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| Section | Weight | Objectives |
|---|---|---|
| Solution Positioning / Overview | 14% | - Data 360 / Data Cloud terminology and business value - Common use cases and data ethics principles - Role in AI and predictive capabilities |
| Identity Resolution and Harmonization | 14% | - Identity graphs and data unification - Reconciliation and result analysis - Matching rules (deterministic / probabilistic) |
| Data Ingestion and Modeling | 20% | - Customer 360 data model and schema design - Ingestion methods, connectors, and transformation - Data mapping, validation, and quality best practices |
| Act on Data / Activation | 18% | - Activation types and use cases - Troubleshooting activation issues and dependencies - Data actions and integration with Salesforce products |
| Setup and Administration | 13% | - Permissions, settings, and security configuration - Data streams, data spaces, and governance - Monitoring, reporting, and tools (Data Explorer, APIs) |
| Segmentation and Insights | 18% | - Segment creation, configuration, and maintenance - Calculated vs streaming insights - AI-powered insights and analysis |
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質問 # 40
Which statement is true related to batch ingestions from Salesforce CRM?
正解：C
解説：
The question asks which statement is true about batch ingestions from Salesforce CRM into Salesforce Data Cloud. Batch ingestion refers to the process of periodically syncing data from Salesforce CRM (e.g., Accounts, Contacts, Opportunities) into Data Cloud. The focus is on how the CRM connector handles changes in data structure (e.g., adding or removing columns) and synchronization behavior.
Why A is Correct: "When a column is added or removed, the CRM connector performs a full refresh." Behavior of the CRM Connector :
The Salesforce CRM connector automatically detects schema changes, such as when a field (column) is added or removed in the source CRM object.
When such changes occur, the CRM connector triggers a full refresh of the data for that object. This ensures that the data model in Data Cloud aligns with the updated schema in Salesforce CRM.
Why a Full Refresh is Necessary :
A full refresh ensures that all records are re-ingested with the updated schema, avoiding inconsistencies or missing data caused by incremental updates.
Incremental updates only capture changes (e.g., new or modified records), so they cannot handle schema changes effectively.
Other Options Are Incorrect :
B). The CRM connector performs an incremental refresh when 600K or more deletion records are detected :
This is incorrect because the CRM connector does not switch to incremental refresh based on the number of deletion records. It always performs incremental updates unless a schema change triggers a full refresh.
C). The CRM connector's synchronization times can be customized to up to 15-minute intervals : While synchronization schedules can be customized, the minimum interval is typically 1 hour , not 15 minutes.
D). CRM data cannot be manually refreshed and must wait for the next scheduled synchronization : This is incorrect because users can manually trigger a refresh of CRM data in Data Cloud if needed.
Steps to Understand CRM Connector Behavior
Step 1: Schema Changes Trigger Full Refresh
If a field is added or removed in Salesforce CRM, the CRM connector detects this change and initiates a full refresh of the corresponding object in Data Cloud.
Step 2: Incremental Updates for Regular Syncs
For regular synchronization, the CRM connector performs incremental updates, capturing only new or modified records since the last sync.
Step 3: Manual Refresh Option
Users can manually trigger a refresh in Data Cloud if immediate synchronization is required, bypassing the scheduled sync.
Step 4: Monitor Synchronization Logs
Use the Data Cloud Monitoring tools to track synchronization status, including full refreshes and incremental updates.
Conclusion
The statement "When a column is added or removed, the CRM connector performs a full refresh" is true. This behavior ensures that the data model in Data Cloud remains consistent with the schema in Salesforce CRM, avoiding potential data integrity issues.
質問 # 41
A consultant wants to confirm the Identity resolution they Just set up. Which two features can the consultant use to validate the data on a unified profile?
Choose 2 answers
正解：A、D
解説：
To validate the data on a unified profile after setting up identity resolution, the consultant can use Data Explorer and the Query API . Here's why:
Understanding Identity Resolution Validation
Identity resolution combines data from multiple sources into a unified profile.
Validating the unified profile ensures that the resolution process is working correctly and that the data is accurate.
Why Data Explorer and Query API?
Data Explorer :
Data Explorer is a built-in tool in Salesforce Data Cloud that allows users to view and analyze unified profiles.
It provides a detailed view of individual profiles, including resolved identities and associated attributes.
Query API :
The Query API enables programmatic access to unified profiles and related data.
Consultants can use the API to query specific profiles and validate the results of identity resolution programmatically.
Other Options Are Less Suitable :
A . Identity Resolution : This refers to the process itself, not a tool for validation.
B . Data Actions : Data actions are used to trigger workflows or integrations, not for validating unified profiles.
Steps to Validate Unified Profiles
Using Data Explorer :
Navigate to Data Cloud > Data Explorer .
Search for a specific profile and review its resolved identities and attributes.
Verify that the data aligns with expectations based on the identity resolution rules.
Using Query API :
Use the Query API to retrieve unified profiles programmatically.
Compare the results with expected outcomes to confirm accuracy.
Conclusion
The consultant should use Data Explorer and the Query API to validate the data on unified profiles, ensuring that identity resolution is functioning as intended.
質問 # 42
Northern Trail Outfitters uploads new customer data to an Amazon S3 bucket on a daily basis to be ingested in Data 360. 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?
正解：D
解説：
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, Refresh Data Stream > Identity Resolution > Calculated Insight 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.
質問 # 43
A company wants to include certain personalized fields in an email by including related attributes during the activation in Data Cloud. It notices that some values, such as purchased product names, do not have consistent casing in Marketing Cloud Engagement. For example, purchased product names appear as follows: Jacket, jacket, shoes, SHOES. The company wants to normalize all names to proper case and replace any null values with a default value.
How should a consultant fulfill this requirement within Data Cloud?
正解：A
質問 # 44
A global marketing team is using Salesforce Data 360 to create highly targeted segments. Many of the team members are new to the organization and are unfamiliar with the data model and attributes, such as various Product Category names. This lack of data knowledge is causing delays, as users must frequently ask the data team for assistance in identifying the proper segmentation criteria. Which configuration should a Data 360 Consultant implement on the data model object (DMO) so that these users can see and select from the actual data values present in the system while building their segments?
正解：B
解説：
The modeling decision should make the data understandable, reusable, and correctly related before segmentation or activation depends on it. Enable Value Suggestion for text fields on DMOs. fits because Data
360 depends on mappings and relationships between data lake objects and data model objects. Correct modeling lets the same attribute mean the same thing across source systems and prevents downstream users from building logic on ambiguous fields. 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.
質問 # 45
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