PDF (New 2025) Actual Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Questions
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Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Syllabus Topics:
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NEW QUESTION # 13
What can you do in a dataset recipe?
- A. Build a dashboard
- B. Combine data from different datasets
- C. Extract external data into Analytics
- D. Make a data chili
Answer: B
NEW QUESTION # 14
When deploying a model to an existing Prediction, w number should be considered"1
- A. 3: Limit of total number of Models.
- B. 5: Limit of total number of Prediction Definitions.
- C. 10: Limit of number of models under a Prediction Definition.
- D. 20: Limit of number of models under a Prediction Definition.
Answer: C
NEW QUESTION # 15
Einstein Discovery gives you several different kinds of recommended insights. Which of these insights is descriptive?
- A. What Is the Difference
- B. Predictions & Improvements
- C. Why It Happened
- D. What Happened
Answer: D
Explanation:
https://trailhead.salesforce.com/en/content/learn/modules/understand_einstein_discovery_stories/use_stories
NEW QUESTION # 16
In a Compare Table formula, you can refer to other columns with:
- A. Numbers (1..9)
- B. Their names
- C. All of the above
- D. Letters (A..Z)
Answer: D
NEW QUESTION # 17
A company wants to allow users who belong to an account team to see all the Opportunities associated with that Account in Einstein Analytics.
Which two actions accomplish this requirement? Choose 2 answers
- A. Apply sharing inheritance.
- B. In the dataflow, extract the OpportunityTeamMember object and augment it with the Opportunity object using 'Opportunityld' as the join field and apply the following security predicate: 'OpportunityTeamMember.Userld' == "$User.Id".
- C. Create a master-detail relationship between the Salesforce Account and Opportunity objects.
- D. In the dataflow, extract the AccountTeamMember object and augment it with the Opportunity object using 'Accountld' as the join field and apply following security predicate: 'AccountTeamMember.Userld' == "$User.Id".
Answer: A,D
NEW QUESTION # 18
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. Verify client expectations that Store is a strong predictor for daily sales quantity.
- B. Ignore alert; the explanation of variation is only 35%, which is below 50%,
- C. Remove the Store field from the model definition, because that is the recommended action.
Answer: A
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 # 19
A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.
What should the consultant do?
- A. Drop the field as it will be difficult to get future respondents.
- B. Fill in the missing data with an average of all incomes.
- C. Apply the predict missing values transformation in recipe nodes.
Answer: C
Explanation:
In CRM Analytics, when dealing with incomplete data, specifically when certain respondents have not filled out fields like income, the Predict Missing Values transformation in a recipe is highly effective. This transformation allows you to predict values for missing fields based on patterns from the existing data. Since the consultant finds this field informative despite having data from only 1% of respondents, applying this transformation can estimate these missing values, which ensures that the dataset remains useful for predictive purposes without discarding important variables.
Reference: CRM Analytics Recipes and Predict Missing Values
NEW QUESTION # 20
A consultant is creating a Churn Prediction model to identify customers who are not likely to renew their contract.
What is the appropriate action to take?
- A. Replace nulls in the Churn Reason filed as 'No reason given'',
- B. Exclude the Churn reason field from the dataset.
- C. For the Churn Reason field, enable the ''This variable contains sensitive data'' box because it may contain sensitive customer behavior.
- D. Exclude active customers (customer who have not churned) from the training dataset since their Churned) from the training dataset since their Churn Reason field are nulls.
Answer: C
NEW QUESTION # 21
A consultant has created a story to maximize the daily sales quantity of consumer products in stores. After creating a story, the consulting is presented with this data alert by Einstein Discovery (see graphic).
What are two appropriate actions to take?
Choose 2 answers
- A. Manually remove the sales bellow 0 (negative sales must be a data issue), but keep the large value (the more data, the better the model will be).
- B. Discuss with the client if values below 0 and above 2,489 are so uncommon that they should perhaps be left out of the story.
- C. Remove the outliers as suggested by Einstein and deploy the model.
- D. Remove the outliers as suggested by Einstein, and verify using model metrics and story insights if the quality improved.
Answer: B,C
NEW QUESTION # 22
After getting approval of the dashboard layout design for a desktop, the Einstein Analytics consultant is ready to start the design process for a mobile layout.
What are three considerations that the consultant should keep in mind when developing the layout? Choose 3 answers
- A. A layout for mobile is eligible for use when the device meets all the device properties set in the Layout panel.
- B. If no layouts are eligible for the mobile device, an error message will be displayed.
- C. If more than one layout is eligible, the one with the most device properties set is used. If there is a tie, the most recently defined layout is used. (Missed)
- D. There are widgets that cannot be displayed on mobile layouts.
- E. If no layouts are eligible for the mobile device, the first defined layout is used. (Missed)
Answer: A,C,E
Explanation:
https://trailhead.salesforce.com/en/content/learn/modules/wave_dashboard_designer_basics/wave_take_analytics_on_the_go
NEW QUESTION # 23
A company asks a Tableau CRM consultant to review the performance of their local data sync.
After removing unused objects and fields from connected
data, what else can the consultant do to improve performance of data sync"*
- A. Merge synced objects
- B. Enable fast sync in analytics settings
- C. Evaluate connection mode for each connected object
- D. Contact Salesforce support to Increase sync speed
Answer: C
NEW QUESTION # 24
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. Create a new data sync connection, run the sync, and add to recipe.
- B. Turn on data syne for the object, run the sync, and then add to recipe.
- C. Clone the recipe, add the new object to the recipe, and run the recipe.
Answer: B
NEW QUESTION # 25
Cloud Kicks has a dashboard that displays accounts and opportunities data in a table that contains actions to open the records in Salesforce. Since the company has allowed several accounts to be created with the same names, when users try to perform actions, they are prompted with only a record ID to select, leaving the users confused and unable to act.
How should the CRM Analytics consultant solve this problem?
- A. Discuss with the stakeholders If the action Is really necessary for the table.
- B. Ask the admin to start applying duplicate rules based on the account's Name field.
- C. Insert more fields in the dataset action "Display Fields" so the users know which account they are acting on.
Answer: C
NEW QUESTION # 26
The client is trying to create a SAQL step to predict sales in each sales region. They cannot get the query to return any results, but have identified that the error is in the time series statement. They have asked an Einstein Consultant to review the following query and fix any errors.
Which timeseries statement will fix the query'
- A. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);
- B. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');
- C. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');
- D. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);
Answer: B
NEW QUESTION # 27
A consultant wants to optimize data loads by extracting Salesforce objects using independent Einstein Analytics dataflows ahead of time. Which construct should be used to accomplish this?
- A. Data Sync (Replication)
- B. Clone
- C. Augment
- D. Dataflow
Answer: C
NEW QUESTION # 28
Where can you view the status of a CSV upload in Analytics?
- A. The Create Dataset page
- B. The data manager
- C. The Analytics home page
- D. The Setup menu
Answer: B
NEW QUESTION # 29
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Updated May-2025 Pass CRM-Analytics-and-Einstein-Discovery-Consultant Exam - Real Practice Test Questions: https://www.actualcollection.com/CRM-Analytics-and-Einstein-Discovery-Consultant-exam-questions.html