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Salesforce Data-Cloud-Consultant 認定試験の出題範囲：
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出題範囲
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Salesforce Certified Data Cloud Consultant 認定 Data-Cloud-Consultant 試験問題 (Q25-Q30):
質問 # 25
How can a consultant modify attribute names to match a naming convention in Cloud File Storage targets?
正解：C
解説：
A Cloud File Storage target is a type of data action target in Data Cloud that allows sending data to a cloud storage service such as Amazon S3 or Google Cloud Storage. When configuring an activation to a Cloud File Storage target, a consultant can modify the attribute names to match a naming convention by setting preferred attribute names in Data Cloud. Preferred attribute names are aliases that can be used to control the field names in the target file. They can be set for each attribute in the activation configuration, and they will override the default field names from the data model object. The other options are incorrect because they do not affect the field names in the target file. Using a formula field to update the field name in an activation will not change the field name, but only the field value. Updating attribute names in the data stream configuration will not affect the existing data lake objects or data model objects. Updating field names in the data model object will change the field names for all data sources and activations that use the object, which may not be desirable or consistent. Reference: Preferred Attribute Name, Create a Data Cloud Activation Target, Cloud File Storage Target
質問 # 26
What is a reason to create a formula when ingesting a data stream?
正解：A
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 # 27
A financial services firm specializing in wealth management contacts a Data Cloud consultant with an identity resolution request. The company wants to enhance its strategy to better manage individual client profiles within family portfolios.
Family members often share addresses and sometimes phone numbers but have distinct investment preferences and financial goals. The firm aims to avoid blending individual family profiles into a single entity to maintain personalized service and accurate financial advice.
Which identity resolution strategy should the consultant put in place?
正解：D
解説：
To manage individual client profiles within family portfolios while avoiding blending profiles, the consultant should recommend a more restrictive design approach for identity resolution. Here's why:
Understanding the Requirement
The financial services firm wants to maintain distinct profiles for individual family members despite shared contact points (e.g., address, phone number).
The goal is to avoid blending profiles to ensure personalized service and accurate financial advice.
Why a Restrictive Design Approach?
Avoiding Over-Matching :
A restrictive design approach ensures that match rules are narrowly defined to prevent over-matching (e.g., merging profiles based solely on shared addresses or phone numbers).
This preserves the uniqueness of individual profiles while still allowing for some shared attributes.
Custom Match Rules :
The consultant can configure custom match rules that prioritize unique identifiers (e.g., email, social security number) over shared contact points.
This ensures that family members with shared addresses or phone numbers remain distinct.
Other Options Are Less Suitable :
A . Configure a single match rule with a single connected contact point based on address : This would likely result in over-matching and blending profiles, which is undesirable.
B . Use multiple contact points without individual attributes in the match rules : This approach lacks the precision needed to maintain distinct profiles.
D . Configure a single match rule based on a custom identifier : While custom identifiers are useful, relying on a single rule may not account for all scenarios and could lead to over-matching.
Steps to Implement the Solution
Step 1: Analyze Shared Attributes
Identify shared attributes (e.g., address, phone number) and unique attributes (e.g., email, social security number).
Step 2: Define Restrictive Match Rules
Configure match rules that prioritize unique attributes and minimize reliance on shared contact points.
Step 3: Test Identity Resolution
Test the match rules to ensure that individual profiles are preserved while still allowing for some shared attributes.
Step 4: Monitor and Refine
Continuously monitor the results and refine the match rules as needed to achieve the desired outcome.
Conclusion
A more restrictive design approach ensures that match rules perform as desired, preserving the uniqueness of individual profiles while accommodating shared attributes within family portfolios.
質問 # 28
Luxury Retailers created a segment targeting high value customers that it activates through Marketing Cloud for email communication. The company notices that the activated count is smaller than the segment count.
What is a reason for this?
正解：C
解説：
The reason for the activated count being smaller than the segment count is A. Data Cloud enforces the presence of Contact Point for Marketing Cloud activations. If the individual does not have a related Contact Point, it will not be activated. A Contact Point is a data model object that represents a channel or method of communication with an individual, such as email, phone, or social media. For Marketing Cloud activations, Data Cloud requires that the individual has a related Contact Point of type Email, which contains a valid email address. If the individual does not have such a Contact Point, or if the Contact Point is missing or invalid, the individual will not be activated and will not receive the email communication. Therefore, the activated count may be lower than the segment count, depending on how many individuals in the segment have a valid email Contact Point. References: Salesforce Data Cloud Consultant Exam Guide, Contact Point, Marketing Cloud Activation
質問 # 29
A bank collects customer data for its loan applicants and high net worth customers. A customer can be both a load applicant and a high net worth customer, resulting in duplicate data.
How should a consultant ingest and map this data in Data Cloud?
正解：B
解説：
To handle duplicate data for customers who are both loan applicants and high net worth individuals, the consultant should ingest the data into two separate Data Lake Objects (DLOs) and map them to the Individual and Contact Point Email Data Model Objects (DMOs). Here's why and how this works:
Understanding the Problem :
Customers may exist in both datasets (loan applicants and high net worth individuals), leading to potential duplication.
To avoid redundancy while maintaining data integrity, the data must be ingested and mapped carefully.
Why Two DLOs?
By ingesting the data into two DLOs, you can maintain separation between the two datasets while still leveraging shared attributes (e.g., email addresses).
Mapping both DLOs to the Individual and Contact Point Email DMOs ensures that identity resolution can consolidate duplicate records based on shared identifiers like email.
Steps to Implement This Solution :
Step 1: Create two DLOs-one for loan applicants and another for high net worth customers.
Step 2: Map both DLOs to the Individual DMO to consolidate customer profiles.
Step 3: Map the email fields from both DLOs to the Contact Point Email DMO to enable identity resolution based on email addresses.
Step 4: Configure identity resolution rules to merge duplicate records based on shared attributes like email.
Why Not Other Options?
A . Use a data transform to consolidate the data into one DLO: Consolidating into a single DLO before mapping would lose the distinction between the two datasets and make it harder to manage updates or changes.
C . Ingest the data into two DLOs and then map to two custom DMOs: Creating custom DMOs is unnecessary complexity when the standard Individual and Contact Point Email DMOs can handle this scenario.
D . Ingest the data into one DLO and then map to one custom DMO: Using a single DLO would result in data loss or confusion, as the distinction between loan applicants and high net worth customers would be lost.
By using two DLOs and mapping them to the standard DMOs, the consultant ensures clean data ingestion and effective identity resolution.
質問 # 30
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