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

Question 1

Universal Containers plans to upload target data from an external tool to CRM Analytics so it can calculate the sales team target attainments.

The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales rep, the target amount, and the month of the target. For each sales rep, the file contains a target for every month of the current year as well as all previous years.

Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?

AOverwrite

BUpdate

CAppend

Answer : A

For uploading target data that changes on a monthly basis and includes historical data (previous years' targets), the appropriate operation is 'Overwrite.' This ensures that each time the CSV file is uploaded, the existing data in the dataset is replaced with the new data. This is critical because the target data includes both current and historical data, and using 'Overwrite' will update the entire dataset while maintaining historical accuracy.

'Append' would add new data without replacing the old records, leading to duplication, and 'Update' is not suitable for completely replacing data in this context.

Question 2

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?

AThe column header is prefixed with 'X' upon upload.

BThe column header is auto-updated to ''Column'' + column number.

CThe column header Is set to *&**(&*(%.

Answer : A

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.

Question 3

A project team member uploads a CSV file to CRM Analytics, and they notice a few records failed during the upload. The manager wants to view the error log generated so this can be fixed and uploaded again. The manager has the CRM Analytics administrator permission but is unable to download the error log details.

Why is the manager unable to download the log details?

AThey do not have the Upload External Data to CRM Analytics permission enabled.

BThey do not have the Download CRM Analytics Data permission enabled.

COnly the user who uploaded the external data file can download the error log.

Answer : C

In CRM Analytics, when a CSV file is uploaded and errors occur during the upload process, an error log is generated. However, only the user who uploaded the external data file can download the error log, even if other users have administrative permissions. This restriction ensures that only the user responsible for the data upload can access the details to resolve the issues.

Question 4

What is the purpose of the CRM Analytics Dashboard Inspector?

ATo view the total time required to run all queries.

BTo automatically remove bottlenecks to make queries run faster.

CTo see the final query for each widget along with the results.

Answer : C

The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard.

While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.

Question 5

A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits.

How can the developers add new fields while keeping the dataset refreshed?

AA recipe for the new fields and when that is successful, add it to the existing recipe as a join node.

BClone the existing recipe to add fields and roll back the original recipe to the last working version.

CRefresh the dataset after working hours to avoid the edited version from failing.

Answer : B

When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:

Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.

Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.

This approach not only ensures data continuity but also provides a safe environment to address any issues that may arise from new developments.

Question 6

A CRM Analytics consultant is updating an existing recipe.

They are looking to add a few additional fields onto the Account dataset. One of the fields to be added is a multi-select picklist field that needs to be shown as text on the dashboard.

What should the consultant do to accomplish this?

AUse the array_join multivalue runcuen co convert this field to a text prior to registering the dataset.

BUse the array multivalue function to convert this field to a text prior to registering the dataset.

CUse the string multivalue function to convert this field to a text prior to registering the dataset.

Answer : A

Question 7

A CRM Analytics consultant has been asked to bring data from an external database as well as five external Salesforce environments into CRM Analytics. Twenty-five objects have been enabled from the local Salesforce connector.

The requirements are:

* 10 objects should be enabled from an external database

* 12 objects each from three of the external Salesforce environments

* 15 objects each from the remaining two external Salesforce environments

The consultant estimates each connector will, per object, bring between 1,000 and 1 million rows of data.

Which limit will be exceeded?

ATotal number of enabled objects

BSalesforce external connector number of synced rows

CStorage rows of data

Answer : A

In evaluating the scenario presented where multiple external sources and objects are being integrated into CRM Analytics, we need to consider the total number of enabled objects across all connections. Here's a breakdown:

10 objects from an external database

12 objects each from three external Salesforce environments, totaling 36 objects

15 objects each from two external Salesforce environments, totaling 30 objects

25 objects already enabled from the local Salesforce connector

This brings us to a total of 101 objects enabled, which may exceed typical limits on the number of objects that can be enabled in a CRM Analytics environment, depending on the specific Salesforce licensing and platform limits.