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SAP Certified Application Associate – SAP Master Data Governance C_MDG_90 Interview Questions

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SAP Certified Application Associate - SAP Master Data Governance C_MDG_90 Interview Questions

After all, if you’ve passed an exam, all you need now is an interview to obtain your desired job. This can help you prepare for an interview, which is an important step in achieving your goals. When it comes to the SAP Certified Application Associate – SAP Master Data Governance C_MDG_90, it’s critical to recognize the importance of both theoretical and practical knowledge. This interview preparation is all about your conceptual understanding and ability to apply that understanding intelligently. As a result, we’ve compiled the top SAP Certified Application Associate – SAP Master Data Governance C MDG 90 interview questions to give you a good sense of what to expect during the interview. This guide contains frequently asked questions that have been examined by experts and will help you prepare effectively for the interview and ace it with flying colors.

Remember that having the right information with a respectable amount of confidence can help you ace the interview. So, look through the following questions carefully and make sure that on the day of the interview, you offer your responses succinctly and assertively. Let’s have a look at the most often asked SAP Certified Application Associate – SAP Master Data Governance C MDG 90 Interview Questions.

Advanced Interview Questions

What is SAP Master Data Governance and why is it important for businesses?

SAP Master Data Governance (MDG) is a solution provided by SAP to manage and govern master data. It provides a centralized system for managing master data entities such as customers, products, suppliers, and others. Master data is an important component of a business’s information system, as it is used in many business processes and transactions.

MDG helps organizations to ensure the quality, accuracy, and consistency of master data across all systems and applications. This is accomplished through a set of tools and processes that are used to manage the creation, modification, and maintenance of master data. The solution also provides a governance framework for managing master data, which includes roles and responsibilities, data quality rules, and data governance processes.

The importance of SAP Master Data Governance for businesses is due to several factors:

  1. Improved Data Quality: By centralizing the management of master data, organizations can ensure that their master data is accurate and consistent across all systems and applications. This helps to reduce the risk of errors and improve the overall quality of the data.
  2. Improved Business Processes: SAP MDG provides a centralized system for managing master data, which helps to streamline business processes and improve the efficiency of operations. This can result in improved customer satisfaction, increased revenue, and reduced costs.
  3. Compliance with Regulatory Requirements: Organizations must comply with various regulatory requirements related to data privacy and security. SAP MDG provides a framework for managing master data that helps organizations to meet these requirements.
  4. Improved Decision-Making: Accurate and consistent master data is essential for effective decision-making. SAP MDG helps organizations to improve the quality and accuracy of their master data, which can lead to better decision-making and improved business outcomes.

In conclusion, SAP Master Data Governance is an important solution for organizations looking to improve the quality, accuracy, and consistency of their master data. It provides a centralized system for managing master data and a governance framework for ensuring that data is managed in a controlled and consistent manner. The benefits of SAP MDG include improved data quality, improved business processes, compliance with regulatory requirements, and improved decision-making.

Can you explain the different types of master data in SAP?

SAP master data refers to the key data elements in an SAP system that are central to the business processes and transactions. There are different types of master data in SAP, including:

  1. Material Master Data: This type of master data refers to information about the materials and products used in an organization, including the product description, unit of measure, and pricing information.
  2. Customer Master Data: This type of master data contains information about the customers of an organization, including the customer name, address, and contact information.
  3. Vendor Master Data: This type of master data contains information about the vendors of an organization, including the vendor name, address, and payment terms.
  4. Equipment Master Data: This type of master data refers to information about the equipment used in an organization, including the equipment type, manufacturer, and maintenance information.
  5. Financial Master Data: This type of master data contains information about the financial transactions of an organization, including the general ledger accounts, cost centers, and internal orders.
  6. Sales and Distribution Master Data: This type of master data contains information about the sales and distribution process, including the sales organization, distribution channel, and shipping conditions.
  7. Production Master Data: This type of master data contains information about the production process, including the bill of materials, routing, and capacity requirements planning.
  8. Human Resource Master Data: This type of master data contains information about the employees of an organization, including the employee name, address, and job information.

Each type of master data plays a critical role in supporting the business processes and transactions within an SAP system. The quality and accuracy of the master data are essential for the successful execution of these processes and transactions.

