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Salesforce Data-Con-101 Exam Overview:

Certification Vendor:Salesforce
Exam Name:Salesforce Certified Data Cloud Consultant
Exam Number:Data-Con-101
Certificate Validity Period:Maintenance required annually (Trailhead modules)
Exam Format:Multiple-select, Multiple-choice
Related Certifications:Salesforce Certified AI Associate
Exam Duration:105 minutes
Passing Score:62%
Exam Price:USD 200
Available Languages:English
Real Exam Qty:60 (plus up to 5 unscored questions)
Sample Questions:Salesforce Data-Con-101 Sample Questions
Exam Way:Online (Webassessor/PSI) or Onsite (Pearson VUE)
Pre Condition:No hard prerequisites; recommended 2+ years of experience in data strategy and data modeling
Official Syllabus URL:https://trailhead.salesforce.com/credentials/datacloudconsultant

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Salesforce Data-Con-101 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Act on Data: This domain focuses on leveraging Data Cloud data for downstream actions through activations and data actions. It covers working with attributes, managing timing dependencies, troubleshooting activation issues like errors and rejected counts, and understanding requirements for triggering automated processes.
Topic 2
  • Data Cloud Overview: This domain covers the foundational understanding of Data Cloud including its core purpose, terminology, business value, and technical architecture. It also addresses typical use cases and the essential principles of ethical data handling when working with customer data.
Topic 3
  • Data Ingestion and Modeling: This domain addresses bringing data into Data Cloud and structuring it properly through transformation, ingestion from various sources, and data mapping. It emphasizes best practices for modeling data to support identity resolution and validating ingested data using available tools.
Topic 4
  • Data Cloud Setup and Administration: This domain focuses on configuring and managing Data Cloud environments through permissions, data streams, data bundles, and data spaces. It also covers administrative tools and techniques for diagnosing and exploring data using reports, dashboards, flows, APIs, and explorer tools.
Topic 5
  • Identity Resolution: This domain explores creating unified customer profiles through matching and reconciliation processes. It covers how rule sets determine when records link together, how conflicting data is resolved, and understanding the outcomes and use cases of unified identities.

Salesforce Certified Data Cloud Consultant Sample Questions (Q84-Q89):

NEW QUESTION # 84
A customer requests that their personal data be deleted.
Which action should the consultant take to accommodate this request in Data Cloud?

Answer: B


NEW QUESTION # 85
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?

Answer: A

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 # 86
What is a reason to create a formula when ingesting a data stream?

Answer: A

Explanation:
Creating a formula during data stream ingestion is often done to manipulate or transform data fields to meet specific requirements. In this case, the most common reason is to transform a date-time field into a date field for use in data mapping . Here's why:
Understanding the Requirement
When ingesting data into Salesforce Data Cloud, certain fields may need to be transformed to align with the target data model.
For example, a date-time field (e.g., "2023-10-05T14:30:00Z") may need to be converted into a date field (e.
g., "2023-10-05") for proper mapping and analysis.
Why Transform a Date-Time Field into a Date Field?
Data Mapping Compatibility :
Some data models or downstream systems may only accept date fields (without the time component).
Transforming the field ensures compatibility and avoids errors during ingestion or activation.
Simplified Analysis :
Removing the time component simplifies analysis and reporting, especially when working with daily trends or aggregations.
Standardization :
Converting date-time fields into consistent date formats ensures uniformity across datasets.
Steps to Implement This Solution
Step 1: Identify the Date-Time Field
During the data stream setup, identify the field that contains the date-time value (e.g., "Order_Date_Time").
Step 2: Create a Formula Field
Use the Formula Field option in the data stream configuration to create a new field.
Apply a transformation function (e.g., DATE() or equivalent) to extract the date portion from the date-time field.
Step 3: Map the Transformed Field
Map the newly created date field to the corresponding field in the target data model (e.g., Unified Profile or Data Lake Object).
Step 4: Validate the Transformation
Test the data stream to ensure the transformation works correctly and the date field is properly ingested.
Why Not Other Options?
A). To concatenate files so they are ingested in the correct sequence :Concatenation is not a typical use case for formulas during ingestion. File sequencing is usually handled at the file ingestion level, not through formulas.
B). To add a unique external identifier to an existing ruleset :Adding a unique identifier is typically done during data preparation or identity resolution, not through formulas during ingestion.
D). To remove duplicate rows of data from the data stream :Removing duplicates is better handled through deduplication rules or transformations, not formulas.
Conclusion
The primary reason to create a formula when ingesting a data stream is to transform a date-time field into a date field for use in data mapping . This ensures compatibility, simplifies analysis, and standardizes the data for downstream use.


NEW QUESTION # 87
A consultant needs to publish segment data to the Audience DMO that can be retrieved using the Query APIs.
When creating the activation target, which type of target should the consultant select?

Answer: A


NEW QUESTION # 88
A consultant needs to update a field in CRM as soon as a record gets updated in the DMO.
Which feature should the consultant use?

Answer: C

Explanation:
When a record in the Data Model Object (DMO) is updated, Data Actions can be used to immediately trigger updates in an external system like Salesforce CRM.
Data Actions allow for real-time or near-real-time updates to external systems.
When a record in the DMO is updated, a Data Action can push updates to CRM fields.
This ensures that CRM always reflects the latest Data Cloud updates without manual intervention.
Why Not A?
Data Share Targets are used for sharing data externally (e.g., Snowflake) but do not update CRM fields directly.
Why Not C?
Rapid Segments are used for fast audience segmentation, not for updating CRM fields.
Why Not D?
Streaming Data Transforms are used for real-time data processing, but they do not update CRM fields directly.
# Salesforce Data Cloud Reference:
Salesforce Help Documentation - Data Actions Overview
Trailhead Module: Automating Data Updates with Data Actions
Salesforce Knowledge Base - Best Practices for Keeping CRM and Data Cloud in Sync


NEW QUESTION # 89
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