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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

Identity Resolution: It describes matching and how its rule sets are applied. Furthermore, it discusses reconciling data and its rule sets, the results of identity resolution, and use cases.

Topic 2

Segmentation and Insights: This topic defines basic concepts of segmentation and use cases, identifies scenarios for analyzing segment membership, configuring, refining, and maintaining segments within Data Cloud, and differentiating between calculated and streaming insights.

Topic 3

Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.

Topic 4

Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.

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Salesforce Certified Data Cloud Consultant Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which configuration supports separate Amazon S3 buckets for data ingestion and activation?

A. Separate user credentials for data stream and activation target
B. Dedicated S3 data sources in Data Cloud setup
C. Multiple S3 connectors in Data Cloud setup
D. Dedicated S3 data sources in activation setup

Answer: B

Explanation:
To support separate Amazon S3 buckets for data ingestion and activation, you need to configure dedicated S3 data sources in Data Cloud setup. Data sources are used to identify the origin and type of the data that you ingest into Data Cloud1. You can create different data sources for each S3 bucket that you want to use for ingestion or activation, and specify the bucket name, region, and access credentials2. This way, you can separate and organize your data by different criteria, such as brand, region, product, or business unit3. The other options are incorrect because they do not support separate S3 buckets for data ingestion and activation. Multiple S3 connectors are not a valid configuration in Data Cloud setup, as there is only one S3 connector available4. Dedicated S3 data sources in activation setup are not a valid configuration either, as activation setup does not require data sources, but activation targets5. Separate user credentials for data stream and activation target are not sufficient to support separate S3 buckets, as you also need to specify the bucket name and region for each data source2. References: Data Sources Overview, Amazon S3 Storage Connector, Data Spaces Overview, Data Streams Overview, Data Activation Overview

NEW QUESTION # 112
The marketing manager at Cloud Kicks plans to bring in corporate phone numbers for its accounts into Data Cloud. They plan to use a custom field with data set to Phone to store these phone numbers.
Which statement is true when ingesting phone numbers?

A. The phone number field should be used as a primary key.
B. Text value can be accepted for ingestion into = phone data type field.
C. The phone number field car only accept 10-digit values.
D. Data Cloud validates the format of the phone number at the time of Ingestion.

Answer: B

Explanation:
When ingesting phone numbers into a custom field with the Phone data type in Salesforce Data Cloud, the correct statement is that text 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 the Phone data 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 :
The Phone data 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 to Object Manager > Account > Fields & Relationships and create a custom field with the Phone data 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 a Phone data type field, as phone numbers are stored as strings and formatting validation occurs later in the process.

NEW QUESTION # 113
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud.
In what order should each process be run to ensure that freshly imported data is ready and available to use for any segment?

A. Refresh Data Stream > Identity Resolution > Calculated Insight
B. Refresh Data Stream > Calculated Insight > Identity Resolution
C. Calculated Insight > Refresh Data Stream > Identity Resolution
D. Identity Resolution > Refresh Data Stream > Calculated Insight

Answer: A

Explanation:
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.
Reference:
1: Configure Data Stream Refresh and Frequency - Salesforce
2: Identity Resolution Ruleset Processing Results - Salesforce
3: Calculated Insights - Salesforce

NEW QUESTION # 114
Cloud Kicks plans to do a full deletion of one of its existing data streams and its underlying data lake object (DLO).
What should the consultant consider before deleting the data stream?

A. The data stream can be deleted without implicitly deleting the underlying DLO.
B. The data stream must be associated with a data kit.
C. The underlying DLO can be used in a data transform.
D. The underlying DLO cannot be mapped to a data model object.

Answer: C

Explanation:
Data Streams and DLOs: In Salesforce Data Cloud, data streams are used to ingest data, which is then stored in Data Lake Objects (DLOs).
Deletion Considerations: Before deleting a data stream, it's crucial to consider the dependencies and usage of the underlying DLO.
Data Transform Usage:
* Impact of Deletion: If the underlying DLO is used in a data transform, deleting the data stream will affect any transforms relying on that DLO.
* Dependency Check: Ensure that the DLO is not part of any active data transformations or processes that could be disrupted by its deletion.
References:
* Salesforce Data Cloud Documentation: Data Streams
* Salesforce Data Cloud Documentation: Data Transforms

NEW QUESTION # 115
The recruiting team at Cumulus Financial wants to identify which candidates have browsed the jobs page on its website at least twice within the last 24 hours. They want the information about these candidates to be available for segmentation in Data Cloud and the candidates added to their recruiting system.
Which feature should a consultant recommend to achieve this goal?

A. Batch bata transform
B. Streaming insight
C. Streaming data transform
D. Calculated insight

Answer: B

Explanation:
A streaming insight is a feature that allows users to create and monitor real-time metrics from streaming data sources, such as web and mobile events. A streaming insight can also trigger data actions, such as sending notifications, creating records, or updating fields, based on the metric values and conditions. Therefore, a streaming insight is the best feature to achieve the goal of identifying candidates who have browsed the jobs page on the website at least twice within the last 24 hours, and adding them to the recruiting system. The other options are incorrect because:
* A streaming data transform is a feature that allows users to transform and enrich streaming data using SQL expressions, such as filtering, joining, aggregating, or calculating values. However, a streaming data transform does not provide the ability to monitor metrics or trigger data actions based on conditions.
* A calculated insight is a feature that allows users to define and calculate multidimensional metrics from data using SQL expressions, such as LTV, CSAT, or average order value. However, a calculated insight is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions.
* A batch data transform is a feature that allows users to create and schedule complex data transformations using a visual editor, such as joining, aggregating, filtering, or appending data.
However, a batch data transform is not suitable for real-time data analysis, as it runs on a scheduled basis and does not support data actions. References: Streaming Insights, Create a Streaming Insight, Use Insights in Data Cloud, Learn About Data Cloud Insights, Data Cloud Insights Using SQL, Streaming Data Transforms, Get Started with Batch Data Transforms in Data Cloud, Transformations for Batch Data Transforms, Batch Data Transforms in Data Cloud: Quick Look, Salesforce Data Cloud: AI CDP.

NEW QUESTION # 116
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