Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Practice Exam - 117 Unique Questions
Latest Questions CRM-Analytics-and-Einstein-Discovery-Consultant Guide to Prepare Free Practice Tests
NEW QUESTION # 14
Before using bindings, you can try using facets to specify interactions between widgets.
- A. True
- B. False
Answer: A
NEW QUESTION # 15
After loading data to Einstein Discovery and creating a story, the client asks the Einstein Consultant to explain the "Unexplained Bar" in the "Why it Happened" chart.
Which explanation is correct?
- A. It should always be 0 or the model should not be used.
- B. It displays the difference between the analyzed data and the data not reviewed.
- C. It only appears for outcomes that do not have an explanation.
- D. It shows the difference between the predicted outcome and the observed outcome.
Answer: D
NEW QUESTION # 16
A consultant built an Einstein Analytics app for the Sales Operations team. The Sales Operations team wants to share their app with other people at the company. The consultant recommends distributing the app as an Einstein Analytics template app.
What can the consultant do to give the Sales team more choices and options with future apps that are generated from the Sales Operations app'
- A. Create a configuration wizard for the app.
- B. Update contents in the Sales Operations app and changes will be pushed down to its generated apps.
- C. Update contents in the Sales Operations app and create new template versions of the app.
- D. Ensure the Sales team has the necessary permissions to customize their apps.
Answer: A
Explanation:
https://trailhead.salesforce.com/en/content/learn/modules/wave_analytics_templates_intro/wave_analytics_templates_in_action
NEW QUESTION # 17
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?
- A. The columnheaderis prefixed with "X" upon upload.
- B. The column header Is set to *&**(&*(%.
- C. The column header is auto-updated to "Column" + column number.
Answer: A
Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as "&**(&(%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
NEW QUESTION # 18
CRM Analytics users at Cloud Kicks are granted access to an app with specific dashboards. When trying to download a specific widget, they are unable to do so.
- A. The users have access to the dashboard but not the dataset.
- B. The permission set for the users is missing the download data permission.
- C. The dashboard has been created for internal use and theusers have a view only license.
Answer: B
NEW QUESTION # 19
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. Ignore alert; the explanation of variation is only 35%, which is below 50%,
- B. Remove the Store field from the model definition, because that is the recommended action.
- C. Verify client expectations that Store is a strong predictor for daily sales quantity.
Answer: C
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 # 20
Which of these is not a method for controlling record-level access?
- A. Role Hierarchy
- B. Profiles
- C. Organization-Wide Defaults
- D. Sharing Rules
Answer: B
Explanation:
Reference:
https://help.salesforce.com/articleView?id=bi_security_datasets_row_level.htm
https://help.salesforce.com/articleView?id=managing_the_sharing_model.htm&type=5
NEW QUESTION # 21
An Einstein Discovery team created a model to maximize the margin of their sales opportunities. They want to deploy the model to the Opportunity object in order to predict the outcome of every newly created or updated Opportunity.
What are the steps to accomplish this?
- A. Create a trigger on Opportunity and use the Salesforce External Connector to get predictions from Einstein Discovery.
- B. Create an Apex batch on Opportunity and use the REST API to get predictions from Einstein Discovery.
- C. Create a trigger on Opportunity and install the Einstein Discovery Writeback managed package from the AppExchange.
- D. Create a trigger on Opportunity and use the REST API to get predictions from Einstein Discovery.
Answer: C
Explanation:
https://help.salesforce.com/articleView?id=bi_edd_wb_native.htm&type=5
NEW QUESTION # 22
The Universal Containers Einstein Analytics team built a dashboard with two widgets:
1. List widget associated to the step "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Pie chart widget associated to the step "Step_pie_3" and grouped by the dimension "Type" The team wants to use bindings so any selection in the List widget will filter the Pie chart.
Additional notes:
* The steps use different datasets.
* Users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding?
- A.
- B.
- C.
- D.
Answer: A
NEW QUESTION # 23
Yasmine told Danielle she's interested in using the artificial intelligence provided by Einstein Discovery to enhance analysis Mosaic probably wouldn't have considered through their own means. How can Danielle use Einstein Analytics to load data into Einstein Discovery?
