Quiz 2026 Analytics-Con-201: Salesforce Certified CRM Analytics and Einstein Discovery Consultant Authoritative VCE Exam Simulator

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Salesforce Analytics-Con-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Layer	25%	- Identify the right data sources and data flows for CRM Analytics 1. Define datasets 2. Describe how Salesforce objects map to CRM Analytics 3. Describe use cases for Data Prep (Dataflows and Recipes) - Implement and configure the right data transformations 1. Identify and resolve data sync issues 2. Use Data Prep actions
Topic 2: Security	10%	- Determine security and sharing settings 1. Implement row-level security 2. Describe how Salesforce security is applied in CRM Analytics
Topic 3: Analytics Dashboard Design	20%	- Design and build CRM Analytics dashboards 1. Apply design best practices 2. Describe the use cases for Visualforce and Lightning components 3. Use Lenses and Dashboards
Topic 4: Analytics Dashboard Configuration	25%	- Configure dashboards 1. Apply query filters 2. Set up dynamic dashboards 3. Configure bindings
Topic 5: Einstein Discovery	20%	- Implement Einstein Discovery 1. Configure and deploy Einstein Discovery models 2. Integrate predictions into CRM Analytics dashboards - Describe Einstein Discovery concepts 1. Identify the use cases for Einstein Discovery 2. Interpret model results 3. Explain the model building process

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q61-Q66):

NEW QUESTION # 61
consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.

Which action should the consultant take?

A. Remove the Store field from the model definition, because that is the recommended action.
B. Verifyclient expectations that Store is a strong predictor for daily sales quantity.
C. Ignore alert; the explanation of variation is only 35%, which is below 50%,

Answer: B

Explanation:
Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here's the rationale:
* Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired.
* Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions.
* Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It's about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making.
By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client's business strategies and operational realities.

NEW QUESTION # 62
Universal Containers plans to upload target data from an external tool to CRM Analytics so it can calculate the sales team target attainments.
The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales rep, the target amount, and the month of the target. For each sales rep, the file contains a target for every month of the current year as well as all previous years.
Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?

A. Append
B. Update
C. Overwrite

Answer: C

Explanation:
For uploading target data that changes on a monthly basis and includes historical data (previous years' targets), the appropriate operation is "Overwrite." This ensures that each time the CSV file is uploaded, the existing data in the dataset is replaced with the new data. This is critical because the target data includes both current and historical data, and using "Overwrite" will update the entire dataset while maintaining historical accuracy.
"Append" would add new data without replacing the old records, leading to duplication, and "Update" is not suitable for completely replacing data in this context.
Reference: The usage of the "Overwrite" operation for updating datasets with changing data is covered in theAnalytics External Data APIdocumentation, as well as in theWave Analytics Explorermodule.

NEW QUESTION # 63
Universal Containers (UC) has a "Sales Manager" dashboard. UC has a compare table that has multiple groupings and columns added showing the Total and Subtotals of the numeric values. A consultant is asked to add additional groups to enhance details about UC's customers.
Which feature should the consultant use to make the navigation of the compare tables easier for the end user?

A. Select the Enable Expand or Collapse option from the table properties.
B. Scroll to the very end to see the details.
C. Create the table using SAQL query to accommodate this and make It user friendly.

Answer: A

NEW QUESTION # 64
What is a benefit of introducing a second local connector?

A. Better maintenance by having a connector per recipe
B. Better deployment management between sandboxes and production environment
C. Better performance by syncing data according to the refresh necessities

Answer: C

Explanation:
Introducing a second local connector in CRM Analytics can improve performance by enabling more granular control over data syncs. By having a separate connector, different datasets or recipes can be synchronized independently based on specific refresh needs, reducing load and improving overall performance. This approach helps optimize data flow operations, especially in large-scale deployments with varying data refresh requirements.

NEW QUESTION # 65
Which statement best describes how to ensure CRM Analytics dashboards are easily used across both desktop and mobile devices?

A. Create multiple layouts and allow CRM Analytics to automatically select and organize dashboard contents to be optimal for the device type.
B. Create multiple layouts, ensure the layout selectors match the device, and resize/hide widgets to ensure the content is appropriate for the device screen size.
C. Create multiple layouts and reorder all the widgets so that they fit nicely within the new default width

Answer: B

Explanation:
To ensure that CRM Analytics dashboards are optimally usable on both desktop and mobile devices, creating multiple layouts tailored to each device type is crucial. Here's why Option C is the best approach:
* Device-Specific Layouts: By creating specific layouts for each device type, you ensure that the dashboard contents are presented in a manner best suited to the screen size and interaction model of the device.
* Layout Selectors: These are used to automatically display the appropriate layout based on the device accessing the dashboard, enhancing user experience without manual intervention.
* Widget Customization: Resizing or hiding certain widgets for specific device layouts ensures that the dashboard remains clean, uncluttered, and easy to navigate, regardless of the device used.

NEW QUESTION # 66
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