Data Management Fundamentals ESL (DMF Exam RE1024)
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Free Data Management Fundamentals ESL (DMF Exam RE1024) Questions
1.
Which artifact is the highest level of abstraction in the Enterprise Data Model?
- A. Data Ownership Model
- B. Top-level Process Model
- C. Subject Area Model
- D. Conceptual Model
- E. Systems Portfolio Model
Explanation
Explanation
The Subject Area Model is the highest level of abstraction within an Enterprise Data Model. It groups major data domains—such as Customer, Product, Finance, or Employee—into broad, business-focused areas without detailing attributes or relationships. This level provides a strategic overview of the organization’s information landscape and helps executives, architects, and governance teams align on scope, ownership, and priorities. The Conceptual Model comes next, adding business entities and high-level relationships, but the Subject Area Model sits above it as the most abstract representation.
The Subject Area Model is the highest level of abstraction within an Enterprise Data Model. It groups major data domains—such as Customer, Product, Finance, or Employee—into broad, business-focused areas without detailing attributes or relationships. This level provides a strategic overview of the organization’s information landscape and helps executives, architects, and governance teams align on scope, ownership, and priorities. The Conceptual Model comes next, adding business entities and high-level relationships, but the Subject Area Model sits above it as the most abstract representation.
2.
Data and information are:
- A. Intertwined and dependent on each other
- B. Pillars of the modern organisational Parthenon
- C. Completely separate things
- D. Used only in the context of business intelligence
- E. Representations of truth
Explanation
Explanation
Data and information share a direct, dependent relationship: data consists of raw facts, figures, or observations, while information is what data becomes once it is processed, structured, contextualized, or interpreted to provide meaning. Data alone cannot support decisions without being transformed into information, and information cannot exist without underlying data. Their interdependence forms the basis of analytics, reporting, and organizational decision-making. Understanding this relationship is essential for effective data management and information governance.
Data and information share a direct, dependent relationship: data consists of raw facts, figures, or observations, while information is what data becomes once it is processed, structured, contextualized, or interpreted to provide meaning. Data alone cannot support decisions without being transformed into information, and information cannot exist without underlying data. Their interdependence forms the basis of analytics, reporting, and organizational decision-making. Understanding this relationship is essential for effective data management and information governance.
3.
In the common enterprise architecture model coded BIAT, the 'I' stands for:
- A. Interoperability
- B. Information
- C. Integration
- D. Identification
- E. Instance
Explanation
Explanation
In the BIAT model, which stands for Business, Information, Applications, and Technology, the 'I' represents Integration. This refers to the seamless connection and communication between different systems, applications, and data within the enterprise architecture. Integration ensures that the various components of the organization’s IT infrastructure work together efficiently, sharing data and services to meet business objectives.
In the BIAT model, which stands for Business, Information, Applications, and Technology, the 'I' represents Integration. This refers to the seamless connection and communication between different systems, applications, and data within the enterprise architecture. Integration ensures that the various components of the organization’s IT infrastructure work together efficiently, sharing data and services to meet business objectives.
4.
In data modelling, it is important to know if time is being modelled because:
- A. Business days are different to calendar days in most organisations
- B. We need to be able to distinguish rules at a point in time from rules over time
- C. The next phase will be faster if time is already modelled
- D. Models are not complete until they consider rules over time
- E. There should be a 'time' entity on the model if time has been modelled
Explanation
Explanation
When modelling data, understanding how time is treated allows you to distinguish between static data (which represents values at a single point in time) and dynamic data (which represents changes over time). By clearly modeling time, you can differentiate rules and behaviors that apply at a specific moment (e.g., pricing at a certain date) from those that apply over a range of time (e.g., pricing changes over the course of a year). This distinction is critical for accurately capturing business logic and making the model more effective for analysis, reporting, and decision-making.
When modelling data, understanding how time is treated allows you to distinguish between static data (which represents values at a single point in time) and dynamic data (which represents changes over time). By clearly modeling time, you can differentiate rules and behaviors that apply at a specific moment (e.g., pricing at a certain date) from those that apply over a range of time (e.g., pricing changes over the course of a year). This distinction is critical for accurately capturing business logic and making the model more effective for analysis, reporting, and decision-making.
5.
