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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud Overview and Licensing | 7% | - Describe the business value of Data Cloud - Explain licensing model and capacity - Describe Data Cloud setup and configuration |
| Calculated Insights | 8% | - Create and configure calculated insights - Use formulas and functions in calculations - Publish insights to Data Cloud - Define metrics and measurements |
| Identity Resolution and Data Unification | 17% | - Understand unified records creation - Configure identity resolution rules - Manage data overlap and deduplication - Configure contact and account matching - Configure match rules and strategies |
| Data Model Setup and Configuration | 15% | - Configure custom objects, fields, and relationships - Set up and configure Data Lake objects - Understand data types and field mapping - Describe Harmonized Data Model concepts - Map source data to harmonized data model |
| Data Actions | 8% | - Configure data sync to external systems - Configure data actions for activation - Set up segmentation and target audiences - Create and manage data bundles |
| Data Ingestion | 15% | - Configure streaming data ingestion - Configure data sync and refresh schedules - Configure batch data ingestion - Set up and manage data connectors - Troubleshoot ingestion issues |
| Data Sharing and Security | 8% | - Implement row-level security - Configure data sharing settings - Manage user access and permissions - Understand data governance requirements |
| Data Cloud Monitoring and Troubleshooting | 22% | - Manage data retention and cleanup - Debug common configuration errors - Analyze identity resolution results - Troubleshoot data quality issues - Use Data Cloud analytics and reports - Monitor data ingestion and sync status |
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NEW QUESTION # 32
A Data 360 Consultant is building a segment to announce a new product launch for customers who have previously purchased black pants. How should the consultant place the attributes for product color and product type from the Order Product object to meet this criteria?
Answer: A
Explanation:
The segmentation and activation design starts with grain: who or what the audience represents, and which attributes must travel with it. Place the attributes for product color and product type in a single container.
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. 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 # 33
Cloud Kicks wants to be able to build a segment of customers who have visited its website within the previous
7 days.
Which filter operator on the Engagement Date field fits this use case?
Answer: B
Explanation:
The filter operator Last Number of Days allows you to filter on date fields using a relative date range that specifies the number of days before today. For example, you can use this operator to filter on customers who have visited your website in the last 7 days, or the last 30 days, or any number of days you want. This operator is useful for creating dynamic segments that update automatically based on the current date12. References:
* Relative Date Filter Reference
* Create Filtered Segments
NEW QUESTION # 34
A Data Cloud customer wants to adjust their identity resolution rules to increase their accuracy of matches. Rather than matching on email address, they want to review a rule that joins their CRM Contacts with their Marketing Contacts, where both use the CRM ID as their primary key.
Which two steps should the consultant take to address this new use case?
Choose 2 answers
Answer: A,D
Explanation:
Explanation
To address this new use case, the consultant should map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both, and create a matching rule based on party identification that matches on CRM ID as the party identification name. This way, the consultant can ensure that the CRM Contacts and Marketing Contacts are matched based on their CRM ID, which is a unique identifier for each individual. By using Party Identification, the consultant can also leverage the benefits of this attribute, such as being able to match across different entities and sources, and being able to handle multiple values for the same individual. The other options are incorrect because they either do not use the CRM ID as the primary key, or they do not use Party Identification as the attribute type. References: Configure Identity Resolution Rulesets, Identity Resolution Match Rules, Data Cloud Identity Resolution Ruleset, Data Cloud Identity Resolution Config Input
NEW QUESTION # 35
A customer has a requirement to be able to view the last time each segment was published within their Data Cloud org.
Which two features should the consultant recommend to best address this requirement?
Choose 2 answers
Answer: A,C
Explanation:
A customer who wants to view the last time each segment was published within their Data Cloud org can use the dashboard and report features to achieve this requirement. A dashboard is a visual representation of data that can show key metrics, trends, and comparisons. A report is a tabular or matrix view of data that can show details, summaries, and calculations. Both dashboard and report features allow the user to create, customize, and share data views based on their needs and preferences. To view the last time each segment was published, the user can create a dashboard or a report that shows the segment name, the publish date, and the publish status fields from the segment object. The user can also filter, sort, group, or chart the data by these fields to get more insights and analysis. The user can also schedule, refresh, or export the dashboard or report data as needed. Reference: Dashboards, Reports
NEW QUESTION # 36
A segment fails to refresh with the error "Segment references too many data lake objects (DLOS)".
Which two troubleshooting tips should help remedy this issue?
Choose 2 answers
Answer: C,D
Explanation:
The error "Segment references too many data lake objects (DLOs)" occurs when a segment query exceeds the limit of 50 DLOs that can be referenced in a single query. This can happen when the segment has too many filters, nested segments, or exclusion criteria that involve different DLOs. To remedy this issue, the consultant can try the following troubleshooting tips:
Split the segment into smaller segments. The consultant can divide the segment into multiple segments that have fewer filters, nested segments, or exclusion criteria. This can reduce the number of DLOs that are referenced in each segment query and avoid the error. The consultant can then use the smaller segments as nested segments in a larger segment, or activate them separately.
Use calculated insights in order to reduce the complexity of the segmentation query. The consultant can create calculated insights that are derived from existing data using formulas. Calculated insights can simplify the segmentation query by replacing multiple filters or nested segments with a single attribute. For example, instead of using multiple filters to segment individuals based on their purchase history, the consultant can create a calculated insight that calculates the lifetime value of each individual and use that as a filter.
The other options are not troubleshooting tips that can help remedy this issue. Refining segmentation criteria to limit up to five custom data model objects (DMOs) is not a valid option, as the limit of 50 DLOs applies to both standard and custom DMOs. Spacing out the segment schedules to reduce DLO load is not a valid option, as the error is not related to the DLO load, but to the segment query complexity.
Reference:
Troubleshoot Segment Errors
Create a Calculated Insight
Create a Segment in Data Cloud
NEW QUESTION # 37
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