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| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and Data Modeling | - Data sources and ingestion methods - Data model objects and harmonization |
| Topic 2: Identity Resolution and Unification | - Identity resolution rules - Unified customer profile concepts |
| Topic 3: Salesforce Data Cloud Fundamentals | - Core concepts and architecture - Data Cloud value and use cases |
| Topic 4: Data Governance and Privacy | - Data privacy and compliance concepts - Security and data access controls |
| Topic 5: Segmentation and Activation | - Activation to Salesforce and external systems - Segmentation logic and audience building |
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NEW QUESTION # 10
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: B,D
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 # 11
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: C,D,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 # 12
Which operator can be used to create a segment for a birthday campaign that is evaluated daily?
Answer: A
Explanation:
This operator can be used to create a segment for a birthday campaign that is evaluated daily. It compares a date attribute to the current date and returns true if they have the same month and day, regardless of the year.
References:
https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_segmentation_operators.htm&type=5
NEW QUESTION # 13
Which three Data Model objects do Reconciliation Rules operate across?
Answer: B,C,E
Explanation:
These are the three Data Model objects that Reconciliation Rules operate across. Reconciliation rules tell Data Cloud how to prioritize values for unified individual profiles when there is conflicting information in the data model. For example, if two profiles have different email addresses, reconciliation rules can determine which one to use for the unified profile. Reconciliation rules can operate on the Individual object, which represents a person or entity, the Party Identification object, which represents an identifier such as a social media handle or a loyalty card number, and the Contact Point Email object, which represents an email address. Reference: Identity Resolution Reconciliation Rules
NEW QUESTION # 14
Every day, Northern Trail Outfitters uploads a summary of the last 24 hours of store transactions to a new file in an Amazon S3 bucket, and files older than seven days are automatically deleted. Each file contains a timestamp in a standardized naming convention.
Which two options should a consultant configure when ingesting this data stream?
Choose 2 answers
Answer: A,D
Explanation:
When ingesting data from an Amazon S3 bucket, the consultant should configure the following options:
The refresh mode should be set to "Upsert", which means that new and updated records will be added or updated in Data Cloud, while existing records will be preserved. This ensures that the data is always up to date and consistent with the source.
The filename should contain a wildcard to accommodate the timestamp, which means that the file name pattern should include a variable part that matches the timestamp format. For example, if the file name is store_transactions_2023-12-18.csv, the wildcard could be store_transactions_*.csv. This ensures that the ingestion process can identify and process the correct file every day.
The other options are not necessary or relevant for this scenario:
Deletion of old files is a feature of the Amazon S3 bucket, not the Data Cloud ingestion process. Data Cloud does not delete any files from the source, nor does it require the source files to be deleted after ingestion.
Full Refresh is a refresh mode that deletes all existing records in Data Cloud and replaces them with the records from the source file. This is not suitable for this scenario, as it would result in data loss and inconsistency, especially if the source file only contains the summary of the last 24 hours of transactions. References: Ingest Data from Amazon S3, Refresh Modes
NEW QUESTION # 15
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