from a Salesforce Wave (Tableau CRM) dashboard is done by selecting "Post to Chatter" from the Actions drop-down menu at the top of the dashboard. This feature allows users to directly communicate findings, share insights, or discuss data visible on the dashboard with other team members through Salesforce Chatter, enhancing collaboration and information sharing. Reference: Salesforce Documentation - Post to Chatter from Dashboards

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

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.