Free and Premium Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Dumps Questions Answers

The edgemart transformation, which grants a dataflow access to an existing, registered dataset, is specified in the Dataflow Editor. This tool within Salesforce Tableau CRM allows users to define and manipulate dataflow transformations, integrating various datasets into the analytical environment.

In Einstein Analytics, the limit for uploading external files to a dataset on a rolling 24-hour basis is 50 files. This limitation is designed to manage the data ingestion process effectively and ensure optimal performance of the analytics system.

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.

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 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.

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: &type=5

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: &type=5

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: &type=5

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.

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.

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.

To enable Analytics for Communities in Tableau CRM, you need specific prerequisites:

• License Requirements: A Customer Community Plus or Partner Community license is necessary, enabling more extensive CRM functionalities for community members.
• Permission Set License: Community members must be assigned the Analytics for Communities permission set license. This setup ensures that community users have the appropriate access to use and benefit from Analytics features within their community environments.

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.

To display boolean fields in a dataset as "Yes" in green for true and "No" in red for false across all dashboards:

• A: Download the XMD for the dataset and change the values and color for the fields and then upload to the dataset. Modifying the Extended Metadata (XMD) allows for comprehensive customization of how data values are displayed across all views that utilize the dataset.
• D: In the explorer, select the boolean fields, then create a derived dimension that references the boolean fields, and then modify the values and colors of the derived dimension. This approach allows for custom display settings to be applied directly within the explorer tool, tailoring the presentation of boolean values on a per-dashboard basis.

To delete activities in Dataset A related to users in Dataset B, the consultant can use a combination of dataflow transformations: "augment" and "filter." First, augment Dataset A with Dataset B using "userID" to combine the datasets based on user association. Then, apply a filter to retain only those records in Dataset A that do not have a corresponding entry in Dataset B.

To improve dashboard performance in the face of growing datasets, the Einstein Analytics consultant can take the following actions:

• B: Distribute steps among separate pages: This reduces the load on any single page, enhancing performance by only loading data necessary for the view being accessed.
• D: Replace separate step filters with a global filter: This reduces the number of queries and processing needed as the global filter applies universally, streamlining data handling.
• E: Move calculations to a dataflow: Offloading calculations to a dataflow minimizes the processing required at runtime, which can significantly improve dashboard performance.

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

• Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.
• Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.
• Availability on both desktop and mobile platforms, ensuring accessibility across different devices.
• It serves as an excellent starting point for service analytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies. Reference: &type=5

In Einstein Discovery, it is not necessary to remove all potential collinear fields before executing the build story because:

• B: No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting. Einstein Discovery includes mechanisms such as ridge regression to manage collinearity, which helps prevent the model from over-fitting even when collinear variables are present.

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 "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.

To address the issue of stages appearing in alphabetical order rather than sales cycle order in a funnel chart, a consultant can:

• Use a dataflow to rename the stages with a preceding number (A): This method involves preprocessing the data to append a numerical prefix to each stage name, thus forcing a specific order in the visualization.
• Use a dataset XMD to rename stages (D): The Extended Metadata (XMD) of a dataset allows for the customization of how data is displayed, including defining custom labels for dimensions which can enforce a particular order in the visualization.

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

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 (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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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

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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.

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.

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 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.

To automate the loading of data from a Microsoft Azure SQL Data Warehouse into an Einstein Analytics dataset, transform it, and merge it with company data daily, the following Einstein Analytics user interface features can be used:

• A: Dataflow: This tool allows for the creation and management of complex data transformation and integration processes necessary for combining data sources.
• D: Recipes: Recipes provide a user-friendly interface for performing data transformation tasks without writing code, which can be used for merging and transforming data.
• E: Analytics REST API: The API can automate data ingestion and management tasks, providing flexibility and control over data integration from external systems.

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.

When Salesforce Analytics runs a dataflow that accesses Salesforce objects, it uses the Integration User. This is a system user configured to perform back-end operations that require interacting with Salesforce data, independent of any specific human user. This setup ensures that data operations such as dataflows are consistent and not tied to the permissions or roles of individual users, which could vary and affect the execution of such tasks. Reference: Salesforce Help - Dataflows and Integration User

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.

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.

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.

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

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.

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.

To meet the requirement where each sales rep can view attainment data for reps in their division but only see their own commission data, applying a security predicate on the existing single dataset is the most efficient and secure method. This approach allows granular control over data visibility based on user-specific criteria, directly aligning with the security and privacy needs specified.

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: &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.

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.