Data-Cloud-Consultant Dumps with Practice Exam Questions Answers

The feature that the consultant should use to solution for this use case is C. Activation alert. Activation alerts are notifications that are sent to users when an activation fails or succeeds for a segment. Activation alerts can be configured in the Activation Settings page, where the consultant can specify the recipients, the frequency, and the conditions for sending the alerts. Activation alerts can help the customer to monitor the status of their activations and troubleshoot any issues that may arise. References: Salesforce Data Cloud Consultant Exam Guide, Activation Alerts

When ingesting data from an Amazon S3 bucket, the consultant should configure the following options:

The refresh mode should be set to “Upsert”, which means that new and updated records will be added or updated in Data Cloud, while existing records will be preserved. This ensures that the data is always up to date and consistent with the source.

The filename should contain a wildcard to accommodate the timestamp, which means that the file name pattern should include a variable part that matches the timestamp format. For example, if the file name is store_transactions_2023-12-18.csv, the wildcard could be store_transactions_*.csv. This ensures that the ingestion process can identify and process the correct file every day.

The other options are not necessary or relevant for this scenario:

Deletion of old files is a feature of the Amazon S3 bucket, not the Data Cloud ingestion process. Data Cloud does not delete any files from the source, nor does it require the source files to be deleted after ingestion.

Full Refresh is a refresh mode that deletes all existing records in Data Cloud and replaces them with the records from the source file. This is not suitable for this scenario, as it would result in data loss and inconsistency, especially if the source file only contains the summary of the last 24 hours of transactions. References: Ingest Data from Amazon S3, Refresh Modes

To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separate Data Lake Objects (DLOs) and map them to the Individual and Contact Point Email Data Model Objects (DMOs). Here’s why and how this works:

Understanding the Problem :

Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.

To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.

Why Two DLOs?

By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).

Mapping both DLOs to the Individual and Contact Point Email DMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.

Steps to Implement This Solution :

Step 1: Create two DLOs—one for loan applicants and another for high net worth customers.

Step 2: Map both DLOs to the Individual DMO to consolidate customer profiles.

Step 3: Map the email fields from both DLOs to the Contact Point Email DMO to enable identity resolution based on email addresses.

Step 4: Configure identity resolution rules to merge duplicate records based on shared attributes like email.

Why Not Other Options?

A. Use a data transform to consolidate the data into one DLO: Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.

C. Ingest the data into two DLOs and then map to two custom DMOs: Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.

D. Ingest the data into one DLO and then map to one custom DMO: Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.

By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.

Deleting an identity resolution ruleset has two major impacts that the consultant should communicate to the customer. First, it will permanently remove all unified customer data that was created by the ruleset, meaning that the unified profiles and their attributes will no longer be available in Data Cloud1. Second, it will eliminate dependencies on data model objects that were used by the ruleset, meaning that the data model objects can be modified or deleted without affecting the ruleset1. These impacts can have significant consequences for the customer’s data quality, segmentation, activation, and analytics, so the consultant should advise the customer to carefully consider the implications of deleting a ruleset before proceeding. The other options are incorrect because they are not impacts of deleting a ruleset. Option A is incorrect because deleting a ruleset will not remove all individual data, but only the unified customer data. The individual data from the source systems will still be available in Data Cloud1. Option D is incorrect because deleting a ruleset will not remove all source profile data, but only the unified customer data. The source profile data from the data streams will still be available in Data Cloud1. References: Delete an Identity Resolution Ruleset

The question asks which statement is true about batch ingestions from Salesforce CRM into Salesforce Data Cloud. Batch ingestion refers to the process of periodically syncing data from Salesforce CRM (e.g., Accounts, Contacts, Opportunities) into Data Cloud. The focus is on how the CRM connector handles changes in data structure (e.g., adding or removing columns) and synchronization behavior.

Why A is Correct: "When a column is added or removed, the CRM connector performs a full refresh."

Behavior of the CRM Connector :

The Salesforce CRM connector automatically detects schema changes, such as when a field (column) is added or removed in the source CRM object.

When such changes occur, the CRM connector triggers a full refresh of the data for that object. This ensures that the data model in Data Cloud aligns with the updated schema in Salesforce CRM.

Why a Full Refresh is Necessary :

A full refresh ensures that all records are re-ingested with the updated schema, avoiding inconsistencies or missing data caused by incremental updates.

Incremental updates only capture changes (e.g., new or modified records), so they cannot handle schema changes effectively.

Other Options Are Incorrect :

B. The CRM connector performs an incremental refresh when 600K or more deletion records are detected : This is incorrect because the CRM connector does not switch to incremental refresh based on the number of deletion records. It always performs incremental updates unless a schema change triggers a full refresh.

C. The CRM connector's synchronization times can be customized to up to 15-minute intervals : While synchronization schedules can be customized, the minimum interval is typically 1 hour , not 15 minutes.

D. CRM data cannot be manually refreshed and must wait for the next scheduled synchronization : This is incorrect because users can manually trigger a refresh of CRM data in Data Cloud if needed.

Steps to Understand CRM Connector Behavior

Step 1: Schema Changes Trigger Full Refresh

If a field is added or removed in Salesforce CRM, the CRM connector detects this change and initiates a full refresh of the corresponding object in Data Cloud.

Step 2: Incremental Updates for Regular Syncs

For regular synchronization, the CRM connector performs incremental updates, capturing only new or modified records since the last sync.

Step 3: Manual Refresh Option

Users can manually trigger a refresh in Data Cloud if immediate synchronization is required, bypassing the scheduled sync.

Step 4: Monitor Synchronization Logs

Use the Data Cloud Monitoring tools to track synchronization status, including full refreshes and incremental updates.

Conclusion

The statement "When a column is added or removed, the CRM connector performs a full refresh" is true. This behavior ensures that the data model in Data Cloud remains consistent with the schema in Salesforce CRM, avoiding potential data integrity issues.