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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Security11%- User permissions and access control
- Row-level security and sharing
- Data encryption and governance
Topic 2: Einstein Discovery Story Design19%- Data preparation for modeling
- Deploying results to Salesforce
- Interpreting insights and predictions
- Model creation and evaluation
Topic 3: Tableau CRM Dashboard Design19%- Visualization best practices
- Mobile optimization
- Interactive design and bindings
Topic 4: Tableau CRM Dashboard Implementation18%- Lens and dashboard building
- SAQL and query logic
- Embedded analytics
Topic 5: Data Layer24%- Schema design and optimization
- Data ingestion and synchronization
- Dataset management and transformation
- Dataflows and recipes
Topic 6: Administration9%- Performance optimization
- Platform configuration and provisioning
- Deployment and change management

Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:

Question 1

A consultant has been brought in to help increase the adaption of Tableau CRM. It has been identified that account managers are struggling to get an overview of their accounts before meeting with them. Ideally they would like to have key details highlighted about active cases and opportunities, opportunity value and product whitespace.
What can the consultant do to increase adoption?

A. Create datasets for account managers to explore
B. Create 4 analytics lenses and embed them onto the account record page
C. Create a dashboard and embed it onto the account record page with a filter based on the viewed account
D. Create a dashboard and embed onto the account record page


Question 2

Results from an Einstein Discovary story are reviewed with a business user. They agree with the findings but noticed that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model may not be good enough to next steps?

A. Proceed with deployment if the model quality metric values ire sufficient.
B. Identify additional data that may have a stronger relationship with the outcome variable.
C. Rerun and update the story with a different algorithm.
D. Edit the model accuracy settings and rerun the story.


Question 3

A customer is reviewing a story that is set to maximize the daily sales quantity of consumer products in stores, and the customer sees chart related to promotional activities and to San Francisco specifically.
What conclusion can be drawn from this insight?
What conclusion can be drawn from this Insight?

A. Promotions increase sales stronger in San Francisco than they do in other cities.
B. The best promotion type is Display.
C. Of all promotions types in San Francisco, sales were the highest using Display promotion.
D. The Other stores (visualized with the gray bars) show no significant data for the sales promotions.


Question 4

The model quality metrics of an Einstein Discovery story indicate that the GINI coefficient in the four folds are 0.82, 0.83, 0.84, and 0.75, respectively.
Which two actions should a consultant take? Choose 2 answers

A. Deploy the story, because the variation in the metrics is within the normal range.
B. Research and check the dataset for outliers in the target field and the main predictors that are shown on top of the story.
C. Confirm that the overall GINI coefficient is good prior to deploying the story.
D. Do not deploy the story immediately, and research why one fold is performing worse than the others.


Question 5

What is the test called that helps to identify categories that exhibit a pattern outside the overall average?

A. T-test
B. Null hypothesis test
C. Chi-square test
D. Significance level test
E. Critical region test


Solutions:

Question 1
Answer: C
Question 2
Answer: A,B
Question 3
Answer: A
Question 4
Answer: C,D
Question 5
Answer: A

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