- A. Data loaded into Einstein Analytics as datasets can then be used in Einstein Discovery without a separate load process.
- B. It's not possible. Danielle will need to log into Einstein Discovery and import the data from there.
- C. Danielle needs a permission set containing the Upload External Data to Analytics permission, then she can log into Einstein Discovery and import the data from there.
- D. Danielle needs a permission set containing the Download Analytics Data permission, then she can use the Export node to load data into Einstein Discovery.
Answer: A
NEW QUESTION # 24
A consultant sets up a Sales Analytics templated app that is very useful for sales operations at Universal Containers (UC). UC wants to make sure all of the data assets associated with the app, including:
recipes, dataflows, connectors, Einstein Discovery models, and prediction definitions are refreshedeveryday at 6:00 AM EST.
How should the consultant proceed?
- A. Use the App Install History under Analytics Settings and schedule the app to run at 6:00 AM EST.
- B. Use the Data Manager and schedule each item to run at 6:00 AM EST based on 'Time-based Scheduling'.
- C. Use the Data Manager and schedule the recipes/dataflows to run at 6:00 AM EST based on 'Time-based Scheduling'.
Answer: A
NEW QUESTION # 25
Einstein Discovery is a tool that:
- A. Understands your business better than you do
- B. Helps you hire the best data scientist for your business
- C. Replaces your team of BI experts and data analysts
- D. Is like having a personal data scientist on staff
Answer: D
NEW QUESTION # 26
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. 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' == "$User.Id".
- B. In the dataflow, extract the AccountTeamMember object and augment it with the Opportunity object using 'Accountld' as the join field and apply following security predicate: 'AccountTeamMember.Userld' == "$User.Id".
- C. Apply sharing inheritance.
- D. Create a master-detail relationship between the Salesforce Account and Opportunity objects.
Answer: B,C
NEW QUESTION # 27
What are predictive insights good for?
- A. Predicting outcomes that you don't actually have the right data for
- B. Exploring your existing data to see what already happened
- C. Choosing between all possible outcomes for a single variable
- D. Drilling down into the underlying reasons behind a prediction
Answer: D
NEW QUESTION # 28
The Einstein Analytics team at a company created a clataset based on the Opportunity__c custom object. The VP of Sales reports seeing the message "No results found" when opening the dataset to explore it. Other users below the VP in the role hierarchy can see rows on the same dataset. Which two problems might be causing this issue?
- A. The Salesforce profile for the VP does not have read permission on some fields of the Opportunity__c custom object
- B. The Security Predicates set up at the dataset level are preventing the VP from seeing data
- C. The dataset is inheriting sharing from Salesforce and the VP can see more than 3000 rows
- D. The Salesforce profile for the VP does not have read permission on the Opportunity__c custom object
Answer: C,D
NEW QUESTION # 29
Universal Containers has a well-defined role hierarchy in Salesforce where everyone is assigned to an appropriate node. The accounts within their instance are categorized by their demography.
An individual sales rep should be able to view all accounts that they own. In addition, sales reps should be able to see any accounts where the value of the account demography matches the demography defined on their user record. A user could have more than one demography defined on their user record.
To meet this requirement, the CRM Analytics consultant has set up a security predicate of the existing
'Account' dataset as follows:
This, however, does not seem to be working as expected.
What is causing the issue?
- A. The Analytics Security User is not provided access permission on custom field Demographic_c on the User object.
- B. The security predicate needs to be updated as 'Ownerld' == "sUser.id" || 'Demography' = "$User.
Demographic__c'. - C. The Sales Rep is not provided access permission on custom field Demographic__c on the User object.
Answer: C
Explanation:
The issue with the security predicate not functioning as expected likely stems from a permissions issue related to the custom field Demographic__c on the User object. Here's a detailed explanation:
* Field-Level Security: If the sales reps do not have access to the Demographic__c field, the security predicate which references this field cannot execute properly as the system cannot evaluate the predicate without accessing the field.
* Permission Settings: Ensuring that the sales reps have the necessary permissions to view and use the Demographic__c field is crucial for the security predicate to function correctly.
