Data-Cloud-Consultant Salesforce Certified Data Cloud Consultant (Data-Con-101) Free Practice Exam Questions (2025 Updated)

When setting up Data Cloud for a multi-brand organization, if a consultant cannot map an object for one of the brands during data stream setup, they should navigate to the Data Space tab and select the object to include it in the new data space. Here’s why:

Understanding the Issue

The consultant is using data spaces to segregate data for different brands.

While mapping a data stream, they notice that an object is unavailable for one of the brands.

This indicates that the object has not been associated with the new data space.

Why Navigate to the Data Space Tab?

Data Spaces and Object Availability :

Objects must be explicitly added to a data space before they can be used in mappings or transformations within that space.

If an object is missing, it means it has not been included in the data space configuration.

Solution Approach :

By navigating to the Data Space tab , the consultant can add the required object to the new data space.

This ensures the object becomes available for mapping and use in the data stream.

Steps to Resolve the Issue

Step 1: Navigate to the Data Space Tab

Go to Data Cloud > Data Spaces and locate the new data space for the brand.

Step 2: Add the Missing Object

Select the data space and click on Edit .

Add the required object (e.g., a Data Model Object or Data Lake Object) to the data space.

Step 3: Save and Verify

Save the changes and return to the data stream setup.

Verify that the object is now available for mapping.

Step 4: Complete the Mapping

Proceed with mapping the object in the data stream.

Why Not Other Options?

A. Create a new data stream and map the second data stream to the data space :Creating a new data stream is unnecessary if the issue is simply object availability in the data space.

B. Copy data from the default data space to a new DMO using the Data Copy feature and link this DMO to the new data space :This is overly complex and not required if the object can simply be added to the data space.

C. Create a batch transform to split data between different data spaces :Batch transforms are used for data processing, not for resolving object availability issues.

Conclusion

The correct solution is to navigate to the Data Space tab and select the object to include it in the new data space . This ensures the object is available for mapping and resolves the issue efficiently.

 To ensure that freshly imported data from an Amazon S3 Bucket is ready and available to use for any segment, the following processes should be run in this order:

Refresh Data Stream: This process updates the data lake objects in Data Cloud with the latest data from the source system. It can be configured to run automatically or manually, depending on the data stream settings1. Refreshing the data stream ensures that Data Cloud has the most recent and accurate data from the Amazon S3 Bucket.

Identity Resolution: This process creates unified individual profiles by matching and consolidating source profiles from different data streams based on the identity resolution ruleset. It runs daily by default, but can be triggered manually as well2. Identity resolution ensures that Data Cloud has a single view of each customer across different data sources.

Calculated Insight: This process performs calculations on data lake objects or CRM data and returns a result as a new data object. It can be used to create metrics or measures for segmentation or analysis purposes3. Calculated insights ensure that Data Cloud has the derived data that can be used for personalization or activation.

1: Configure Data Stream Refresh and Frequency - Salesforce

2: Identity Resolution Ruleset Processing Results - Salesforce

3: Calculated Insights - Salesforce

The correct answer is B, Unified Individual. A Unified Individual is a record that represents a customer across different data sources, created by applying identity resolution rulesets. Identity resolution rulesets are sets of match and reconciliation rules that define how to link and merge data from different sources based on common attributes. Data Cloud uses identity resolution rulesets to resolve data across multiple data sources and helps you create one record for each customer, regardless of where the data came from1. A retail customer who wants to bring customer data from different sources and use identity resolution for segmentation should segment on the Unified Individual entity, which contains the resolved and consolidated customer data. The other options are incorrect because they do not represent the resolved customer data across different sources. A Subscriber is a record that represents a customer who has opted in to receive marketing communications. A Unified Contact is a record that represents a customer who has a relationship with a specific business unit. An Individual is a record that represents a customer’s profile data from a single data source. References:

Identity Resolution Ruleset Processing Results

Consider Data Implications for Segmentation

Prepare for your Salesforce Data Cloud Consultant Credential

AI-based Identity Resolution: Linking Diverse Customer Data

The correct answer is A. Deactivate the segment. If a segment is no longer needed, it can be deactivated through Data Cloud and applies to all chosen targets. A deactivated segment no longer publishes, but it can be reactivated at any time1. This option allows the user to pause a segment activation with the intent of using that segment again.

