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Salesforce Analytics-Con-201 Exam Syllabus Topics:

Section	Weight	Objectives
Analytics Administration & Deployment	18%	- Implementation & Maintenance 1. Use Dashboard Inspector and Publisher 2. Monitor and optimize query performance 3. Manage versioning and deployment
Security & Access	12%	- Sharing & Permissions 1. Implement row-level security and inheritance 2. Configure app, dataset, and field-level security 3. Manage user and group access
Einstein Discovery	22%	- Deployment & Usage 1. Interpret results and recommendations 2. Embed predictions in dashboards and Lightning pages 3. Monitor and maintain model performance - Model Building & Configuration 1. Train, evaluate, and refine models 2. Select prediction type: numeric, binary, multi-class 3. Prepare and assess data for modeling
Dashboard Design & Development	25%	- Design Principles 1. Apply best practices for usability and performance 2. Design for mobile and Lightning compatibility - Build & Configure Dashboards 1. Create lenses, steps, and visualizations 2. Use SAQL for advanced queries 3. Implement bindings, filters, and interactions
Data Layer	23%	- Data Sources & Integration 1. Extract, load, and prepare data using Data Manager 2. Define datasets and map Salesforce objects 3. Use Dataflows and Recipes for transformation - Data Management & Optimization 1. Resolve data sync and refresh issues 2. Implement data modeling and relationships 3. Optimize dataset performance

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2026 Salesforce Analytics-Con-201: Salesforce Certified CRM Analytics and Einstein Discovery Consultant –High-quality New Questions

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q53-Q58):

NEW QUESTION # 53
Cloud Kicks (CK) has a dashboard in CRM Analytics with forecasting data. One widget is a compare table using the timeseries function showing the quarterly forecast. However, CK is interested in enhancing the dashboard with a weekly forecast per customer.
How should CK achieve this?

A. An SAQL query with a timeseries statement specifying Opportunity Name as the partition and Y-M-W as the Date Cols
B. An SAQL query with @ timeseries statement specifying Account Name as the partition and Y-M-D as the Date Cols
C. An SAQL query with a timeseries statement specifying Account Name as the partition and Y-M-W as the Date Cols

Answer: C

Explanation:
To create a weekly forecast per customer, anSAQL (Salesforce Analytics Query Language)query can be used.
The timeseries function generates forecast data based on a specified date range. In this case, the forecast is customer-specific, meaning the partition should be based onAccount Nameto ensure that the forecast is generated for each customer. The date column (Date Cols) should be set to Y-M-W to aggregate the forecast data on a weekly basis.

NEW QUESTION # 54
Universal Containers has a dashboard for sales managers. They need the ability to visualize the number of Closed Won opportunities by month, quarter, or year, and then display the result in a single chart. A CRM Analytics consultant creates a custom query to display three values:
ClosedDate_month, ClosedDate_ quarter, and ClosedDate_year.
What should the consultant do next?

A. Use result binding/interaction to update the grouping in the chart.
B. Use selection binding/interaction to update the measure In the chart.
C. Use selection binding/interaction to update the grouping In the chart.

Answer: C

NEW QUESTION # 55
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "* & **( & *(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?

A. The column header Is set to * & **( & *(%.
B. The columnheaderis prefixed with "X" upon upload.
C. The column header is auto-updated to "Column" + column number.

Answer: B

Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as " & **( & (%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
Reference: You can find the CSV upload rules, including naming conventions and auto-formatting behaviors, outlined in theSalesforce CRM Analyticsdocumentation provided during training, particularly under the module "Wave Analytics Explorer."

NEW QUESTION # 56
A CRM Analytics administrator is working on deploying a dashboard and a dataset from a developer sandbox to a full sandbox. They have deployed the dataset via change set and manually copy-pasted the dashboard JSON into the target org. However, they notice that the conditional formatting and the widget-specific number formats have been lost in the target environment.
What is causing this issue?

A. Analytics Dashboard XMD was NOT included as part of the deployment package.
B. Analytics Dataset XMD was NOT included as part of the deployment package,
C. The recipe that generated the dataset also needs to be included as part of the package.

Answer: B

Explanation:
When deploying a dataset and dashboard between environments in CRM Analytics, it's essential to include theExtended Metadata (XMD)file, which controls aspects such as conditional formatting and number formatting. In this case, the administrator manually copied the dashboard JSON but did not deploy the Analytics Dataset XMD, which leads to the loss of conditional formatting and widget-specific number formats in the target environment. Including the XMD ensures that all formatting and metadata are transferred correctly.
Reference: Detailed information on XMD files and their role in formatting is covered in theCRM Analyticsdeployment and customization documentation.

NEW QUESTION # 57
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. Ignore alert; the explanation of variation is only 35%, which is below 50%,
B. Verifyclient expectations that Store is a strong predictor for daily sales quantity.
C. Remove the Store field from the model definition, because that is the recommended action.

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 # 58
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