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NEW QUESTION # 35
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. Reference: Ingest Data from Amazon S3, Refresh Modes
NEW QUESTION # 36
A customer requests that their personal data be deleted.
Which action should the consultant take to accommodate this request in Data Cloud?
Answer: C
Explanation:
Explanation
The Data Rights Subject Request tool is a feature that allows Data Cloud users to manage customer requests for data access, deletion, or portability. The tool provides a user interface and an API to create, track, and fulfill data rights requests. The tool also generates a report that contains the customer's personal data and the actions taken to comply with the request. The consultant should use this tool to accommodate the customer's request for data deletion in Data Cloud. References: Data Rights Subject Request Tool, Create a Data Rights Subject Request
NEW QUESTION # 37
How does Data Cloud ensure high availability and fault tolerance for customer data?
Answer: A
Explanation:
* Ensuring High Availability and Fault Tolerance:
High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.
Reference:
* Data Distribution Across Multiple Regions and Data Centers:
Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.
If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.
* Benefits of Regional Data Distribution:
Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.
Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.
Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.
* Implementation in Salesforce Data Cloud:
Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.
This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.
NEW QUESTION # 38
A customer needs to integrate in real time with Salesforce CRM.
Which feature accomplishes this requirement?
Answer: D
Explanation:
The correct answer is A. Streaming transforms. Streaming transforms are a feature of Data Cloud that allows real-time data integration with Salesforce CRM. Streaming transforms use the Data Cloud Streaming API to synchronize micro-batches of updates between the CRM data source and Data Cloud in near-real time1. Streaming transforms enable Data Cloud to have the most current and accurate CRM data for segmentation and activation2.
The other options are incorrect for the following reasons:
B . Data model triggers. Data model triggers are a feature of Data Cloud that allows custom logic to be executed when data model objects are created, updated, or deleted3. Data model triggers do not integrate data with Salesforce CRM, but rather manipulate data within Data Cloud.
C . Sales and Service bundle. Sales and Service bundle is a feature of Data Cloud that allows pre-built data streams, data model objects, segments, and activations for Sales Cloud and Service Cloud data sources4. Sales and Service bundle does not integrate data in real time with Salesforce CRM, but rather ingests data at scheduled intervals.
D . Data actions and Lightning web components. Data actions and Lightning web components are features of Data Cloud that allow custom user interfaces and workflows to be built and embedded in Salesforce applications5. Data actions and Lightning web components do not integrate data with Salesforce CRM, but rather display and interact with data within Salesforce applications.
Reference:
1: Load Data into Data Cloud
2: [Data Streams in Data Cloud]
3: [Data Model Triggers in Data Cloud] unit on Trailhead
4: [Sales and Service Bundle in Data Cloud] unit on Trailhead
5: [Data Actions and Lightning Web Components in Data Cloud] unit on Trailhead
6: [Data Model in Data Cloud] unit on Trailhead
7: [Create a Data Model Object] article on Salesforce Help
8: [Data Sources in Data Cloud] unit on Trailhead
9: [Connect and Ingest Data in Data Cloud] article on Salesforce Help
10: [Data Spaces in Data Cloud] unit on Trailhead
11: [Create a Data Space] article on Salesforce Help
12: [Segments in Data Cloud] unit on Trailhead
13: [Create a Segment] article on Salesforce Help
14: [Activations in Data Cloud] unit on Trailhead
15: [Create an Activation] article on Salesforce Help
NEW QUESTION # 39
Northern Trail Outfitters uploads new customer data to an Amazon S3 Bucket on a daily basis to be ingested in Data Cloud. Based on this, a calculated insight is created that shows the total spend per customer in the last 30 days.
In which sequence should each process be run to ensure that freshly imported data is ready and available to use for any segment?
Answer: C
Explanation:
To ensure that freshly imported data is ready and available for use in any segment, the processes should be run in the following sequence: Refresh Data Stream > Identity Resolution > Calculated Insight . Here's why:
Understanding the Requirement
Northern Trail Outfitters uploads new customer data daily to an Amazon S3 bucket, which is ingested into Data Cloud.
A calculated insight is created to show the total spend per customer in the last 30 days.
The goal is to ensure that the data is properly refreshed, resolved, and processed before being used in segments.
Why This Sequence?
Step 1: Refresh Data Stream
Before any processing can occur, the data stream must be refreshed to ingest the latest data from the Amazon S3 bucket.
This ensures that the most up-to-date customer data is available in Data Cloud.
Step 2: Identity Resolution
After refreshing the data stream, identity resolution must be performed to merge related records into unified profiles.
This step ensures that customer data is consolidated and ready for analysis.
Step 3: Calculated Insight
Once identity resolution is complete, the calculated insight can be generated to calculate the total spend per customer in the last 30 days.
This ensures that the insight is based on the latest and most accurate data.
Other Options Are Incorrect :
B . Refresh Data Stream > Calculated Insight > Identity Resolution : Calculated insights cannot be generated before identity resolution because they rely on unified profiles.
C . Calculated Insight > Refresh Data Stream > Identity Resolution : Calculated insights require both fresh data and resolved identities, so this sequence is invalid.
D . Identity Resolution > Refresh Data Stream > Calculated Insight : Identity resolution cannot occur without first refreshing the data stream to bring in the latest data.
Conclusion
The correct sequence is Refresh Data Stream > Identity Resolution > Calculated Insight , ensuring that the data is properly refreshed, resolved, and processed before being used in segments.
NEW QUESTION # 40
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