Analytics-Con-201最新考題 & Analytics-Con-201信息資訊

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Salesforce Analytics-Con-201 Exam Syllabus Topics:

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

>> Analytics-Con-201最新考題 <<

Analytics-Con-201信息資訊 - Analytics-Con-201認證

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最新的 Salesforce Consultant Analytics-Con-201 免費考試真題 (Q19-Q24):

問題 #19
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 selection binding/interaction to update the grouping In the chart.
B. Use selection binding/interaction to update the measure In the chart.
C. Use result binding/interaction to update the grouping in the chart.

答案：A

問題 #20
Cloud Kicks has informed CRM Analytics developers that they have two scenarios with restricted row-level security.
The parameters being:
1. Non-CXOs and VPs working in EMEA can have access to EMEA records only.
2. CXOs and VPs should have access to all data irrespective of the region (APAC, EMEA, etc.).
Which sharing method works for this scenario?

A. Create two separate datasets; one for EMEA, and one for CXOs and VPs.
B. Use a field on the user record like Department/Region, and apply row-level security based on that field.
C. Create two sets of dashboards; one for EMEA, and one for CXOs and VPs while filtering the dashboard on the region.

答案：B

解題說明：
For Cloud Kicks' requirements regarding access to data based on roles and geographic regions, the most efficient and scalable approach is to implement row-level security using fields on the user record, like Department or Region. Here's the rationale for choosing this approach:
* Scalability and Maintenance: By applying security rules based on user record fields, Cloud Kicks can manage access dynamically without needing to maintain multiple dashboards or datasets. This reduces administrative overhead and simplifies updates as roles or regional structures change.
* Flexibility: Using a field on the user record to control access allows for easy expansion or modification of security policies as new regions or roles are added.
* Simplicity: This method ensures a clear and straightforward security model that can be easily audited and understood by administrators and compliance teams.

問題 #21
A CRM Analytics consultant is asked to make changes to the current sales dashboard at Cloud Kicks. The dashboard is crucial to track the daily sales performance of the company and needs to be available for other users while the consultant works on the changes.
How should the consultant proceed to update the dashboard?

A. Clone the dashboard to a new one, apply the changes, share the new dashboard with the users, and delete the old one.
B. Wait for a period of least usability or the dashboard to edit it.
C. Self assign as a dashboard publisher and make the changes to the dashboard in draft mode while maintaining a previous version live.

答案：C

問題 #22
Universal Containers (UC) creates a dataset, "Book11", containing a budget per region per month for the first
6 months of the year, as shown in the graphic below. Now, UC wants to create a lens showing the total budget for each region for each month. Every combination of region and month must be shown in the lens, even if there is no data.

How should a CRM Analytics consultant help UC build this lens?

A. Use a "Compare Table" and add a column leveraging the "Running Total" function.
B. Use a "Compare Table" and use the "Show Totals" option.
C. Use a "fill" statement in SAQL query with a "partition" parameter.

答案：A

問題 #23
The below image shows a numeric outcome being deployed (Regression).

Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?

A. Area Under Curve, Confusion Matrix
B. Area Under Curve, R2 (R-squared)
C. Root Mean Square Error, Minimum Square Error

答案：C

解題說明：
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.

問題 #24
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