Salesforce Certified CRM Analytics and Einstein Discovery Consultant (Analytics-Con-201) Exam Practice Test Instant Access

Question 1

A versioning feature allows CRM Analytics users to be added as Publishers and make changes separately while a 'Live' version is still being used by other users. Once the changes are complete, the user can then set their updated version as the Live version.

Which CRM Analytics item is this leveraged for?

AApp

BDataset

Cgoats

Answer : A

In CRM Analytics, the versioning feature described is typically leveraged for Apps. This feature allows:

Parallel Development: Users can work on changes in a separate version without affecting the live version being accessed by others.

Controlled Publishing: Once changes are finalized, the user can then promote their version to be the new live version, ensuring seamless updates without disrupting ongoing usage.

Collaborative Workflows: Facilitates teamwork by allowing multiple users to propose and test changes in a controlled environment before making those changes live.

This approach ensures that CRM Analytics apps remain dynamic and can evolve over time while maintaining stability and continuity for end-users.

Question 2

A system administrator and a CRM Analytics consultant are working together on deploying arecipe/dataflow and a dataset to another org. Prior to this deployment, a package was deployed with all the custom fields used in the dataflow and dataset.

While running the recipe/dataflow in the target environment, the consultant encounters multiple errors related to these custom fields.

How should this be resolved?

ACheck whether the system administrator has been provided access to these custom fields on the target org.

BCheck whether the Analytics Integration user has been provided access to these custom fields on the target org.

CCheck whether the consultant has been provided access to these custom fields on the target org.

Answer : B

Question 3

The marketing team at Cloud Kicks has five dashboards in an app. Four widgets are replicas of each other in three of the dashboards.

What is the best way to maintain these widgets?

ACreate/Edit a lens and add each dashboard.

BCreate/Edit the widgets individually on each dashboard.

CCreate/Edit a component for the widgets.

Answer : C

To maintain consistency and ease of updates across multiple dashboards, creating or editing a component for the widgets is the most effective method. This approach:

Efficiency in Updates: Allows changes to be made in one place, which automatically propagates to all instances where the component is used across dashboards.

Consistency: Ensures uniformity in the appearance and functionality of the widgets across different dashboards.

Simplicity: Reduces the need for redundant work, where each widget would otherwise need to be updated individually.

Question 4

The CRM Analytics consultant at Universal Containers has set data syncs and recipe runs back to back. However, they notice that the data syncs and recipe run jobs fail repeatedly. Upon investigation,

they realize the data syncs and recipes are tightly coupled which leads to too many runs being queued and eventually being canceled.

How should the consultant resolve this issue?

ARaise a case with Salesforce Support to help Increase the concurrency limits of the org.

BSet up failure notifications so that the CRM Analytics consultant gets notified when this happens and can fix the Issue.

CEnable priority scheduling to automatically queue shorter or smaller runs before longer or larger ones.

Answer : C

Question 5

The administrator at Cloud Kicks has been asked to sync data from an external object created in Salesforce into CRM Analytics.

What should the administrator keep in mind?

ASalesforce external objects are unsupported in RM Analytics recipes digest transformations.

BUsing a custom connector to connect to the external objects will load it into CRM Analytics.

CLoading the external object data into CRM Analytics will help join objects in the recipes.

Answer : A

Question 6

Cloud Kicks (CK) has a dashboard in CRM Analytics with forecasting dat

a. One widget is a compare table using the timeseries function showing the quarterly forecast. However, CK is interested in enhancing the dashboard with a weekly forecast per customer.

How should CK achieve this?

AAn SAQL query with a timeseries statement specifying Opportunity Name as the partition and Y-M-W as the Date Cols

BAn SAQL query with a timeseries statement specifying Account Name as the partition and Y-M-W as the Date Cols

CAn SAQL query with @ timeseries statement specifying Account Name as the partition and Y-M-D as the Date Cols

Answer : B

To create a weekly forecast per customer, an SAQL (Salesforce Analytics Query Language) query can be used. The timeseries function generates forecast data based on a specified date range. In this case, the forecast is customer-specific, meaning the partition should be based on Account Name to ensure that the forecast is generated for each customer. The date column (Date Cols) should be set to Y-M-W to aggregate the forecast data on a weekly basis.

Question 7

Universal Containers (UC) is using CRM Analytics to create two datasets.

* Dataset A: Contains a list of activities with an "activityID" dimension and a "userID" dimension

* Dataset B: Contains a list of users with a "userID" dimension

UC wants to delete all activities from Dataset A related to users in Dataset B.

How should the CRM Analytics consultant help UC achieve this?

AUse the combination of recipe transformations: 'join' and ''filter''.

BUse the recipe node 'delete' and set 'userID' as the deletion ID.

CUse the ''update'' transformation and utilize the 'filter' node.

Answer : A

In CRM Analytics, when dealing with two datasets, such as Dataset A (activities) and Dataset B (users), and you want to delete records from Dataset A based on the users listed in Dataset B, you would typically use a combination of a join and filter transformation in a recipe. The join transformation allows you to combine data from both datasets based on the shared userID dimension, and the filter transformation would then be used to delete or exclude any activities from Dataset A that are associated with the users from Dataset B. This approach ensures that only relevant activities remain in Dataset A after filtering out the unwanted ones. Reference: CRM Analytics Recipes: Join and Filter Transformations