Salesforce-Data-Cloud模擬資料 & Salesforce-Data-Cloud認定試験JPNTestのSalesforce-Data-Cloud試験参考書は他のSalesforce-Data-Cloud試験に関連するする参考書よりずっと良いです。これは試験の一発合格を保証できる問題集ですから。この問題集の高い合格率が多くの受験生たちに証明されたのです。JPNTestのSalesforce-Data-Cloud問題集は成功へのショートカットです。この問題集を利用したら、あなたは試験に準備する時間を節約することができるだけでなく、試験で楽に高い点数を取ることもできます。 Salesforce Data Cloud Accredited Professional Exam 認定 Salesforce-Data-Cloud 試験問題 (Q48-Q53):質問 # 48
A consultant is integrating an Amazon 53 activated campaign with the customer's destination system.
In order for the destination system to find the metadata about the segment, which file on the 53 will contain this information for processing?
A. The .txt file
B. The .zip file
C. The .csv file
D. The json file
正解：D
解説：
The file on the Amazon S3 that will contain the metadata about the segment for processing is B. The json file. The json file is a metadata file that is generated along with the csv file when a segment is activated to Amazon S3. The json file contains information such as the segment name, the segment ID, the segment size, the segment attributes, the segment filters, and the segment schedule. The destination system can use this file to identify the segment and its properties, and to match the segment data with the corresponding fields in the destination system. References: Salesforce Data Cloud Consultant Exam Guide, Amazon S3 Activation
質問 # 49
What is a reason to create a formula when ingesting a data stream?
A. To concatenate files so they are ingested in the correct sequence
B. To remove duplicate rows of data from the data stream
C. To add a unique external identifier to an existing ruleset
D. To transform is date time field into a dale field for use in data mapping
正解：D
解説：
Creating a formula during data stream ingestion is often done to manipulate or transform data fields to meet specific requirements. In this case, the most common reason is to transform a date-time field into a date field for use in data mapping . Here's why:
Understanding the Requirement
When ingesting data into Salesforce Data Cloud, certain fields may need to be transformed to align with the target data model.
For example, a date-time field (e.g., "2023-10-05T14:30:00Z") may need to be converted into a date field (e.
g., "2023-10-05") for proper mapping and analysis.
Why Transform a Date-Time Field into a Date Field?
Data Mapping Compatibility :
Some data models or downstream systems may only accept date fields (without the time component).
Transforming the field ensures compatibility and avoids errors during ingestion or activation.
Simplified Analysis :
Removing the time component simplifies analysis and reporting, especially when working with daily trends or aggregations.
Standardization :
Converting date-time fields into consistent date formats ensures uniformity across datasets.
Steps to Implement This Solution
Step 1: Identify the Date-Time Field
During the data stream setup, identify the field that contains the date-time value (e.g., "Order_Date_Time").
Step 2: Create a Formula Field
Use the Formula Field option in the data stream configuration to create a new field.
Apply a transformation function (e.g., DATE() or equivalent) to extract the date portion from the date-time field.
Step 3: Map the Transformed Field
Map the newly created date field to the corresponding field in the target data model (e.g., Unified Profile or Data Lake Object).
Step 4: Validate the Transformation
Test the data stream to ensure the transformation works correctly and the date field is properly ingested.
Why Not Other Options?
A). To concatenate files so they are ingested in the correct sequence :Concatenation is not a typical use case for formulas during ingestion. File sequencing is usually handled at the file ingestion level, not through formulas.
B). To add a unique external identifier to an existing ruleset :Adding a unique identifier is typically done during data preparation or identity resolution, not through formulas during ingestion.
D). To remove duplicate rows of data from the data stream :Removing duplicates is better handled through deduplication rules or transformations, not formulas.
Conclusion
The primary reason to create a formula when ingesting a data stream is to transform a date-time field into a date field for use in data mapping . This ensures compatibility, simplifies analysis, and standardizes the data for downstream use.
質問 # 50
A consultant wants to ensure that every segment managed by multiple brand teams adheres to the same set of exclusion criteria, that are updated on a monthly basis.
What is the most efficient option to allow for this capability?
A. Create a segment and copy it for each brand.
B. Create, publish, and deploy a data kit.
C. Create a nested segment.
D. Create a reusable container block with common criteria.
正解：D
解説：
The most efficient option to allow for this capability is to create a reusable container block with common criteria. A container block is a segment component that can be reused across multiple segments. A container block can contain any combination of filters, nested segments, and exclusion criteria. A consultant can create a container block with the exclusion criteria that apply to all the segments managed by multiple brand teams, and then add the container block to each segment. This way, the consultant can update the exclusion criteria in one place and have them reflected in all the segments that use the container block.
The other options are not the most efficient options to allow for this capability. Creating, publishing, and deploying a data kit is a way to share data and segments across different data spaces, but it does not allow for updating the exclusion criteria on a monthly basis. Creating a nested segment is a way to combine segments using logical operators, but it does not allow for excluding individuals based on specific criteria. Creating a segment and copying it for each brand is a way to create multiple segments with the same exclusion criteria, but it does not allow for updating the exclusion criteria in one place.
Create a Container Block
Create a Segment in Data Cloud
Create and Publish a Data Kit
Create a Nested Segment
質問 # 51
What happens if no file name is specified in AWS S3 data stream during ingestion?
A. The system chooses the first file found in the S3 bucket
B. The system does not fetch any file and the data stream shows an error.
C. The ingestion setup is completed but the data stream shows 0 records
D. The ingestion setup can't be completed without specifying the filename.
正解：B
解説：
If no file name is specified in AWS S3 data stream during ingestion, the system does not fetch any file and the data stream shows an error. AWS S3 data stream is a feature that allows you to stream data from Amazon Web Services Simple Storage Service (AWS S3) to Data Cloud in near real time. You need to specify the file name or prefix of the files that you want to ingest from your S3 bucket. If you leave this field blank, the system cannot find any matching files and returns an error message. Reference: AWS S3 Data Stream
質問 # 52
Which two statements about Data Cloud's Web and Mobile App connector are true?
A. Mobile and Web SDK schema can be updated todelete an existing field
B. Tenant Specific Endpoint is auto-generated in Data Cloud when setting up a Mobile or Web app connection
C. Data Cloud administrators can see the status of a Web or Mobile connector app on the app details page
D. Any Data Streams associated with Web or Mobile connector app will be automatically deleted upon deleting the app from Data Cloud Setup
正解：B、C
解説：
The app details page shows the status of the app, such as active, inactive, or error. The tenant specific endpoint is a unique URL that is generated for each app and used to send data to Data Cloud from the web or mobile SDK.
References:https://help.salesforce.com/s/ar ... ctor.htm&type=5