Data-Cloud-Consultant Exam Dumps - Salesforce Data Cloud Questions and Answers

The SFTP Connector is a data source connector that allows Data Cloud to ingest data from an SFTP server. The customer can use the SFTP Connector to create a data stream from their exported file and bring it into Data Cloud as a data lake object. The other options are not the best ways to bring the file into Data Cloud because:

B. The Cloud Storage Connector is a data source connector that allows Data Cloud to ingest data from cloud storage services such as Amazon S3, Azure Storage, or Google Cloud Storage. The customer does not have their data in any of these services, but only on an SFTP site.

C. The Data Import Wizard is a tool that allows users to import data for many standard Salesforce objects, such as accounts, contacts, leads, solutions, and campaign members. It is not designed to import data from an SFTP site or for custom objects in Data Cloud.

D. The Dataloader is an application that allows users to insert, update, delete, or export Salesforce records. It is not designed to ingest data from an SFTP site or into Data Cloud. References: SFTP Connector - Salesforce, Create Data Streams with the SFTP Connector in Data Cloud - Salesforce, Data Import Wizard - Salesforce, Salesforce Data Loader

 Unified Profile:Creating a unified customer profile in Salesforce Data Cloud involves consolidating data from various sources.

 Reconciliation Rules:These rules determine which data source is considered the "best" when conflicting data is encountered. Changing reconciliation rules allows prioritizing specific sources.

 Source Priority:Setting source priority involves defining which data source should be preferred over others for specific attributes.

 Process:

Step 1:Access the Data Cloud settings for reconciliation rules.

Step 2:Select the Contact Point Address details.

Step 3:Change the reconciliation rules for address attributes to "Source Priority."

Step 4:Move the Order DMO to the top of the priority list. This ensures that address details from customer orders are prioritized and selected as the best data to save to the unified profile.

 Benefits:

Accuracy:Ensures the most accurate and reliable address data is used in the unified profile.

Relevance:Gives priority to the most relevant and frequently updated source (customer orders).

 References:

Salesforce Data Cloud Reconciliation Rules

Salesforce Unified Customer Profile

To validate the data on a unified profile, the consultant can use the following features:

Identity Resolution: This feature allows the consultant to view and edit the identity resolution rulesets that determine how individuals are unified from different data sources1.

Data Explorer: This feature allows the consultant to browse and filter the unified profiles and view their attributes, segments, and activities2.

Profile Explorer: This feature allows the consultant to drill down into a specific unified profile and view its details, such as source records, identity graph, calculated insights, and data actions3. References:

1: Identity Resolution in Data Cloud

2: Data Explorer in Data Cloud

3: Profile Explorer in Data Cloud

When disconnecting a data source in Salesforce Data Cloud, the system checks for active dependencies that rely on the data source. Based on Salesforce’s official documentation (Disconnect a Data Source), the error occurs if the data source has data streams or segments associated with it. Here’s the breakdown:

Key Dependencies That Block Disconnection

Data Stream (Option B):

Why It Matters:A data stream is the pipeline that ingests data from the source into Data Cloud. If an active data stream is connected to the data source, disconnecting the source will fail because the stream depends on it for ongoing data ingestion.

Resolution:Delete or pause the data stream first.

Documentation Reference:"Before disconnecting a data source, delete all data streams that are associated with it." (Salesforce Help Article)

Segment (Option C):

Why It Matters:Segments built using data from the source will reference that data source. Disconnecting the source would orphan these segments, so the system blocks the action.

Resolution:Delete or modify segments that depend on the data source.

Documentation Reference:"If there are segments that use data from the data source, you must delete those segments before disconnecting the data source." (Salesforce Help Article)

Why Other Options Are Incorrect

Activation (A):Activations send segments to external systems (e.g., Marketing Cloud) but do not directly depend on the data source itself. The dependency chain is Segment → Activation, not Data Source → Activation.

Activation Target (D):Activation targets (e.g., Marketing Cloud) are destinations and do not tie directly to the data source.

Steps to Disconnect a Data Source

Delete Dependent Segments:Navigate to Data Cloud > Segments and remove any segments built using the data source.

Delete or Pause Data Streams:Go to Data Cloud > Data Streams and delete streams linked to the data source.

Disconnect the Data Source:Once dependencies are resolved, disconnect the source via Data Cloud > Data Sources.

The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time. This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization. This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.

The other options are incorrect for the following reasons:

A. CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.

B. The CRM Connector’s synchronization times can be customized to up to 60-minute intervals, not 15-minute intervals3. This option is false.

C. Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered. This option is false.