How does SAP Master Data Governance ensure data consistency and accuracy?

SAP Master Data Governance (MDG) is a data governance solution provided by SAP that helps organizations ensure the consistency and accuracy of their master data. It does this by providing a centralized platform for managing, maintaining, and governing master data across the enterprise.

Here’s how SAP Master Data Governance ensures data consistency and accuracy:

  1. Centralized platform: MDG provides a centralized platform for managing master data, so that all master data is managed and maintained in a single place. This ensures that all systems and processes are using the same version of the data, reducing the risk of data inconsistencies.
  2. Data modeling: MDG provides a flexible data modeling capability, which allows organizations to define the structure of their master data and enforce specific business rules. This helps ensure that the master data is consistently structured and that all data is entered in a standardized format, which reduces the risk of data inaccuracies.
  3. Workflow management: MDG provides a robust workflow management system that can be used to automate the approval process for changes to master data. This ensures that changes are properly reviewed and approved before they are implemented, reducing the risk of incorrect changes being made.
  4. Data quality checks: MDG includes a range of data quality checks that are applied to master data as it is being entered and maintained. These checks can be configured to enforce specific data quality rules and to ensure that the data being entered is complete, accurate, and consistent.
  5. Data stewardship: MDG includes a data stewardship framework that allows organizations to assign responsibility for specific areas of master data to individual users or teams. This ensures that the data is properly maintained and that there is a clear understanding of who is responsible for maintaining the data quality.

Overall, SAP Master Data Governance provides a comprehensive solution for managing master data and ensuring that it is consistent and accurate across the enterprise. By using MDG, organizations can improve the quality of their master data and reduce the risk of data inconsistencies and inaccuracies.

What are the key benefits of using SAP Master Data Governance for data management?

SAP Master Data Governance (MDG) is a software solution that enables organizations to manage, govern, and maintain the accuracy and consistency of their master data. The key benefits of using SAP MDG for data management are:

  1. Improved Data Quality: SAP MDG ensures that the master data is accurate, complete, and consistent across all systems, which helps in reducing the risk of data errors and improves data quality.
  2. Better Data Governance: SAP MDG provides a central repository for master data that enables organizations to manage, monitor, and control access to their master data. This helps in reducing the risk of data security breaches and improves data governance.
  3. Streamlined Data Management: SAP MDG provides a centralized platform for managing master data, which helps in reducing the time and effort required to maintain and update master data in multiple systems.
  4. Increased Collaboration: SAP MDG enables multiple users to collaborate and contribute to master data management in real-time, which helps in improving data accuracy and reducing data inconsistencies.
  5. Better Data Integration: SAP MDG integrates master data with other systems and applications, which helps in improving data accuracy and reducing data inconsistencies across different systems.
  6. Enhanced Data Analytics: SAP MDG provides real-time insights into master data, which helps in improving data accuracy and reducing data inconsistencies, and enables organizations to make data-driven decisions.

In conclusion, SAP Master Data Governance provides organizations with a centralized platform for managing and maintaining the accuracy and consistency of their master data, which helps in improving data quality, reducing data inconsistencies, and improving data governance.

Can you walk us through the process of creating and managing master data in SAP?

Master data in SAP refers to the data that is critical to an organization’s business processes and functions, such as customer data, vendor data, material data, and financial data. This data is used as a reference for transactions, reports, and other business processes.

The process of creating and managing master data in SAP involves the following steps:

  1. Define data fields: The first step is to define the data fields that will be used to create master data. This includes fields such as customer name, address, and contact details, material description, and vendor information.
  2. Create data templates: Next, data templates are created for each type of master data, such as customer data, material data, and vendor data. These templates define the data fields that will be used to store master data.
  3. Populate master data: After creating data templates, the next step is to populate the master data. This can be done manually, by entering data directly into SAP, or by importing data from external sources, such as spreadsheets or databases.
  4. Validate data: Once the master data is populated, it is important to validate the data to ensure that it is accurate and consistent. This can be done by using validation rules, such as checking that a customer’s address is in the correct format or that a material number is unique.
  5. Approve master data: After validating the data, it is important to have someone in the organization approve the master data. This is typically done by a data steward or a business analyst who is responsible for ensuring that the data is accurate and meets the organization’s standards.
  6. Manage master data: Once the master data is approved, it is important to manage the data on an ongoing basis. This involves updating the data when it changes, such as when a customer moves to a new address, or when a material is discontinued.
  7. Monitor data quality: Finally, it is important to monitor the quality of the master data to ensure that it remains accurate and up-to-date. This can be done by using data quality reports and dashboards, which provide insights into the accuracy and completeness of the master data.