The other options are incorrect for the following reasons:

B. Delete the segment. This option permanently removes the segment from Data Cloud and cannot be undone2. This option does not allow the user to use the segment again.

C. Skip the activation. This option skips the current activation cycle for the segment, but does not affect the future activation cycles3. This option does not pause the segment activation indefinitely.

D. Stop the publish schedule. This option stops the segment from publishing to the chosen targets, but does not deactivate the segment4. This option does not pause the segment activation completely.

1: Deactivated Segment article on Salesforce Help

2: Delete a Segment article on Salesforce Help

3: Skip an Activation article on Salesforce Help

4: Stop a Publish Schedule article on Salesforce Help

The Source Sequence reconciliation rule sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name. This rule allows you to define which data source should be used as the primary source of truth for each attribute, and which data sources should be used as fallbacks in case the primary source is missing or invalid. For example, you can set the Source Sequence rule to use data from Salesforce CRM as the first priority, data from Marketing Cloud as the second priority, and data from Google Analytics as the third priority for the first name attribute. This way, the unified profile will use the first name value from Salesforce CRM if it exists, otherwise it will use the value from Marketing Cloud, and so on. This rule helps you to ensure the accuracy and consistency of the unified profile attributes across different data sources. References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution, Reconciliation Rules

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.

The B2C Commerce Order Bundle is a data bundle that creates a data stream to flow order data from a B2C Commerce instance to Data Cloud. However, this data bundle does not ingest any historical data and only ingests new orders from the time the data stream is created. Therefore, if a consultant wants to ingest historical order data, they need to use a different method, such as exporting the data from B2C Commerce and importing it to Data Cloud using a CSV file12. References:

Create a B2C Commerce Data Bundle

Data Access and Export for B2C Commerce and Commerce Marketplace

Data Cloud is a data platform that can help customers connect, prepare, harmonize, unify, query, analyze, and act on their data across various Salesforce and external sources. Some of the use cases that are considered a good fit for Data Cloud are:

To ingest and unify data from various sources to reconcile customer identity. Data Cloud can help customers bring all their data, whether streaming or batch, into Salesforce and map it to a common data model. Data Cloud can also help customers resolve identities across different channels and sources and create unified profiles of their customers.

To use harmonized data to more accurately understand the customer and business impact. Data Cloud can help customers transform and cleanse their data before using it, and enrich it with calculated insights and related attributes. Data Cloud can also help customers create segments and audiences based on their data and activate them in any channel. Data Cloud can also help customers use AI to predict customer behavior and outcomes.

The other two options are not use cases that are considered a good fit for Data Cloud. Data Cloud does not provide features to create and orchestrate cross-channel marketing messages, as this is typically handled by other Salesforce solutions such as Marketing Cloud. Data Cloud also does not eliminate the need for separate business intelligence and IT data management tools, as it is designed to work with them and complement their capabilities.

Learn How Data Cloud Works

About Salesforce Data Cloud

Discover Use Cases for the Platform

Understand Common Data Analysis Use Cases

Identity resolution is the process of linking data from different sources into a unified profile of a customer or an individual. Identity resolution uses matching rules to compare the attributes of different records and determine if they belong to the same person. Matching rules can be based on exact or fuzzy matching of various attributes, such as name, email, phone, address, or custom identifiers. A healthcare client who wants to use identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII), such as name or email, should use a matching rule criteria that is based on a unique and reliable identifier that is specific to the healthcare domain. One such identifier is the patient ID, which is a unique number assigned to each patient by a healthcare provider or system. By using the party identification on patient ID as a matching rule criteria, the healthcare client can ensure that only records that have the same patient ID are matched and unified, and avoid false positives or false negatives that may occur due to common or similar names or emails. The party identification on patient ID is also a secure and compliant way of handling sensitive healthcare data, as it does not expose or share any PII that may be subject to data protection regulations or standards. References: Configure Identity Resolution Rulesets, A framework of identity resolution: evaluating identity attributes and methods