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| Section | Weight | Objectives |
|---|---|---|
| Setup and Administration | 13% | - Monitoring, reporting, and tools (Data Explorer, APIs) - Permissions, settings, and security configuration - Data streams, data spaces, and governance |
| Segmentation and Insights | 18% | - Segment creation, configuration, and maintenance - AI-powered insights and analysis - Calculated vs streaming insights |
| Data Ingestion and Modeling | 20% | - Ingestion methods, connectors, and transformation - Customer 360 data model and schema design - Data mapping, validation, and quality best practices |
| Act on Data / Activation | 18% | - Data actions and integration with Salesforce products - Troubleshooting activation issues and dependencies - Activation types and use cases |
| Solution Positioning / Overview | 14% | - Common use cases and data ethics principles - Role in AI and predictive capabilities - Data 360 / Data Cloud terminology and business value |
| Identity Resolution and Harmonization | 14% | - Matching rules (deterministic / probabilistic) - Reconciliation and result analysis - Identity graphs and data unification |
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NEW QUESTION # 101
A Data 360 Consultant recently discovered that their Identity Resolution process is matching individuals who share email addresses or phone numbers but are not actually the same individual. What should the consultant do to address this issue?
Answer: A
Explanation:
The identity logic is about linking source profiles safely, then choosing the best surviving values for the unified profile. Create and run a new ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved. is appropriate because identity resolution needs reliable match inputs, qualified identifiers, and controlled reconciliation. It is not just deduplication; it is a rules-driven process that connects source records into a trusted unified profile. 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 # 102
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 # 103
Northern Trail Outfitters (NTD) creates a calculated insight to computerecency, frequency, monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores that it activates to a Marketing Cloud activation target.
Which two actions are required when configuring the activation?
Choose 2 answers
Answer: B,C
Explanation:
Explanation
To configure an activation to a Marketing Cloud activation target, you need to choose a segment and select contact points. Choosing a segment allows you to specify which unified individuals you want to activate.
Selecting contact points allows you to map the attributes from the segment to the fields in the Marketing Cloud data extension. You do not need to add additional attributes or add the calculated insight in the activation, as these are already part of the segment definition. References: Create a Marketing Cloud Activation Target; Types of Data Targets in Data Cloud
NEW QUESTION # 104
A data architect is modeling a relationship between a custom Vehicle data model object (DMO) and the standard Individual DMO to track car ownership. The requirement states that one vehicle can be owned by only one individual, but one individual can own multiple vehicles. Which cardinality should the architect select when creating this relationship from Vehicle to Individual?
Answer: B
Explanation:
The modeling decision should make the data understandable, reusable, and correctly related before segmentation or activation depends on it. Many-to-One (N:1) 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.
NEW QUESTION # 105
Which statement is true related to batch ingestions from Salesforce CRM?
Answer: A
Explanation:
The design point is to preserve source fidelity while shaping data only where Data 360 processing needs it.
Here, When a column is added or removed, the CRM Connector performs a full refresh. 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.
NEW QUESTION # 106
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