In conclusion, creating and managing master data in SAP is a critical process that requires a structured approach and careful attention to detail. The steps outlined above provide a roadmap for ensuring that master data is accurate, consistent, and up-to-date, which is essential for supporting business processes and driving business success.

How does SAP Master Data Governance integrate with other SAP solutions, such as SAP S/4HANA?

SAP Master Data Governance (MDG) is a central solution for managing and maintaining master data across SAP systems. It provides a single point of control for master data, ensuring that data quality, consistency, and completeness are maintained.

The integration of SAP MDG with other SAP solutions, such as SAP S/4HANA, is crucial to ensure a seamless and consistent master data management process. The integration allows data to be shared and updated across different systems, ensuring that data is up-to-date and accurate.

The following are the ways in which SAP MDG integrates with SAP S/4HANA:

  1. Data Replication: SAP MDG can be used to replicate master data from SAP S/4HANA to other systems, ensuring that data is up-to-date and accurate across all systems.
  2. Data Quality: SAP MDG provides a set of tools and functionalities for managing and maintaining data quality, such as data validation and data enrichment. These tools can be used to ensure that data entered into SAP S/4HANA is of high quality and meets specific standards.
  3. Data Governance: SAP MDG provides a central platform for data governance, enabling organizations to manage and maintain master data across different systems and applications. This includes data classification, data governance workflows, and data ownership.
  4. Data Mapping: SAP MDG can be used to map master data between SAP S/4HANA and other systems, ensuring that data is consistently maintained across all systems.
  5. Data Centralization: SAP MDG provides a central repository for master data, enabling organizations to maintain a single source of truth for master data. This eliminates the need for manual data entry, reducing errors and improving data quality.

In conclusion, the integration of SAP Master Data Governance with SAP S/4HANA enables organizations to manage and maintain master data across different systems, ensuring data quality, consistency, and completeness. This integration ensures a seamless and efficient master data management process, reducing errors and improving data quality.

Can you explain the role of data stewards in SAP Master Data Governance and their responsibilities?

Data Stewards play a crucial role in SAP Master Data Governance. They are responsible for ensuring the accuracy, completeness, and consistency of the master data within the SAP system. They are also responsible for defining and implementing data governance policies, procedures, and controls to ensure that master data is managed effectively and efficiently.

The main responsibilities of Data Stewards in SAP Master Data Governance include:

  1. Data Quality Management: Data Stewards are responsible for ensuring that master data is accurate, complete, and consistent across the enterprise. They work with data owners and business users to validate data and resolve any data quality issues.
  2. Data Governance Policy: Data Stewards are responsible for defining and implementing data governance policies and procedures. They ensure that data governance policies are aligned with business needs and goals, and that all stakeholders are aware of these policies and procedures.
  3. Data Access and Security: Data Stewards are responsible for managing the access to master data and ensuring that data is protected from unauthorized access. They also ensure that data security policies and procedures are in place to protect sensitive data.
  4. Data Classification and Management: Data Stewards are responsible for classifying master data and ensuring that it is managed and stored in a way that meets business needs. They work with data owners to define data retention policies and procedures, and to ensure that master data is archived or deleted when it is no longer needed.
  5. Data Auditing and Monitoring: Data Stewards are responsible for monitoring and auditing master data to ensure that it is managed effectively and efficiently. They work with data owners and business users to resolve data quality issues and to implement data governance policies and procedures.

In summary, Data Stewards play a critical role in SAP Master Data Governance by ensuring the accuracy, completeness, and consistency of master data, and by implementing data governance policies, procedures, and controls to manage master data effectively and efficiently.

Can you discuss the challenges faced in master data management and how SAP Master Data Governance addresses them?

As organizations become more data-driven, the importance of having accurate, consistent, and up-to-date master data is becoming increasingly crucial. Master data is the critical data that organizations rely on to make business decisions, including customer and supplier information, product information, and financial data. However, the challenge of managing master data effectively is not trivial.

