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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Act on Data | 18% | - Troubleshooting activations - Attribute usage and management - Process automation and triggering - Activation concepts and use cases |
| Topic 2: Identity Resolution | 14% | - Unified profile creation - Matching and reconciliation rules - Conflict resolution - Identity use cases |
| Topic 3: Data Cloud Overview | 18% | - Architecture and ecosystem - Business value and use cases - Core concepts and terminology |
| Topic 4: Data Cloud Setup and Administration | 12% | - Data streams and data bundles - Data spaces and configuration - Monitoring and maintenance tools - Permissions and access control |
| Topic 5: Data Ingestion and Modeling | 20% | - Transformation and mapping - Ingestion from various sources - Data modeling best practices - Data validation and inspection |
| Topic 6: Segmentation and Insights | 18% | - Segment membership analysis - Calculated vs streaming insights - Insight implementation - Segment creation and configuration |
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NEW QUESTION # 56
Which two objects or fields are supported for ingestion using the Salesforce CRM connector?
Answer: A,C
Explanation:
These two objects or fields are supported for ingestion using the Salesforce CRM connector. You can select standard or custom objects from your Salesforce CRM org and map them to Data Cloud data model objects. Reference: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_salesforce_crm.htm&type=5
NEW QUESTION # 57
Northern Trail Outfitters wants to be able to calculate each customer's lifetime value (LTV) but also create breakdowns of the revenue sourced by website, mobile app, and retail channels. How should this use case be addressed in Data Cloud?
Answer: C
Explanation:
This feature can help Northern Trail Outfitters calculate each customer's lifetime value (LTV) and create breakdowns of the revenue sourced by different channels. Streaming data transformations allow you to transform and enrich streaming data from different sources using formulas and operators. Reference: https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_streaming_data_transform.htm&type=5
NEW QUESTION # 58
What should an organization use to stream inventory levels from an inventory management system into Data Cloud in a fast and scalable, near-real-time way?
Answer: B
Explanation:
The Ingestion API is a RESTful API that allows you to stream data from any source into Data Cloud in a fast and scalable way. You can use the Ingestion API to send data from your inventory management system into Data Cloud as JSON objects, and then use Data Cloud to create data models, segments, and insights based on your inventory data. The Ingestion API supports both batch and streaming modes, and can handle up to
100,000 records per second. The Ingestion API also provides features such as data validation, encryption, compression, and retry mechanisms to ensure data quality and security. References: Ingestion API Developer Guide, Ingest Data into Data Cloud
NEW QUESTION # 59
How can attribute names be modified to match a naming convention in CloudFile Storage target?
Answer: C
Explanation:
You can modify the attribute names in the data model by editing the field label or API name of the corresponding field. This will change how the attribute name appears in the Cloud File Storage target.
References:https://help.salesforce.com/s/articleView?id=sf.c360_a_data_cloud_data_model.htm&type=5
NEW QUESTION # 60
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,C
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 # 61
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