* Data Visibility: The security model in CRM Analytics relies heavily on the underlying data permissions in Salesforce. If these permissions are not correctly configured, the expected data visibility through CRM Analytics will not be achieved.
NEW QUESTION # 30
A consultant built a very useful Einstein Analytics app for Sales Operations, and they want to share its contents with the rest of Global Sales. However, they do not want to add everyone in Sales to their app. The consultant recommends extending the Sales Operations app and distributing it as an Einstein Analytics template app, but needs to locate specific information to get started.
Given the code statement above, which endpoint should it be posted to?
- A. /services/data/v . /analytics/wizard
- B. /services/data/v . /wave/templates
- C. /services/data/v . /wave/apps
- D. /services/data/v . /analytics/projects
Answer: B
Explanation:
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_rest.meta/bi_dev_guide_rest/bi_resources_templates.htm
NEW QUESTION # 31
Trending data limits: Maximum number of trended datasets per user and rows per snapshot:
- A. 10 per user, 100,000 per snapshot
- B. 5 per user, 200,000 per snapshot
- C. 10 per user, 200,000 per snapshot
- D. 5 per user, 100,000 per snapshot
Answer: D
NEW QUESTION # 32
Universal Containers asks a CRM Analytics consultant to review the performance of its local data sync.
After removing unused objects and fields from connected data, what else should the consultant do to improve performance of the data sync?
- A. Evaluate connection mode for each connected object.
- B. Enable fast sync in analytics settings.
- C. Contact Salesforce Support to increase sync speed.
Answer: A
Explanation:
To improve the performance of local data sync in Universal Containers, evaluating the connection mode for each connected object is a practical approach. Here's the rationale:
Optimization of Resources: Different connection modes (e.g., Full Sync, Incremental Sync) use different amounts of resources. Choosing the right mode for each object based on how frequently its data changes can optimize the sync process and reduce load times.
Efficient Data Handling: By tailoring the connection mode to the needs of specific data objects, the overall efficiency of the data sync process is improved, leading to faster refresh rates and more timely data availability.
Cost and Performance Balance: Evaluating and selecting the appropriate connection mode can also help balance performance needs with cost constraints, as some modes may consume more compute resources than others.
NEW QUESTION # 33
Which chart type is suitable for rendering five measures in a lens visualization?
- A. Scatter chart
- B. Metric Radar chart
- C. Stacked Bar chart
- D. Treemap chart
Answer: C
NEW QUESTION # 34
What are the two main parts of a lens/exploration?
- A. Grouping
- B. Dataset
- C. Visualization
- D. Query
- E. Measure
Answer: A,C
NEW QUESTION # 35
After getting approval for the dashboard layout design for a desktop, the CRM Analytics consultant is ready to start the design process for a mobile layout.
Which consideration should the consultant keep in mind?
- A. "Tablet" or "Phone" layout-where only minWidth and maxWidth have been set-may be displayed on a desktop if the dashboard is embedded in a small frame, or if the browser window is small.
- B. Create a layout with the property "phone" to show the dashboard on the mobile app similar to creating a layout with the property "dashboard" to show on the desktop for the same dashboard,
- C. If no layouts are eligible for the mobile device, an error message will be displayed but the dashboard will still be visible on the desktop without errors.
Answer: A
Explanation:
When designing for different device types in CRM Analytics, particularly for mobile layouts, it's crucial to consider how the layout will respond not just on mobile devices but also under various display conditions on desktops. Here's the rationale for focusing on this consideration:
Responsiveness: Layouts designated for tablets or phones may also be triggered on desktop environments if conditions such as browser window size or embedded frame dimensions mimic those typical of smaller devices.
Design Flexibility: Understanding this behavior is essential for creating versatile dashboards that maintain functionality and visual integrity across all potential viewing scenarios.
User Experience: Ensuring that the dashboard behaves predictably across device types and sizes enhances user engagement and effectiveness, as it provides a consistent experience regardless of the access point.
This approach ensures that the dashboard remains functional and accessible no matter how or where it is being viewed, aligning with best practices for responsive and adaptive design in modern analytics environments.
NEW QUESTION # 36
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