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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion | 15% | - Configure streaming data ingestion - Configure data sync and refresh schedules - Set up and manage data connectors - Troubleshoot ingestion issues - Configure batch data ingestion |
| Topic 2: Data Model Setup and Configuration | 15% | - Describe Harmonized Data Model concepts - Set up and configure Data Lake objects - Understand data types and field mapping - Map source data to harmonized data model - Configure custom objects, fields, and relationships |
| Topic 3: Identity Resolution and Data Unification | 17% | - Understand unified records creation - Configure contact and account matching - Configure match rules and strategies - Manage data overlap and deduplication - Configure identity resolution rules |
| Topic 4: Data Sharing and Security | 8% | - Manage user access and permissions - Configure data sharing settings - Implement row-level security - Understand data governance requirements |
| Topic 5: Data Actions | 8% | - Create and manage data bundles - Set up segmentation and target audiences - Configure data actions for activation - Configure data sync to external systems |
| Topic 6: Data Cloud Monitoring and Troubleshooting | 22% | - Debug common configuration errors - Monitor data ingestion and sync status - Troubleshoot data quality issues - Use Data Cloud analytics and reports - Manage data retention and cleanup - Analyze identity resolution results |
| Topic 7: Calculated Insights | 8% | - Use formulas and functions in calculations - Publish insights to Data Cloud - Create and configure calculated insights - Define metrics and measurements |
| Topic 8: Data Cloud Overview and Licensing | 7% | - Describe Data Cloud setup and configuration - Describe the business value of Data Cloud - Explain licensing model and capacity |
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NEW QUESTION # 86
A new user of Data Cloud only needs to be able to review individual rows of ingested data and validate that it has been modeled successfully to its linked data model object. The user will also need to make changes if required.
What is the minimum permission set needed to accommodate this use case?
Answer: D
Explanation:
Explanation
The Data Cloud User permission set is the minimum permission set needed to accommodate this use case. The Data Cloud User permission set grants access to the Data Explorer feature, which allows the user to review individual rows of ingested data and validate that it has been modeled successfully to its linked data model object. The user can also make changes to the data model object fields, such as adding or removing fields, changing field types, or creating formula fields. The Data Cloud User permission set does not grant access to other Data Cloud features or tasks, such as creating data streams, creating segments, creating activations, or managing users. The other permission sets are either too restrictive or too permissive for this use case. The Data Cloud for Marketing Specialist permission set only grants access to the segmentation and activation features, but not to the Data Explorer feature. The Data Cloud Admin permission set grants access to all Data Cloud features and tasks, including the Data Explorer feature, but it is more than what the user needs. The Data Cloud for Marketing Data Aware Specialist permission set grants access to the Data Explorer feature, but also to the segmentation and activation features, which are not required for this use case. References: Data Cloud Standard Permission Sets, Data Explorer, Set Up Data Cloud Unit
NEW QUESTION # 87
A customer has two Data Cloud orgs. A new configuration has been completed and tested for an Amazon S3 data stream and its mappings in one of the Data Cloud orgs.
What is recommended to package and promote this configuration to the customer's second org?
Answer: D
Explanation:
* Data Cloud Configuration Promotion: When managing configurations across multiple Salesforce Data Cloud orgs, it's essential to use tools that ensure consistency and accuracy in the promotion process.
* Data Kits: Salesforce Data Cloud allows users to package and promote configurations using data kits. These kits encapsulate data stream definitions, mappings, and other configuration elements into a portable format.
* Process:
Create a data kit in the source org that includes the Amazon S3 data stream configuration and mappings.
Export the data kit from the source org.
Import the data kit into the target org, ensuring that all configurations are transferred accurately.
* Advantages: Using data kits simplifies the migration process, reduces the risk of configuration errors, and ensures that all settings and mappings are consistently applied in the new org.
* Reference:
Salesforce Data Cloud Developer Guide
Salesforce Data Cloud Packaging
NEW QUESTION # 88
What does it mean to build a trust-based, first-party data asset?
Answer: D
Explanation:
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 # 89
A consultant is discussing the benefits of Data Cloud with a customer that has multiple disjointed data sources.
Which two functional areas should the consultant highlight in relation to managing customer data?
Choose 2 answers
Answer: A,C
Explanation:
Data Cloud is an open and extensible data platform that enables smarter, more efficient AI with secure access to first-party and industry data1. Two functional areas that the consultant should highlight in relation to managing customer data are:
* Data Harmonization: Data Cloud harmonizes data from multiple sources and formats into a common schema, enabling a single source of truth for customer data1. Data Cloud also applies data quality rules and transformations to ensure data accuracy and consistency.
* Unified Profiles: Data Cloud creates unified profiles of customers and prospects by linking data across different identifiers, such as email, phone, cookie, and device ID1. Unified profiles provide a holistic view of customer behavior, preferences, and interactions across channels and touchpoints. The other options are not correct because:
* Master Data Management: Master Data Management (MDM) is a process of creating and maintaining a single, consistent, and trusted source of master data, such as product, customer, supplier, or location data. Data Cloud does not provide MDM functionality, but it can integrate with MDM solutions to enrich customer data.
* Data Marketplace: Data Marketplace is a feature of Data Cloud that allows users to discover, access, and activate data from third-party providers, such as demographic, behavioral, and intent data. Data Marketplace is not a functional area related to managing customer data, but rather a source of external data that can enhance customer data. References:
* Salesforce Data Cloud
* [Data Harmonization for Data Cloud]
* [Unified Profiles for Data Cloud]
* [What is Master Data Management?]
* [Integrate Data Cloud with Master Data Management]
* [Data Marketplace for Data Cloud]
NEW QUESTION # 90
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud.
In what order should each process be run to ensure that freshly imported data is ready and available to use for any segment?
Answer: C
Explanation:
Explanation
To ensure that freshly imported data from an Amazon S3 Bucket is ready and available to use for any segment, the following processes should be run in this order:
* Refresh Data Stream: This process updates the data lake objects in Data Cloud with the latest data from the source system. It can be configured to run automatically or manually, depending on the data stream settings1. Refreshing the data stream ensures that Data Cloud has the most recent and accurate data from the Amazon S3 Bucket.
* Identity Resolution: This process creates unified individual profiles by matching and consolidating source profiles from different data streams based on the identity resolution ruleset. It runs daily by default, but can be triggered manually as well2. Identity resolution ensures that Data Cloud has a single view of each customer across different data sources.
* Calculated Insight: This process performs calculations on data lake objects or CRM data and returns a result as a new data object. It can be used to create metrics or measures for segmentation or analysis purposes3. Calculated insights ensure that Data Cloud has the derived data that can be used for personalization or activation.
References:
* 1: Configure Data Stream Refresh and Frequency - Salesforce
* 2: Identity Resolution Ruleset Processing Results - Salesforce
* 3: Calculated Insights - Salesforce
NEW QUESTION # 91
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