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Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Syllabus Topics:

Topic	Details
Topic 1	Data Layer: In this comprehensive section, Salesforce consultants delve into the heart of data extraction and loading. It's all about showcasing a deep understanding of implementing refreshes for data syncs, performing data transformations, and implementing delivery management strategies in dataflows.
Topic 2	Einstein Discovery: This section unveils the magic of AI-driven insights and candidates' ability to analyze and choose one of the three types of predictions. It involves leveraging Einstein's advanced analytics capabilities to adjust data parameters, add or remove data and columns for the improvement of the model.
Topic 3	Admin Configuration: This topic takes Salesforce consultants on a journey through the enablement of CRM Analytics. It tests their ability to design a solution that is suitable for data sync dataflows recipes limits.

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q32-Q37):

NEW QUESTION # 32
What kind of insight is "What Is The Difference"?

A. Descriptive
B. Prescriptive
C. Diagnostic
D. Selective
E. Predictive

Answer: C

Explanation:
https://trailhead.salesforce.com/content/learn/modules/understand_einstein_discovery_stories/understand-what-is-the-difference-insights

NEW QUESTION # 33
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 . /analytics/projects
D. /services/data/v . /wave/apps

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 # 34
Universal Containers' CRM Analytics team is building a dashboard with two widgets.
1. List widget associated to the query "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Donut chart widget associated to the query "Step_pie_3" and grouped by the dimension "Type" The team wants to use bindings/interactions so any selection in the List widget will filter the Donut chart. The queries use different datasets, and users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding/interaction?

Answer: A

Explanation:
For the given requirement where a selection in a list widget needs to filter data displayed on a donut chart, and considering the list allows for multi-selection of the 'Type', the correct binding/interaction would be to use a syntax that captures the multi-select aspect and passes it appropriately. The right syntax, as indicated in Option A, looks like this:
"{{column(Type_2.selection, ["Type"]).asObject()}}"
This syntax ensures:
* Multi-selection: The column() function in combination with .asObject() ensures that multiple selected values from the 'Type_2' query can be passed as an object, which the donut chart can utilize to filter its content.
* Correct Data Type Handling: By using .asObject(), the binding ensures the data passed between widgets maintains the correct structure expected by the CRM Analytics dashboard, thereby ensuring accurate filtering.

NEW QUESTION # 35
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. Remove the Store field from the model definition, because that is the recommended action.
C. Ignore alert; the explanation of variation is only 35%, which is below 50%,

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 # 36
Universal Containers uses CRM Analytics to build dashboards for different departments: Sales, Service, and Marketing. Users in 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 should a CRM Analytics consultant address this need?

A. Create one app for each department, put common datasets in the shared app, and use profiles to share apps.
B. Create one app for each department, put common datasets in the shared app, and use permission sets to share apps.
C. Create one app for each department, put common datasets in the shared app, and use roles to share apps.

Answer: B

Explanation:
For managing access to department-specific dashboards while leveraging common datasets, the best approach involves the use of apps and permission sets. Here's why:
App Segregation: Creating a separate app for each department (Sales, Service, Marketing) allows for tailored dashboards and datasets to be grouped by department, facilitating easier management and navigation.
Shared Common Datasets: Placing common datasets in a shared app ensures that all departments can access necessary data without duplication, maintaining consistency and reducing storage requirements.
Use of Permission Sets: Leveraging permission sets to control access to these apps is a flexible and scalable approach. Permission sets can be finely tuned to grant or restrict access based on user roles within the organization, and they can be easily adjusted as roles or organizational structures change.
This structure not only ensures data security and appropriate access but also enhances the efficiency of managing CRM Analytics resources across different departments.

NEW QUESTION # 37
......

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Newest Salesforce Reliable Dumps Files–the Best Accurate Latest CRM-Analytics-and-Einstein-Discovery-Consultant Study Materials

Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Syllabus Topics:

Latest Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Study Materials - Pdf Demo CRM-Analytics-and-Einstein-Discovery-Consultant Download

Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q32-Q37):

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