Prepare with Salesforce Analytics-Con-201 exam torrent, pass for sure
Updated: Jul 24, 2026
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Salesforce Analytics-Con-201 Exam Overview:
| Certification Vendor: | Salesforce |
| Exam Name: | Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam |
| Exam Number: | Analytics-Con-201 |
| Exam Format: | Multiple Choice, Multiple Select, Scenario-based Questions |
| Exam Duration: | 105 minutes |
| Real Exam Qty: | 60 |
| Certificate Validity Period: | Maintained via Salesforce certification maintenance (Trailhead modules, annual upkeep) |
| Related Certifications: | Salesforce Certified Administrator Salesforce Certified Platform App Builder Salesforce Certified Data Analyst |
| Available Languages: | English |
| Recommended Training: | Salesforce Trailhead CRM Analytics Modules Einstein Discovery Learning Resources |
| Exam Registration: | Pearson VUE Salesforce Exams Salesforce Certification Registration |
| Sample Questions: | Salesforce Analytics-Con-201 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE or authorized testing centers |
| Pre Condition: | No strict prerequisites required, but recommended experience with Salesforce CRM Analytics (Tableau CRM), data modeling, and basic Salesforce platform knowledge |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials |
Salesforce Analytics-Con-201 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Integration and Connectivity | - Data synchronization and refresh strategies - Connecting external data sources |
| Dashboard and App Development | - Lens creation and dashboard design - Interactive filters and user experience optimization |
| Einstein Discovery | - Insights interpretation and recommendations - Model building and predictions |
| Security and Access | - Row-level security and sharing rules - App and dataset permissions |
| Data Management and Preparation | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions:
1. A CRM Analytics consultant has been asked to add a custom object to existing recipe. When trying to locate the object, the consultant can see only Direct Data and NOT the SFDC Local data sync.
How should the consultant resolve this?
A) Turn on data syne for the object, run the sync, and then add to recipe.
B) Create a new data sync connection, run the sync, and add to recipe.
C) Clone the recipe, add the new object to the recipe, and run the recipe.
2. A CRM Analytics consultant is adding direct data into an existing recipe. When trying to click on the input node, data preview is taking a long time to show any results.
How should the consultant resolve this?
A) Update the Sample Size under Data Preview Sampling to 5,000 rows.
B) Register the Salesforce object and use Filtered Rows under Data Preview Sampling.
C) Use the Filtered Rows sampling mode under Data Preview Sampling to filter records.
3. Exhibit.
Given that the queries are using different datasets, which change should a CRM Analytics consultant make to solve this issue?
A) Use "Connect Data Sources" and create a connection to connect the two datasets.
B) Use result binding/Interaction in the filters section of the query "Type 1".
C) Use "Connect Data Sources" and create a connection to connect the two widgets.
4. The CRM Analytics consultant at AW Computing is designing dashboard. They want to add a rate between two metrics and display it in a new chart. They are finding that the new chart seems to prejudice the design of other charts.
What should the consultant do to resolve this?
A) Create a component with the rate chart and insert it in one of the tooltips.
B) Clone the dashboard and replace one of the charts with the new rate chart.
C) Adjust the dashboard widgets to accommodate the rate chart.
5. After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?
A) The dataset contains multiple dominant values.
B) The model contains too many outlier values.
C) The outcome variable may be causing data leakage.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |
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