Here are some of the challenges faced in master data management and how SAP Master Data Governance addresses them:

  1. Data quality and consistency: Ensuring the quality and consistency of master data is a major challenge as data is often spread across multiple systems, applications, and databases. SAP Master Data Governance provides a centralized solution for managing master data, ensuring that all data is accurate and up-to-date.
  2. Data governance: Implementing a comprehensive data governance process can be challenging, especially for organizations with multiple departments, business processes, and data sources. SAP Master Data Governance provides a centralized solution for data governance, ensuring that all data is governed in a consistent and controlled manner.
  3. Data integration: Integrating master data from different systems and applications can be difficult, especially when dealing with legacy systems. SAP Master Data Governance provides a centralized solution for data integration, ensuring that data is accurately and consistently integrated across all systems and applications.
  4. Data security: Ensuring that master data is secure and protected from unauthorized access is critical. SAP Master Data Governance provides a secure solution for master data management, ensuring that data is protected from unauthorized access and that all data access is controlled and monitored.
  5. Data privacy: Ensuring that master data is protected from privacy breaches is becoming increasingly important, especially as organizations are subject to various data privacy regulations. SAP Master Data Governance provides a solution for managing privacy-sensitive data, ensuring that data is protected from privacy breaches and that all data access is controlled and monitored.

In conclusion, SAP Master Data Governance provides a comprehensive solution for managing master data, addressing the major challenges faced in master data management. With SAP Master Data Governance, organizations can ensure that their master data is accurate, consistent, up-to-date, and protected from unauthorized access, ensuring that they have the data they need to make informed business decisions.

Can you explain the concept of data modeling in SAP Master Data Governance?

Data modeling in SAP Master Data Governance refers to the process of creating a logical representation of the structure, relationships, and constraints of a company’s master data. This representation, known as a data model, serves as a blueprint for how master data should be organized, stored, and maintained within an SAP system.

The data modeling process in SAP Master Data Governance involves several key steps:

  1. Understanding the Business Requirements: The first step in data modeling is to gather a deep understanding of the business requirements and what type of data is required to support the business processes.
  2. Identifying Master Data Entities: Based on the business requirements, the data modeler will identify the key master data entities that need to be modeled. This could include entities such as customer, vendor, material, and financial data.
  3. Defining Attributes: For each master data entity, the data modeler will define the attributes or characteristics that describe the entity. For example, the customer entity might have attributes such as customer name, address, and phone number.
  4. Establishing Relationships: The data modeler will then establish the relationships between the master data entities. For example, a customer may have a relationship with a vendor, or a material may have a relationship with a financial account.
  5. Applying Constraints: The data modeler will also apply constraints to the data model to ensure that the master data is entered and maintained in a consistent and accurate manner. For example, the data model may enforce constraints such as mandatory fields or maximum lengths for data values.

Once the data model is completed, it can be used as a foundation for building the master data governance processes within SAP Master Data Governance. The data model ensures that the master data is structured in a way that supports the business processes and allows for efficient and accurate data management.

In conclusion, data modeling is a critical step in SAP Master Data Governance as it provides a clear understanding of the structure, relationships, and constraints of the company’s master data, which serves as a foundation for the design and implementation of the master data governance processes.

How does SAP Master Data Governance support data governance and data governance compliance?

SAP Master Data Governance (MDG) is a software solution that helps organizations manage and govern their master data. It provides a centralized platform for defining and maintaining data quality, consistency, and compliance.

The following are some of the ways in which SAP MDG supports data governance and data governance compliance:

  1. Centralized Repository: SAP MDG provides a centralized repository for master data, making it easier to maintain data quality and consistency across the organization. It also provides a centralized platform for defining data quality and governance policies, which can be easily enforced across the organization.
  2. Data Governance Workflow: SAP MDG provides a comprehensive data governance workflow that enables organizations to manage their data governance activities in a centralized and efficient manner. The workflow includes key steps such as data creation, modification, and approval, which are managed by the data governance team.
  3. Data Validation and Approval: SAP MDG includes robust data validation and approval mechanisms to ensure data quality and consistency. It provides a set of predefined validation rules that can be easily customized to meet the specific requirements of an organization. The validation and approval process can also be easily integrated with other SAP systems, such as SAP ERP, to ensure that data is consistent across all systems.
  4. Compliance Management: SAP MDG includes built-in compliance management capabilities that help organizations meet regulatory and legal requirements. The software supports the management of data privacy, data security, and data protection policies, helping organizations maintain compliance with data protection laws and regulations.
  5. Data Auditing and Reporting: SAP MDG provides detailed data auditing and reporting capabilities, enabling organizations to monitor data quality and compliance. The software provides reports on data quality, data usage, and data governance activities, which can be used to improve data quality and ensure compliance with data governance policies.

In conclusion, SAP MDG provides a comprehensive solution for managing and governing master data, helping organizations maintain data quality, consistency, and compliance. It provides a centralized platform for defining and enforcing data governance policies, and includes robust data validation and approval mechanisms, compliance management, and data auditing and reporting capabilities.

Basic Interview Questions

Q1. What is the purpose of MDG-C?

SAP MDG-C stands for Master Data Governance for Customer. This allows us to govern customer master data on a hub system and replicate the customer master data to various client systems.

Q2. Define SAP Business workflow.

SAP Business Workflow is useful for processing change requests in SAP Master Data Governance. In order to define the process flow of the change request, we can either use standard workflow templates or custom workflow templates while defining a change request type.

Q3. Define workflow analysis.

Workflow analysis is generally the process of breaking down the performance of a workflow and examining the trends for improvement. Moreover, business users can tweak processes for optimal efficiency and workplace productivity by looking at a workflow at a granular task level.

Q4. What are the three steps of workflow?

The three steps of the workflow are:

  • Input
  • Transformation
  • Output

Q5. What is a workflow process?

A workflow process is a succession of sequential tasks carried out on the basis of user-defined rules or conditions so as to execute a business process.

Q7. Define load balancing.

Load balancing is the efficient and methodical distribution of network or application traffic across several servers in a server farm. Hence, it ensures that no single server bears too much demand.

Q8. What is shared data?

Shared data is the amount of data that is shared by many people on a single plan. Day by day, shared data plans are becoming increasingly rare as this is the age of unlimited data.

Q9. What is the set of status control attributes?

Well, the set of status control attributes include:

  • no processing
  • Changes are allowed in data
  • objects can be added or eliminated

Q10. What are reconciliation accounts?

Reconciliation accounts are useful to link sub-ledger to ledger accounts in real-time. In case, GL is set up as a recon act then direct posting is restricted. Moreover, the reconciliation account updates when SL posting happens.

Q11. What is the Data Replication Framework?

The Data Replication Framework offers a distribution model for integrating into the overall landscape. Moreover, we can use DRF in order to replicate data from the Master Data Governance hub to all relevant target systems including both SAP and non-SAP systems.

Q12. What do you mean by RFM?

RFM is basically an add-on for the SAP MDG platform which delivers capabilities for consistent definition, replication, and authorization of key master data entities.

Q13. What are the benefits of MDG-RFM?

  • Timely identification of defects
  • Reduction of the holistic maintenance effort
  • Flexible workflow capabilities
  • Supports comprehensive article-search capabilities
  • Full transparency

Q14. Whom do you call a business partner?

A business partner is basically a person, group of persons, organization, or group of organizations in which a company has a business interest.

Q15. What do you mean by a chart of accounts?

Chart of accounts consists of a group of GL so as to form an account group. Thus, every company code is assigned a chart of accounts.

Q16. What are the three types of chart of accounts?

The three types are:

  • Group
  • Operational
  • Country

Q17. What does data manipulation refer to?

Data manipulation is the procedure of adjusting data to make it organized and comparatively easier to read and understand. Moreover, data manipulation language i.e. DML is a programming language that adjusts data by inserting, eliminating, and modifying data in a database to cleanse or map the data.

Q18. What is data visualization?

Data visualization is basically the graphical representation of data and information. Subsequently, with the use of visual elements such as graphs, charts, and maps, data visualization tools give an accessible way of viewing and understanding outliers, trends, and patterns in data.

Q19. What are the types of data sharing?

The three types of data sharing are:

  • Sharing data between the functional units.
  • Data sharing between the management units.
  • Sharing data between the geographically dispersed locations.

