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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Data Cloud Consultant |
| Exam Number: | Data-Con-101 |
| Available Languages: | English |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Duration: | 105 minutes |
| Passing Score: | 68% |
| Exam Price: | $200 USD |
| Related Certifications: | Salesforce Certified Administrator Salesforce Platform App Builder Salesforce Marketing Cloud Engagement |
| Real Exam Qty: | 60-65 |
| Certificate Validity Period: | Maintenance required annually (Trailhead maintenance program) |
| Recommended Training: | Salesforce Partner Training Resources Salesforce Trailhead - Data Cloud Learning Paths |
| Exam Registration: | Salesforce Credentials Portal |
| Sample Questions: | Salesforce Data-Con-101 Sample Questions |
| Exam Way: | Online proctored exam or test center delivery |
| Pre Condition: | No strict prerequisites; recommended to have Salesforce Administrator knowledge or equivalent experience. |
Die Schulungsunterlagen zur Salesforce Data-Con-101 Zertifizierungsprüfung von ExamFragen sind am besten. Wir sind bei den Kandidaten sehr beliebt. Wenn Sie die Schulungsunterlagen zur Salesforce Data-Con-101 Zertifizierungsprüfung von ExamFragen zur ExamFragen benutzen, geben wir Ihnen eine 100%-Pass-Garantie. Sonst erstatteten wir Ihnen die gammte Summe zurück, um Ihre Interessen zu schützen. Unser ExamFragen ist ganz zuverlässig.
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27. Frage
Northern Trail Outfitters is using the Marketing Cloud Starter Data Bundles to bring Marketing Cloud data into Data Cloud.
What are two of the available datasets in Marketing Cloud Starter Data Bundles?
Choose 2 answers
Antwort: C,D
Begründung:
The Marketing Cloud Starter Data Bundles are predefined data bundles that allow you to easily ingest data from Marketing Cloud into Data Cloud1. The available datasets in Marketing Cloud Starter Data Bundles are Email, MobileConnect, and MobilePush2. These datasets contain engagement events and metrics from different Marketing Cloud channels, such as email, SMS, and push notifications2. By using these datasets, you can enrich your Data Cloud data model with Marketing Cloud data and create segments and activations based on your marketing campaigns and journeys1. The other options are incorrect because they are not available datasets in Marketing Cloud Starter Data Bundles. Option A is incorrect because Personalization is not a dataset, but a feature of Marketing Cloud that allows you to tailor your content and messages to your audience3. Option C is incorrect because Loyalty Management is not a dataset, but a product of Marketing Cloud that allows you to create and manage loyalty programs for your customers4. References: Marketing Cloud Starter Data Bundles in Data Cloud, Connect Your Data Sources, Personalization in Marketing Cloud, Loyalty Management in Marketing Cloud
28. Frage
A customer has a custom Customer Email c object related to the standard Contact object in Salesforce CRM.
This custom object
stores the email address a Contact that they want to use for activation.
To which data entity is mapped?
Antwort: D
Begründung:
The Contact Point_Email object is the data entity that represents an email address associated with an individual in Data Cloud. It is part of the Customer 360 Data Model, which is a standardized data model that defines common entities and relationships for customer data. The Contact Point_Email object can be mapped to any custom or standard object that stores email addresses in Salesforce CRM, such as the custom Customer Email__c object. The other options are not the correct data entities to map to because:
A). The Contact object is the data entity that represents a person who is associated with an account that is a customer, partner, or competitor in Salesforce CRM. It is not the data entity that represents an email address in Data Cloud.
C). The custom Customer Email__c object is not a data entity in Data Cloud, but a custom object in Salesforce CRM. It can be mapped to a data entity in Data Cloud, such as the Contact Point_Email object, but it is not a data entity itself.
D). The Individual object is the data entity that represents a unique person in Data Cloud. It is the core entity for managing consent and privacy preferences, and it can be related to one or more contact points, such as email addresses, phone numbers, or social media handles. It is not the data entity that represents an email address in Data Cloud. References: Customer 360 Data Model: Individual and Contact Points - Salesforce, Contact Point_Email | Object Reference for the Salesforce Platform | Salesforce Developers,
[Contact | Object Reference for the Salesforce Platform | Salesforce Developers], [Individual | Object Reference for the Salesforce Platform | Salesforce Developers]
29. Frage
Which statement is true related to batch ingestions from Salesforce CRM?
Antwort: A
Begründung:
The question asks which statement is true about batch ingestions from Salesforce CRM into Salesforce Data Cloud. Batch ingestion refers to the process of periodically syncing data from Salesforce CRM (e.g., Accounts, Contacts, Opportunities) into Data Cloud. The focus is on how the CRM connector handles changes in data structure (e.g., adding or removing columns) and synchronization behavior.
Why A is Correct: "When a column is added or removed, the CRM connector performs a full refresh." Behavior of the CRM Connector :
The Salesforce CRM connector automatically detects schema changes, such as when a field (column) is added or removed in the source CRM object.
When such changes occur, the CRM connector triggers a full refresh of the data for that object. This ensures that the data model in Data Cloud aligns with the updated schema in Salesforce CRM.
Why a Full Refresh is Necessary :
A full refresh ensures that all records are re-ingested with the updated schema, avoiding inconsistencies or missing data caused by incremental updates.
Incremental updates only capture changes (e.g., new or modified records), so they cannot handle schema changes effectively.
Other Options Are Incorrect :
B). The CRM connector performs an incremental refresh when 600K or more deletion records are detected :
This is incorrect because the CRM connector does not switch to incremental refresh based on the number of deletion records. It always performs incremental updates unless a schema change triggers a full refresh.
