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>> Data-Con-101 Prüfungsfragen <<

Echte Data-Con-101 Fragen und Antworten der Data-Con-101 Zertifizierungsprüfung

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Salesforce Data-Con-101 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • Data Ingestion and Modeling: This domain addresses bringing data into Data Cloud and structuring it properly through transformation, ingestion from various sources, and data mapping. It emphasizes best practices for modeling data to support identity resolution and validating ingested data using available tools.
Thema 2
  • Segmentation and Insights: This domain centers on creating audience segments and deriving analytical insights from Data Cloud. It includes configuring and maintaining segments, analyzing membership scenarios, and distinguishing between calculated insights and real-time streaming insights.
Thema 3
  • Act on Data: This domain focuses on leveraging Data Cloud data for downstream actions through activations and data actions. It covers working with attributes, managing timing dependencies, troubleshooting activation issues like errors and rejected counts, and understanding requirements for triggering automated processes.
Thema 4
  • Data Cloud Overview: This domain covers the foundational understanding of Data Cloud including its core purpose, terminology, business value, and technical architecture. It also addresses typical use cases and the essential principles of ethical data handling when working with customer data.

Salesforce Certified Data Cloud Consultant Data-Con-101 Prüfungsfragen mit Lösungen (Q127-Q132):

127. Frage
A customer requests that their personal data be deleted.
Which action should the consultant take to accommodate this request in Data Cloud?

Antwort: A


128. Frage
A company wants to include certain personalized fields in an email by including related attributes during the activation in Data Cloud. It notices that some values, such as purchased product names, do not have consistent casing in Marketing Cloud Engagement. For example, purchased product names appear as follows: Jacket, jacket, shoes, SHOES. The company wants to normalize all names to proper case and replace any null values with a default value.
How should a consultant fulfill this requirement within Data Cloud?

Antwort: D

Begründung:
To normalize purchased product names (e.g., converting casing to proper case and replacing null values with a default value) within Salesforce Data Cloud, the best approach is to create a batch data transform that generates a new DLO. Here's the detailed explanation:
Understanding the Problem :The company wants to ensure that product names in Marketing Cloud Engagement are consistent and properly formatted. The inconsistencies in casing (e.g., "Jacket," "jacket,"
"shoes," "SHOES") and the presence of null values need to be addressed before activation.
Why Batch Data Transform?
A batch data transform allows you to process large volumes of data in bulk, making it ideal for cleaning and normalizing datasets.
By creating a new DLO, you ensure that the original data remains intact while providing a clean, transformed dataset for downstream use cases like email personalization.
Steps to Implement This Solution :
Step 1: Navigate to the Data Streams section in Salesforce Data Cloud and identify the data stream containing the purchased product names.
Step 2: Create a new batch data transform by selecting the relevant data stream as the source.
Step 3: Use transformation functions to normalize the product names:
Apply the PROPER() function to convert all product names to proper case.
Use the COALESCE() function to replace null values with a default value (e.g., "Unknown Product").
Step 4: Configure the batch data transform to output the results into a new DLO . This ensures that the transformed data is stored separately from the original dataset.
Step 5: Activate the new DLO for use in Marketing Cloud Engagement. Ensure that the email templates pull product names from the transformed DLO instead of the original dataset.
Why Not Other Options?
A). Create a streaming insight with a data action: Streaming insights are designed for real-time processing and are not suitable for bulk transformations like normalizing casing or replacing null values.
B). Use formula fields when ingesting at the data stream level: Formula fields are useful for simple calculations but are limited in scope and cannot handle complex transformations like null value replacement.
Additionally, modifying the ingestion process may not be feasible if the data stream is already in use.
C). Create one batch data transform per data stream: This approach is inefficient and redundant. Instead of creating multiple transforms, a single batch transform can handle all the required changes and output a unified, clean dataset.
By creating a batch data transform that generates a new DLO, the company ensures that the product names are consistently formatted and ready for use in personalized emails, improving the overall customer experience.


129. Frage
Which solution provides an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis?

Antwort: C

Begründung:
The solution that provides an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis is the Marketing Cloud Data extension Data Stream. The Marketing Cloud Data extension Data Stream is a feature that allows customers to stream data from Marketing Cloud data extensions to Data Cloud data spaces. Customers can select which data extensions they want to stream, and Data Cloud will automatically create and update the corresponding data model objects (DMOs) in the data space.
Customers can also map the data extension fields to the DMO attributes using a user interface or an API. The Marketing Cloud Data extension Data Stream can help customers ingest subscriber profile attributes and other data from Marketing Cloud into Data Cloud without writing any code or setting up any complex integrations.
The other options are not solutions that provide an easy way to ingest Marketing Cloud subscriber profile attributes into Data Cloud on a daily basis. Automation Studio and Profile file API are tools that can be used to export data from Marketing Cloud to external systems, but they require customers to write scripts, configure file transfers, and schedule automations. Marketing Cloud Connect API is an API that can be used to access data from Marketing Cloud in other Salesforce solutions, such as Sales Cloud or Service Cloud, but it does not support streaming data to Data Cloud. Email Studio Starter Data Bundle is a data kit that contains sample data and segments for Email Studio, but it does not contain subscriber profile attributes or stream data to Data Cloud.
Marketing Cloud Data Extension Data Stream
Data Cloud Data Ingestion
[Marketing Cloud Data Extension Data Stream API]
[Marketing Cloud Connect API]
[Email Studio Starter Data Bundle]


130. Frage
During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?

Antwort: A

Begründung:
The feature that the consultant should highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile is D. Identity Resolution. Identity Resolution is the process of identifying, matching, and reconciling data about individuals across different data sources and creating a unified profile that represents a single view of the customer. Identity Resolution uses various methods and rules to determine the best match and reconciliation of data, such as deterministic matching, probabilistic matching, reconciliation rules, and identity graphs. Identity Resolution enables the customer to have a complete and accurate understanding of their customers and their interactions across different channels and touchpoints. References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution


131. Frage
A healthcare client wants to make use of identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII).
Which matching rule criteria should a consultant recommend for the most accurate matching results?

Antwort: D

Begründung:
Identity resolution is the process of linking data from different sources into a unified profile of a customer or an individual. Identity resolution uses matching rules to compare the attributes of different records and determine if they belong to the same person. Matching rules can be based on exact or fuzzy matching of various attributes, such as name, email, phone, address, or custom identifiers. A healthcare client who wants to use identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII), such as name or email, should use a matching rule criteria that is based on a unique and reliable identifier that is specific to the healthcare domain. One such identifier is the patient ID, which is a unique number assigned to each patient by a healthcare provider or system. By using the party identification on patient ID as a matching rule criteria, the healthcare client can ensure that only records that have the same patient ID are matched and unified, and avoid false positives or false negatives that may occur due to common or similar names or emails. The party identification on patient ID is also a secure and compliant way of handling sensitive healthcare data, as it does not expose or share any PII that may be subject to data protection regulations or standards. References: Configure Identity Resolution Rulesets, A framework of identity resolution: evaluating identity attributes and methods


132. Frage
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