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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud Setup and Administration | 12% | - Administration tools: reports, dashboards, flows, packaging - Data streams, data bundles, and data spaces - Permissions, permission sets, and org settings - Data exploration and diagnostics: Explorer tools, APIs |
| Data Cloud Overview | 18% | - Architecture, dependencies, and lifecycle - Common enterprise use cases - Core functions, terminology, and business value - Data ethics and responsible usage principles |
| Act on Data | 18% | - Activation concepts and use cases - Troubleshooting activation issues - Data actions and requirements - Attributes, related attributes, and timing dependencies |
| Segmentation and Insights | 18% | - Segment creation, maintenance, and membership analysis - Segmentation concepts and business scenarios - Calculated vs. streaming insights |
| Data Ingestion and Modeling | 20% | - Ingestion from various source systems - Validation and inspection of ingested data - Transformation and data mapping capabilities - Data modeling best practices aligned with identity resolution |
| Identity Resolution | 14% | - Unified identity outcomes and use cases - Matching rules and application - Reconciliation rules and data unification |
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NEW QUESTION # 62
A consultant wants to build a new audience in Data Cloud.
Which three criteria can the consultant include when building a segment?
Choose 3 answers
Answer: B,C,E
Explanation:
A segment is a subset of individuals who meet certain criteria based on their attributes and behaviors. A consultant can use different types of criteria when building a segment in Data Cloud, such as:
Direct attributes: These are attributes that describe the characteristics of an individual, such as name, email, gender, age, etc. These attributes are stored in the Profile data model object (DMO) and can be used to filter individuals based on their profile data.
Calculated Insights: These are insights that perform calculations on data in a data space and store the results in a data extension. These insights can be used to segment individuals based on metrics or scores derived from their data, such as customer lifetime value, churn risk, loyalty tier, etc.
Related attributes: These are attributes that describe the relationships of an individual with other DMOs, such as Email, Engagement, Order, Product, etc. These attributes can be used to segment individuals based on their interactions or transactions with different entities, such as email opens, clicks, purchases, etc.
The other two options are not valid criteria for building a segment in Data Cloud. Data stream attributes are attributes that describe the streaming data that is ingested into Data Cloud from various sources, such as Marketing Cloud, Commerce Cloud, Service Cloud, etc. These attributes are not directly available for segmentation, but they can be transformed and stored in data extensions using streaming data transforms.
Streaming insights are insights that analyze streaming data in real time and trigger actions based on predefined conditions. These insights are not used for segmentation, but for activation and personalization. References: Create a Segment in Data Cloud, Use Insights in Data Cloud, Data Cloud Data Model
NEW QUESTION # 63
Cumulus Financial wants its service agents to view a display of all cases associated with a Unified Individual on a contact record.
Which two features should a consultant consider for this use case?
Choose 2 answers
Answer: B,D
Explanation:
A Unified Individual is a profile that combines data from multiple sources using identity resolution rules in Data Cloud. A Unified Individual can have multiple contact points, such as email, phone, or address, that link to different systems and records. A consultant can use the following features to display all cases associated with a Unified Individual on a contact record:
Profile API: This is a REST API that allows you to retrieve and update Unified Individual profiles and related attributes in Data Cloud. You can use the Profile API to query the cases that are related to a Unified Individual by using the contact point ID or the unified ID as a filter. You can also use the Profile API to update the Unified Individual profile with new or modified case information from other systems.
Lightning Web Components: These are custom HTML elements that you can use to create reusable UI components for your Salesforce apps. You can use Lightning Web Components to create a custom component that displays the cases related to a Unified Individual on a contact record. You can use the Profile API to fetch the data from Data Cloud and display it in a table, list, or chart format. You can also use Lightning Web Components to enable actions, such as creating, editing, or deleting cases, from the contact record.
The other two options are not relevant for this use case. A Data Action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. A Data Action is used for activation and personalization, not for displaying data on a contact record. A Query APL is a query language that allows you to access and manipulate data in Data Cloud. A Query APL is used for data exploration and analysis, not for displaying data on a contact record. References: Profile API Developer Guide, Lightning Web Components Developer Guide, Create Unified Individual Profiles Unit
NEW QUESTION # 64
A consultant needs to package Data Cloud components from one
organization to another.
Which two Data Cloud components should the consultant include in a
data kit to achieve this goal?
Choose 2 answers
Answer: A,C
Explanation:
To package Data Cloud components from one organization to another, the consultant should include the following components in a data kit:
Data model objects: These are the custom objects that define the data model for Data Cloud, such as Individual, Segment, Activity, etc. They store the data ingested from various sources and enable the creation of unified profiles and segments1.
Identity resolution rulesets: These are the rules that determine how data from different sources are matched and merged to create unified profiles. They specify the criteria, logic, and priority for identity resolution2. References:
1: Data Model Objects in Data Cloud
2: Identity Resolution Rulesets in Data Cloud
NEW QUESTION # 65
What does it mean to build a trust-based, first-party data asset?
Answer: D
Explanation:
Building a trust-based, first-party data asset means collecting, managing, and activating data from your own customers and prospects in a way that respects their privacy and preferences. It also means providing them with clear and honest information about how you use their data, what benefits they can expect from sharing their data, and how they can control their data. By doing so, you can create a mutually beneficial relationship with your customers, where they trust you to use their data responsibly and ethically, and you can deliver more relevant and personalized experiences to them. A trust-based, first-party data asset can help you improve customer loyalty, retention, and growth, as well as comply with data protection regulations and standards. References: Use first-party data for a powerful digital experience, Why first-party data is the key to data privacy, Build a first-party data strategy
NEW QUESTION # 66
Northern Trail Outfitters unifies individuals in its Data Cloud instance.
Which three features ca e consultant use to validate the data on a unified profile?
Choose 3 answers
Answer: C,D,E
Explanation:
To validate the data on a unified profile, the consultant can use the following features:
Identity Resolution: This feature allows the consultant to view and edit the identity resolution rulesets that determine how individuals are unified from different data sources1.
Data Explorer: This feature allows the consultant to browse and filter the unified profiles and view their attributes, segments, and activities2.
Profile Explorer: This feature allows the consultant to drill down into a specific unified profile and view its details, such as source records, identity graph, calculated insights, and data actions3. References:
1: Identity Resolution in Data Cloud
2: Data Explorer in Data Cloud
3: Profile Explorer in Data Cloud
NEW QUESTION # 67
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