C). The CRM connector's synchronization times can be customized to up to 15-minute intervals : While synchronization schedules can be customized, the minimum interval is typically 1 hour , not 15 minutes.
D). CRM data cannot be manually refreshed and must wait for the next scheduled synchronization : This is incorrect because users can manually trigger a refresh of CRM data in Data Cloud if needed.
Steps to Understand CRM Connector Behavior
Step 1: Schema Changes Trigger Full Refresh
If a field is added or removed in Salesforce CRM, the CRM connector detects this change and initiates a full refresh of the corresponding object in Data Cloud.
Step 2: Incremental Updates for Regular Syncs
For regular synchronization, the CRM connector performs incremental updates, capturing only new or modified records since the last sync.
Step 3: Manual Refresh Option
Users can manually trigger a refresh in Data Cloud if immediate synchronization is required, bypassing the scheduled sync.
Step 4: Monitor Synchronization Logs
Use the Data Cloud Monitoring tools to track synchronization status, including full refreshes and incremental updates.
Conclusion
The statement "When a column is added or removed, the CRM connector performs a full refresh" is true. This behavior ensures that the data model in Data Cloud remains consistent with the schema in Salesforce CRM, avoiding potential data integrity issues.
30. Frage
A global fashion retailer operates online sales platforms across AMFR, FMFA, and APAC. the data formats for customer, order, and product Information vary by region, and compliance regulations require data to remain unchanged in the original data sources They also require a unified view of customer profiles for real- time personalization and analytics.
Given these requirement, which transformation approach should the company implement to standardise and cleanse incoming data streams?
Antwort: D
Begründung:
Given the requirements to standardize and cleanse incoming data streams while keeping the original data unchanged in compliance with regional regulations, the best approach is to implement batch data transformations . Here's why:
Understanding the Requirements
The global fashion retailer operates across multiple regions (AMER, EMEA, APAC), each with varying data formats for customer, order, and product information.
Compliance regulations require the original data to remain unchanged in the source systems.
The company needs a unified view of customer profiles for real-time personalization and analytics.
Why Batch Data Transformations?
Batch Transformations for Standardization :
Batch data transformations allow you to process large volumes of data at scheduled intervals.
They can standardize and cleanse data (e.g., converting different date formats, normalizing product names) without altering the original data in the source systems.
Compliance with Regulations :
Since the original data remains unchanged in the source systems, batch transformations comply with regional regulations.
The transformed data is stored in a separate layer (e.g., a new Data Lake Object or Unified Profile) for downstream use.
Unified Customer Profiles :
After transformation, the cleansed and standardized data can be used to create a unified view of customer profiles in Salesforce Data Cloud.
This enables real-time personalization and analytics across regions.
Steps to Implement This Solution
Step 1: Identify Transformation Needs
Analyze the differences in data formats across regions (e.g., date formats, currency, product IDs).
Define the rules for standardization and cleansing (e.g., convert all dates to ISO format, normalize product names).
Step 2: Create Batch Transformations
Use Data Cloud's Batch Transform feature to apply the defined rules to incoming data streams.
Schedule the transformations to run at regular intervals (e.g., daily or hourly).
Step 3: Store Transformed Data Separately
Store the transformed data in a new Data Lake Object (DLO) or Unified Profile.
Ensure the original data remains untouched in the source systems.
Step 4: Enable Unified Profiles
Use the transformed data to create a unified view of customer profiles in Salesforce Data Cloud.
Leverage this unified view for real-time personalization and analytics.
Why Not Other Options?
A). Implement streaming data transformations :Streaming transformations are designed for real-time processing but may not be suitable for large-scale standardization and cleansing tasks. Additionally, they might not align with compliance requirements to keep the original data unchanged.
C). Transform data before ingesting into Data Cloud :Transforming data before ingestion would require modifying the original data in the source systems, violating compliance regulations.
D). Use Apex to transform and cleanse data :Using Apex is overly complex and resource-intensive for this use case. Batch transformations are a more efficient and scalable solution.
Conclusion
By implementing batch data transformations , the global fashion retailer can standardize and cleanse its data while complying with regional regulations and enabling a unified view of customer profiles for real-time personalization and analytics.
31. Frage
Cumulus Financial uses Data Cloud to segment banking customers and activate them for direct mail via a Cloud File Storage activation. The company also wants to analyze individuals who have been in the segment within the last 2 years.
Which Data Cloud component allows for this?
Antwort: C
Begründung:
The segment membership data model object is a Data Cloud component that allows for analyzing individuals who have been in a segment within a certain time period. The segment membership data model object is a table that stores the information about which individuals belong to which segments and when they were added or removed from the segments. This object can be used to create calculated insights, such as segment size, segment duration, segment overlap, or segment retention, that can help measure the effectiveness of segmentation and activation strategies. The segment membership data model object can also be used to create nested segments or segment exclusions based on the segment membership criteria, such as segment name, segment type, or segment date range. The other options are not correct because they are not Data Cloud components that allow for analyzing individuals who have been in a segment within the last 2 years. Nested segments and segment exclusions are features that allow for creating more complex segments based on existing segments, but they do not provide the historical data about segment membership. Calculated insights are custom metrics or measures that are derived from data model objects or data lake objects, but they do not store the segment membership information by themselves. References: Segment Membership Data Model Object, Create a Calculated Insight, Create a Nested Segment
32. Frage
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P.S. Kostenlose und neue Data-Con-101 Prüfungsfragen sind auf Google Drive freigegeben von ExamFragen verfügbar: https://drive.google.com/open?id=1OyPv7-LLioEF03vT6W_LFZqHVaAs_nQB