References:

1: Connect and Ingest Data in Data Cloud article on Salesforce Help

2: Data Sources in Data Cloud unit on Trailhead

3: Data Cloud for Admins module on Trailhead

4: [Formula Fields in Data Cloud] unit on Trailhead

: [Data Streams in Data Cloud] unit on Trailhead

 Understanding the Use Case:

The leadership team wants to focus on customers who have deposited more than $250,000 in the last five years and are not using advisory services.

 The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.

The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.

References:

Data Cloud Identity Resolution Reconciliation Rule Input

Configure Identity Resolution Rulesets

Data and Identity in Data Cloud

 Understanding Unified Individual Profile:

The unified individual profile combines data from multiple sources to create a comprehensive view of each customer.

To configure and use Segment Intelligence in Salesforce Data Cloud for improving marketing ROI, the user requires administrative privileges. Here’s the detailed analysis:

Data Cloud Admin (Option D):

Permission Set Scope:

The Data Cloud Admin permission set grants full access to configure advanced Data Cloud features, including Segment Intelligence, which provides AI-driven insights (e.g., audience trends, engagement metrics).

Admins can define metrics, enable predictive models, and analyze segment performance, all critical for optimizing marketing ROI.

Official Documentation:

Salesforce’s Data Cloud Permission Sets Guide explicitly states that Segment Intelligence configuration and management require administrative privileges. Only the Data Cloud Admin role can modify data model settings, access AI/ML tools, and apply segment recommendations (Source: "Admin vs. Standard User Permissions").

Why "Cloud Marketing Manager (C)" Is Incorrect:

No Standard Permission Set:

"Cloud Marketing Manager" is not a standard Salesforce Data Cloud permission set. This option may conflate Marketing Cloud roles (e.g., Marketing Manager) with Data Cloud’s permission structure.

Marketing Cloud vs. Data Cloud:

While Marketing Cloud has roles like "Marketing Manager," Data Cloud uses distinct permission sets (Admin, User, Data Aware Specialist). Segment Intelligence is a Data Cloud feature and requires Data Cloud-specific permissions.

Other Options:

Data Cloud Data Aware Specialist (A): Provides read-only access to data governance tools but lacks permissions to configure Segment Intelligence.

Data Cloud User (B): Allows basic segment activation and viewing but cannot set up AI-driven insights.

Steps to Validate:

Step 1: Assign the Data Cloud Admin permission set via Setup > Users > Permission Sets.

Step 2: Navigate to Data Cloud > Segment Intelligence to configure analytics, review AI recommendations, and optimize segments.

Step 3: Use insights to refine targeting and measure ROI improvements.

Conclusion: The Data Cloud Admin permission set is required to configure and leverage Segment Intelligence, as it provides the necessary administrative rights to Data Cloud’s advanced analytics and AI tools. "Cloud Marketing Manager" is not a valid permission set in Data Cloud.

When ingesting phone numbers into a custom field with thePhonedata type in Salesforce Data Cloud, the correct statement is thattext values can be accepted for ingestion into a phone data type field. Here’s why:

Understanding the Requirement

The marketing manager at Cloud Kicks plans to ingest corporate phone numbers into Data Cloud using a custom field with thePhonedata type.

It is important to understand how phone numbers are validated and stored during ingestion.

Why Text Values Can Be Accepted?

Phone Data Type Behavior:

ThePhonedata type in Salesforce accepts text values, as phone numbers are typically stored as strings (e.g., "+1-800-555-1234").

While the field is designed for phone numbers, it does not enforce strict formatting rules during ingestion.

Validation During Ingestion:

Salesforce does not validate the format of phone numbers at the time of ingestion.

Validation occurs only when the data is used in downstream systems or applications that enforce formatting rules.

Other Options Are Incorrect:

B. Data Cloud validates the format of the phone number at the time of ingestion: This is incorrect because Data Cloud does not validate phone number formats during ingestion.

C. The phone number field can only accept 10-digit values: This is incorrect because the Phone data type supports various formats, including international numbers.

D. The phone number field should be used as a primary key: This is incorrect because phone numbers are not unique identifiers and should not be used as primary keys.

Steps to Ingest Phone Numbers

Step 1: Create a Custom Field

Navigate toObject Manager > Account > Fields & Relationshipsand create a custom field with thePhonedata type.

Step 2: Configure Data Ingestion

Ensure the source data includes phone numbers as text values.

Map the phone number field from the source to the custom field in Data Cloud.

Step 3: Validate Data Usage

Test the ingested data to ensure it meets downstream requirements (e.g., formatting for dialing).

Conclusion

Text values can be accepted for ingestion into aPhonedata type field, as phone numbers are stored as strings and formatting validation occurs later in the process.