EXAM C-C4H63-2411 COLLECTION PDF | EXAM C-C4H63-2411 GUIDE MATERIALS

Exam C-C4H63-2411 Collection Pdf | Exam C-C4H63-2411 Guide Materials

Exam C-C4H63-2411 Collection Pdf | Exam C-C4H63-2411 Guide Materials

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SAP C-C4H63-2411 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Ingestion: Data ingestion is a core component of customer data platforms, and assesses the consultant's expertise in this area. It covers various methods and tools for importing data from multiple sources into the SAP Customer Data Platform.
Topic 2
  • Data Privacy: This section focuses on data privacy considerations in customer data management. SAP Customer Data Solutions Consultants should be well-versed in data protection regulations and privacy-enhancing technologies. The exam may evaluate the ability to implement privacy-compliant data handling processes and ensure adherence to relevant laws and standards.
Topic 3
  • Implementing B2B: In this final section, the main focus is on implementing SAP Customer Data Solutions in business-to-business (B2B) contexts. SAP Customer Data Solutions Consultants should understand the unique challenges and requirements of B2B customer data management.
Topic 4
  • SAP Customer Data Platform: SAP Customer Data Solutions Consultants are expected to demonstrate comprehensive knowledge of the SAP Customer Data Platform. This domain covers the platform's core functionalities, including data collection, unification, and activation.

SAP Certified Associate - Implementation Consultant - SAP Customer Data Platform Sample Questions (Q62-Q67):

NEW QUESTION # 62
What kinds of prediction can you select for a predictive indicator? Note: There are 2 correct answers to this question.

  • A. Customer lifetime value
  • B. Customer order value
  • C. Customer volume
  • D. Customer churn

Answer: A,D

Explanation:
Within the SAP Customer Data Platform, predictive indicators are designed to make predictions based on customer profile or group and activity data. Specifically, you can select predictions for Customer lifetime value (CLV) and Customer churn. The CLV prediction is about forecasting the potential revenue a customer will generate over their lifetime, while the churn prediction estimates the likelihood of a customer ceasing to use a company's services or products1.
Reference = This information is derived from the SAP Help Portal documentation, which details the types of predictions you can configure for predictive indicators within the SAP Customer Data Platform, including churn probabilities for profiles and groups, as well as customer lifetime value for profiles and groups1.


NEW QUESTION # 63
What is the purpose of the Connector Studio?

  • A. It enables the setup of ETL jobs and data pipelines for data ingestion.
  • B. It enables activation of preconfigured application connectors.
  • C. It enables creation of templates for source/destination REST API applications.
  • D. It enables activation of newly created audiences.

Answer: C

Explanation:
The purpose of the Connector Studio within the SAP Customer Data Platform is to serve as a low-code/no-code solution that enables the creation of integrations between the SAP Customer Data Platform and any REST platform or application. It allows users to create templates for source/destination REST API applications, facilitating the integration process and enabling seamless data flow between different systems.
Reference = This information is derived from the SAP Help Portal documentation on the Connector Studio, which provides a detailed explanation of its role and capabilities in the context of the SAP Customer Data Platform123.


NEW QUESTION # 64
Which of the following are valid data processing reasons when creating processing purposes? Note: There are 3 correct answers to this question.

  • A. Consent
  • B. Data ingestion
  • C. Contract
  • D. Legal obligation
  • E. Legal interest

Answer: A,C,D

Explanation:
The SAP Customer Data Platform identifies several valid reasons for processing data when creating processing purposes. These include:
Consent: Processing based on the data subject's consent, typically obtained through agreements like terms of service or privacy policies.
Contract: Processing required to fulfill a contract, such as using a customer's address for product delivery.
Legal obligation: Processing necessary to comply with legal requirements.
These reasons ensure that personal data handling within the platform adheres to applicable laws and regulations, such as GDPR and CCPA, and aligns with the principles of data governance enforced by the platform.
Reference = This information is supported by the SAP Help Portal documentation on processing purposes within the SAP Customer Data Platform, which outlines the use cases and core concepts related to data governance enforcement1.


NEW QUESTION # 65
How does outbound Data Governance manage the data that is sent to destination applications in SAP Customer Data Platform?

  • A. By mapping event purpose nodes to the data privacy section
  • B. By including the processing purpose in the action configuration for data distribution
  • C. By combining static and dynamic purposes into customer profiles
  • D. By storing collected processing purposes from external systems

Answer: B


NEW QUESTION # 66
Which of the following merge rules must be defined at attribute level? Note: There are 2 correct answers to this question.

  • A. Decide whether to concatenate the incoming value with the existing value of the same attribute
  • B. Decide whether more than one value can be saved for an attribute
  • C. Decide whether to override existing data, based on the quality of the incoming and existing data
  • D. Decide what to do if a data conflict occurs for an identifier attribute

Answer: A,C

Explanation:
In the SAP Customer Data Platform, merge rules at the attribute level are essential for managing how incoming data is integrated with existing data. For an attribute, you can define whether to concatenate the incoming value with the existing value, which is crucial when you want to preserve all historical data points for a particular attribute. Additionally, you can set rules to decide whether to override existing data with incoming data based on their respective data quality. This ensures that the most accurate and up-to-date information is retained in the customer profile. Reference = Configure Merge Rules Based on Data Quality | SAP Help Portal, Merge Rules | SAP Help Portal In the context of the SAP Customer Data Platform, defining merge rules at the attribute level is crucial for managing how incoming data is integrated with existing customer profiles. Two significant rules that must be specified at this level include:
A . Decide whether to concatenate the incoming value with the existing value of the same attribute: This rule determines how to handle scenarios where an incoming data value for a specific attribute should be added to the existing value(s) for that attribute in the profile, rather than replacing it. Concatenation can be useful for attributes where maintaining a history or collection of values is important, such as addresses or phone numbers.
D . Decide whether to override existing data, based on the quality of the incoming and existing data: This rule involves assessing the quality or reliability of incoming data compared to what is already present in the customer profile and deciding whether the new data should replace the old. This could be based on factors like data source credibility, recency, or completeness, ensuring that the customer profile remains as accurate and up-to-date as possible.
These rules are integral to maintaining the integrity and usefulness of customer data within the platform, ensuring that data merging and updating processes are carried out in a way that enhances the quality of the customer profiles.


NEW QUESTION # 67
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