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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Tableau CRM Dashboard Implementation | 18% | - Lens and dashboard building - Embedded analytics - SAQL and query logic |
| Einstein Discovery Story Design | 19% | - Data preparation for modeling - Interpreting insights and predictions - Deploying results to Salesforce - Model creation and evaluation |
| Tableau CRM Dashboard Design | 19% | - Mobile optimization - Interactive design and bindings - Visualization best practices |
| Administration | 9% | - Performance optimization - Deployment and change management - Platform configuration and provisioning |
| Data Layer | 24% | - Dataset management and transformation - Dataflows and recipes - Schema design and optimization - Data ingestion and synchronization |
| Security | 11% | - Data encryption and governance - Row-level security and sharing - User permissions and access control |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
Question 1
An Einstein consultant created a dashboard that went through review. Some few charts were added and removed in the process. The dashboard is now approved for production.
Which best practice should be done before releasing the dashboard?
A. Rename all the steps to correspond with the defined naming convention.
B. Remove the redundant steps to ensure dashboard performance.
C. Apply conditional formatting on the new charts.
D. Modify the XMD so all labels and values are user-friendly.
Question 2
A customer has a dataset consisting of over 300 unique product names. They request a prediction model with the product names included.
Which action should the Einstein Consultant take?
A. Use SKU numbers rather than product names to increase clarity.
B. Adjust the model to eliminate extreme values in the outcome variable.
C. Run the model using the default variables in the Product object
D. Split the analysis into multiple models will each having fewer products
Question 3
The Universal Containers company uses Einstein Analytics to build dashboards for different departments: Sales, Service, and Marketing. Users for the same department have the same role and need to have access to the same dashboards. Dashboards for different departments use some common datasets with the same row-level security.
How can an Einstein Consultant address this need?
A. Create one application for each department and use roles to share applications.
B. Create one application for each department, put common datasets in the shared app, and use profiles to share applications.
C. Create one application for each department, put common datasets in the shared app, and use permission sets to share applications.
D. Create one application for each department, put common datasets in the shared app, and use roles to share applications.
Question 4
A customer is reviewing a story that is set to maximize the daily sales quantity of consumer products in stores and sees this chart. The visualized tooltip belongs to the blue bar for San Francisco, reflecting, November daily sales quantities in that city specifically.
What two conclusions can be drawn from this insight?
A. November sales are higher than in other months. This November-effect is the strongest in San Francisco.
B. The average daily in SAN Francisco stores in November as 1601 items higher than the global average of 335.
C. The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all November sales in the country
D. The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all other months in San Francisco.
Question 5
A company wants to use Einstein Analytics to analyze the trend of their sales pipeline. The company wants to accelerate the sales process. To do so, they need to know the average time an opportunity spends in each stage. This value is not pre-calculated in Salesforce, so a consultant recommends using a dataflow to calculate it.
How can a dataflow be used to calculate the average time an opportunity spends in each stage?
A. computeRelative Transformation with offset parameter to calculate the duration
B. flatten Transformation with Offset Expressions to calculate the duration
C. computeExpression Transformation with SAQL Expression to calculate the duration
D. computeRelative Transformation with SAQL Expression to calculate the duration
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: B,D | Question 5 Answer: D |
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