Data-Cloud-Consultant높은통과율인기덤프문제, Data-Cloud-Consultant최고품질덤프공부자료

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Salesforce Data-Cloud-Consultant Exam Overview:

Certification Vendor:Salesforce
Exam Name:Salesforce Certified Data 360 Consultant (formerly Salesforce Certified Data Cloud Consultant)
Exam Number:Data-Con-101 / Data-Cloud-Consultant
Certificate Validity Period:3 years
Exam Duration:105 minutes
Related Certifications:Salesforce Certified Marketing Cloud Consultant
Salesforce Certified Data Architect
Salesforce Certified Administrator
Real Exam Qty:60 (plus up to 5 unscored questions)
Passing Score:62% - 70%
Exam Price:USD $200 + applicable taxes
Exam Format:Multiple-select, Multiple-choice, Scenario-based questions
Available Languages:English
Recommended Training:Salesforce Trailhead - Data 360 / Data Cloud Learning Path
Exam Registration:Salesforce Webassessor Registration
Sample Questions:Salesforce Data-Cloud-Consultant Sample Questions
Exam Way:Online proctored or onsite at authorized testing centers
Pre Condition:No mandatory prerequisites; recommended: 1–2 years experience in data management, Salesforce implementation, or CDP solutions
Official Syllabus URL:https://help.salesforce.com/s/articleView?id=005298940&language=en_US

>> Data-Cloud-Consultant높은 통과율 인기 덤프문제 <<

Data-Cloud-Consultant최고품질 덤프공부자료 & Data-Cloud-Consultant덤프샘플문제 체험

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Salesforce Data-Cloud-Consultant 시험요강:

주제소개
주제 1
  • 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.
주제 2
  • Act on Data: This topic defines activations and their basic use cases, using attributes and related attributes, identifying and analyzing timing dependencies affecting the Data Cloud lifecycle. Additionally it focuses on troubleshooting common problems with activations, and using data actions, including their requirements and intended use cases.
주제 3
  • 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.

최신 Salesforce Data Cloud Data-Cloud-Consultant 무료샘플문제 (Q57-Q62):

질문 # 57
A Data 360 Consultant for a high- growth retail brand is configuring the ingestion of customer behavioral data from a mobile application. The brand requires that the raw, unmodified data be stored exactly as it appears in the source system before any mapping or transformations occur to maintain a historical record of the transmission. Which object serves as the container of this source data in Data 360?

정답:B

설명:
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, Data Lake Object fits because it changes the shape, keying, or refresh behavior at the Data 360 layer instead of forcing the source system to carry an analytics-specific design. In production, this keeps the upstream application simpler and gives the data team a repeatable way to prepare records for mapping, identity resolution, insights, or segmentation. 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.


질문 # 58
Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plans to use Data Cloud and ingest the Contact object via the CRM Connector.
What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?

정답:C

설명:
Explanation
The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point.
This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers.
The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streamingtransforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the existing fields in the Contact object. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone


질문 # 59
What is the minimum requirement needed when using the Visual Insights Builder to create a calculated insight?

정답:C

설명:
The analytics requirement determines whether the logic belongs in an insight, a semantic metric, or a query- time layer. At least one measure and one dimension matches the need because the business is asking for reusable metrics, dimensions, or aggregation behavior that consumers can filter and analyze. Data 360 separates raw harmonized data from analytical definitions so that dashboards, Tableau experiences, and segment logic remain consistent. 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. This is the nuance exam questions often test: the platform capability must match both the technical layer and the business timing requirement, not just sound related to data.


질문 # 60
Which information is provided in a .csv file when activating to Amazon S3?

정답:B

설명:
When activating to Amazon S3, the information that is provided in a .csv file is the activated data payload. The activated data payload is the data that is sent from Data Cloud to the activation target, which in this case is an Amazon S3 bucket1. The activated data payload contains the attributes and values of the individuals or entities that are included in the segment that is being activated2. The activated data payload can be used for various purposes, such as marketing, sales, service, or analytics3. The other options are incorrect because they are not provided in a .csv file when activating to Amazon S3. Option A is incorrect because an audit log is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Activation History tab4. Option C is incorrect because the metadata regarding the segment definition is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Segmentation tab5. Option D is incorrect because the manifest of origin sources within Data Cloud is not provided in a .csv file, but it can be viewed in the Data Cloud UI under the Data Sources tab. References: Data Activation Overview, Create and Activate Segments in Data Cloud, Data Activation Use Cases, View Activation History, Segmentation Overview, [Data Sources Overview]


질문 # 61
Northern Trail Qutfitters wants to be able to calculate each customer's lifetime value {LTV) but also create breakdowns of the revenue sourced by website, mobile app, and retail channels.
What should a consultant use to address this use case in Data Cloud?

정답:B

설명:
Explanation
Metrics on metrics is a feature that allows creating new metrics based on existing metrics and applying mathematical operations on them. This can be useful for calculating complex business metrics such as LTV, ROI, or conversion rates. In this case, the consultant can use metrics on metrics to calculate the LTV of each customer by summing up the revenue generated by them across different channels. The consultant can also create breakdowns of the revenue by channel by using the channel attribute as a dimension in the metric definition. References: Metrics on Metrics, Create Metrics on Metrics


질문 # 62
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