Q20. What is the controlling area?

The basic org unit in management accounting. When organizational structures are formed, one or more governing regions are allocated to an operational concern. As a result, numerous business codes can share the same Controlling area only if their FY variations and charts of accounts are comparable (COA)

Q21. What is the purpose of ABAP report?

Well, the Replicate Business Data for Advanced Compliance Reporting Service report in the SAP ERP source system allows us to replicate configuration, transactional, and master data from the source system to the cloud database.

Q22. Why do we create an RFC destination?

For the following purposes, we create the RFC destination:

  • Replication of data from SAP ERP source system to the cloud database, with thw use of ABAP report
  • Fetching details of reported documents from the cloud database to the SAP ERP source system
  • Integration of the SAP S/4HANA system with the service so as to submit reports created using SAP S/4 HANA for Advanced Compliance Reporting.

Q23. What is the use of configure scope?

Configure Scope is useful for customizing activity in order to configure the default scope for the mass process. This default scope will show on the Scope screen in the SAP Fiori UI. Moreover, it determines the fields available in the edit step of the SAP Fiori UI.

Q24. What are the requirements of business functions?

Before activating the business functions, we need to ensure that we have the administration authorization for MDG. Moreover, the authorization objects that are needed are delivered with the authorization role SAP_MDG_ADMIN.

Q25. What is SAP fraud management?

The integration of SAP Fraud Management allows the authorized users in change request processing in the SAP MDG, central governance to perform the screening of a person or company by SAP Fraud Management.

Q26. What is the need for fraud management?

Well, fraud management and investigation is necessary as it provides the essentials for resolving fraud allegations from inception to deposition. Hence, the use of proper procedures, techniques, and skills is essential so as to conduct an effective fraud investigation or examination.

Q27. What are the components of data model?

The components of the data model are:

  • entity types
  • attributes
  • relationships

Q28. Mention the types of risk control.

The three types of internal risk controls are:

  • detective
  • corrective
  • preventative

Q29. What is the purpose of data modelling?

Well, the aim of data modeling is to define the structure of the data storage. In the master data processing, a change request stores the master data changes in a staging area. Moreover, the data model can define a reuse area used for data storage after the completion of change request processing and the activation of related data.

Q30. Who should manage the risk of fraud?

 A combined effort has to be made by the board of directors, internal and external auditors, the audit committee, risk management personnel, operations personnel, investigators, and others in order to manage the risk of fraud.

Q31. How are entity details divided?

  • General Details
  • Key Assignment
  • Hierarchies
  • Reuse
  • Enablement Status
  • Texts

Q32. What is the role of Attributes tab?

This is used for defining the attributes of each entity type in the data model. Attributes are mapped as non-key fields in the created database tables of the entity type. Moreover, we are required to assign an existing data element to each attribute.

Q33. What are the common types of user interface?

The common types of the user interface are:

  • Single-Object Processing
  • Search
  • Multiple-Record Processing

Q34. What is the purpose of Data Quality and Search?

The data quality functions of MDG enable us to enrich and validate master data and prevent the creation of duplicates. Moreover, the different search capabilities are not just used to find master data that can be processed but can also be used to match data so as to prevent the creation of duplicate information.

Q35. Define process modeling.

The setup of change requests, governance scope, and workflow allows us to simulate the desired governance process in a variety of ways.

Q36. What does ERP stand for?

Well, ERP stands for Enterprise Resource Planning Software. This is an integrated computer-based system useful for managing the resources of a company effectively. Moreover, it ensures smooth information amongst various departments in an enterprise or an organization along with managing workflows.

Q37. Define data sets.

The data sets are the sequential files that are processed on the application server. Hence, they are useful for handling files in SAP.

Q38. Mention the steps of data mining.

The three steps of data mining are:

  • Initial Exploration
  • Model building
  • Deployment

Q39. What is metadata?

Well, metadata is the information that describes items in ArcGIS. When care is taken so as to provide good descriptions, one can find suitable items with a search and evaluate which of the items in the search results is the correct one to use.

Q40. What are the source systems in SAP?

Some of the source systems in SAP are:

  • SAP BW
  • SAP R/3 source system
  • External Systems
  • Flat files

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