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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Data Cloud Consultant Exam |
| Exam Number: | Data-Cloud-Consultant |
| Available Languages: | English |
| Recommended Training: | Salesforce Trailhead Data Cloud Learning |
| Exam Registration: | Salesforce Credentials |
| Sample Questions: | Salesforce Data-Cloud-Consultant Sample Questions |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials |
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NEW QUESTION # 50
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?
Answer: D
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 # 51
Northern Trail Outfitters (NTO), an outdoor lifestyle clothing brand, recently started a new line of business. The new business specializes in gourmet camping food. For business reasons as well as security reasons, it's important to NTO to keep all Data Cloud data separated by brand.
Which capability best supports NTO's desire to separate its data by brand?
Answer: C
Explanation:
Data spaces are logical containers that allow you to separate and organize your data by different criteria, such as brand, region, product, or business unit1. Data spaces can help you manage data access, security, and governance, as well as enable cross-cloud data integration and activation2. For NTO, data spaces can support their desire to separate their data by brand, so that they can have different data models, rules, and insights for their outdoor lifestyle clothing and gourmet camping food businesses. Data spaces can also help NTO comply with any data privacy and security regulations that may apply to their different brands3. The other options are incorrect because they do not provide the same level of data separation and organization as data spaces. Data streams are used to ingest data from different sources into Data Cloud, but they do not separate the data by brand4. Data model objects are used to define the structure and attributes of the data, but they do not isolate the data by brand5. Data sources are used to identify the origin and type of the data, but they do not partition the data by brand. Reference: Data Spaces Overview, Create Data Spaces, Data Privacy and Security in Data Cloud, Data Streams Overview, Data Model Objects Overview, [Data Sources Overview]
NEW QUESTION # 52
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud. Based on this, a calculated insight is created that shows the total spend per customer in the last
30 days.
In which sequence should each process be run to ensure that freshly imported data is ready and available to use for any segment?
Answer: A
Explanation:
To ensure that freshly imported data is ready and available for use in any segment, the processes should be run in the following sequence: Refresh Data Stream > Identity Resolution > Calculated Insight . Here's why:
Understanding the Requirement
Northern Trail Outfitters uploads new customer data daily to an Amazon S3 bucket, which is ingested into Data Cloud.
A calculated insight is created to show the total spend per customer in the last 30 days.
The goal is to ensure that the data is properly refreshed, resolved, and processed before being used in segments.
Why This Sequence?
Step 1: Refresh Data Stream
Before any processing can occur, the data stream must be refreshed to ingest the latest data from the Amazon S3 bucket.
This ensures that the most up-to-date customer data is available in Data Cloud.
Step 2: Identity Resolution
After refreshing the data stream, identity resolution must be performed to merge related records into unified profiles.
This step ensures that customer data is consolidated and ready for analysis.
Step 3: Calculated Insight
Once identity resolution is complete, the calculated insight can be generated to calculate the total spend per customer in the last 30 days.
This ensures that the insight is based on the latest and most accurate data.
Other Options Are Incorrect :
B). Refresh Data Stream > Calculated Insight > Identity Resolution : Calculated insights cannot be generated before identity resolution because they rely on unified profiles.
C). Calculated Insight > Refresh Data Stream > Identity Resolution : Calculated insights require both fresh data and resolved identities, so this sequence is invalid.
D). Identity Resolution > Refresh Data Stream > Calculated Insight : Identity resolution cannot occur without first refreshing the data stream to bring in the latest data.
Conclusion
The correct sequence is Refresh Data Stream > Identity Resolution > Calculated Insight , ensuring that the data is properly refreshed, resolved, and processed before being used in segments.
NEW QUESTION # 53
Which solution provides an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis?
Answer: C
Explanation:
The solution that provides an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis is the Marketing Cloud Data extension Data Stream. The Marketing Cloud Data extension Data Stream is a feature that allows customers to stream data from Marketing Cloud data extensions to Data Cloud data spaces. Customers can select which data extensions they want to stream, and Data Cloud will automatically create and update the corresponding data model objects (DMOs) in the data space. Customers can also map the data extension fields to the DMO attributes using a user interface or an API. The Marketing Cloud Data extension Data Stream can help customers ingest subscriber profile attributes and other data from Marketing Cloud into Data Cloud without writing any code or setting up any complex integrations.
The other options are not solutions that provide an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis. Automation Studio and Profile file API are tools that can be used to export data from Marketing Cloud to external systems, but they require customers to write scripts, configure file transfers, and schedule automations. Marketing Cloud Connect API is an API that can be used to access data from Marketing Cloud in other Salesforce solutions, such as Sales Cloud or Service Cloud, but it does not support streaming data to Data Cloud. Email Studio Starter Data Bundle is a data kit that contains sample data and segments for Email Studio, but it does not contain subscriber profile attributes or stream data to Data Cloud.
References:
Marketing Cloud Data Extension Data Stream
Data Cloud Data Ingestion
[Marketing Cloud Data Extension Data Stream API]
[Marketing Cloud Connect API]
[Email Studio Starter Data Bundle]
NEW QUESTION # 54
A solution architect needs to create a segment of " High Value Customers " . They define this as anyone who qualifies for the existing Platinum Loyalty segment OR anyone who has spent more than US $5,000. What is the most efficient way to build this?
Answer: B
Explanation:
The segmentation and activation design starts with grain: who or what the audience represents, and which attributes must travel with it. Use a nested segment for " Platinum Loyalty " and add a separate container for the minimum spend within OR operator. works because Data 360 segmentation and activation must respect audience grain, relationship paths, and activation payload rules. A segment can qualify the audience, but activation determines which related attributes or contact points are actually sent downstream. The distractors fall short because they either move the problem into the wrong system, add needless duplication, ignore Data
360 object relationships, or rely on a feature built for a different lifecycle stage. In a real implementation, those choices usually create brittle pipelines, stale data, security exposure, or segments that look correct on paper but fail when activated. Thinking like an architect, the selected option places the logic where Data 360 can govern it and reuse it reliably.
NEW QUESTION # 55
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