When a data quality team has more issues than they can manage, they should look to:
- A. Delete any issue that is greater than 6 months old
- B. Establish a program of quick wins targeting easy fixes over a short time period
- C. Implement data validation rules on data entry systems
- D. Initiate data quality improvement cycles, focusing on achieving incremental improvements
- E. Hire more people
Explanation
Explanation
When a data quality team is overwhelmed, it's important to prioritize manageable, incremental improvements rather than trying to fix everything at once. Data quality improvement cycles (often referred to as PDCA—Plan, Do, Check, Act) allow teams to systematically address issues, measure progress, and continuously improve over time. By focusing on small, achievable improvements, teams can build momentum, reduce backlogs, and make sustainable progress without becoming overwhelmed. This approach is more effective than relying solely on quick fixes, rule implementation, or hiring more people.
When a data quality team is overwhelmed, it's important to prioritize manageable, incremental improvements rather than trying to fix everything at once. Data quality improvement cycles (often referred to as PDCA—Plan, Do, Check, Act) allow teams to systematically address issues, measure progress, and continuously improve over time. By focusing on small, achievable improvements, teams can build momentum, reduce backlogs, and make sustainable progress without becoming overwhelmed. This approach is more effective than relying solely on quick fixes, rule implementation, or hiring more people.
6.
A pensioner who usually receives a quarterly bill of around $300 was sent a $100,000,000 electricity bill. They were a victim of poor data quality checks in which dimension?
- A. Timeliness
- B. Integrity
- C. Reasonableness
- D. Currency
- E. Accuracy
Explanation
Explanation
Reasonableness checks ensure that data values fall within an expected or logical range. A quarterly electricity bill jumping from approximately $300 to $100,000,000 is clearly implausible and should have been caught by a reasonableness rule, such as upper and lower billing thresholds or anomaly detection. This type of validation verifies whether a value “makes sense” in context, preventing extreme outliers or absurd figures from entering customer-facing systems. Because the issue involves an unrealistic amount rather than incorrect timing, format, or calculation, the failed dimension is reasonableness.
Reasonableness checks ensure that data values fall within an expected or logical range. A quarterly electricity bill jumping from approximately $300 to $100,000,000 is clearly implausible and should have been caught by a reasonableness rule, such as upper and lower billing thresholds or anomaly detection. This type of validation verifies whether a value “makes sense” in context, preventing extreme outliers or absurd figures from entering customer-facing systems. Because the issue involves an unrealistic amount rather than incorrect timing, format, or calculation, the failed dimension is reasonableness.
7.
Business continuity is an aspect of Governance. What should a business continuity plan include?
- A. Precedes business rules
- B. Outlines how a business will continue operating during an unplanned disruption in service
- C. Defines unplanned disruptions that may occur
- D. Provides explanation to customers during an unplanned disruption in service
- E. Explains to external stakeholders why performance expectations are not being met
Explanation
Explanation
A business continuity plan primarily focuses on ensuring that operations can continue during unexpected disruptions such as cyberattacks, natural disasters, or system failures. It provides structured procedures, recovery strategies, backup mechanisms, and responsibilities to minimize downtime. The core purpose is to protect critical business functions and maintain essential services even under adverse conditions. This aligns directly with the definition and purpose of a business continuity plan.
A business continuity plan primarily focuses on ensuring that operations can continue during unexpected disruptions such as cyberattacks, natural disasters, or system failures. It provides structured procedures, recovery strategies, backup mechanisms, and responsibilities to minimize downtime. The core purpose is to protect critical business functions and maintain essential services even under adverse conditions. This aligns directly with the definition and purpose of a business continuity plan.
8.
What causes data redundancy or data ROT?
- A. Poor assimilation of collected data
- B. Poor data management practices
- C. Server and human error
- D. Dataset inaccuracies developed over time
- E. All of these
Explanation
Explanation
Data redundancy and data ROT (Redundant, Outdated, or Trivial data) arise from multiple contributing factors rather than a single cause. Poor data management practices allow duplicate and outdated records to persist. Poor assimilation of collected data results in inconsistent or unnecessary datasets. Server errors, system failures, and human input mistakes also contribute to duplicated or irrelevant information. Finally, dataset inaccuracies naturally develop over time as information becomes outdated. Because all listed factors directly contribute to redundancy and ROT, the correct answer is “All of these.”
Data redundancy and data ROT (Redundant, Outdated, or Trivial data) arise from multiple contributing factors rather than a single cause. Poor data management practices allow duplicate and outdated records to persist. Poor assimilation of collected data results in inconsistent or unnecessary datasets. Server errors, system failures, and human input mistakes also contribute to duplicated or irrelevant information. Finally, dataset inaccuracies naturally develop over time as information becomes outdated. Because all listed factors directly contribute to redundancy and ROT, the correct answer is “All of these.”
9.
Periodic archiving of transaction data from a production CRM system is critical for:
- A. Enabling the distribution of transaction data across the enterprise
- B. Training junior DBAs
- C. Managing deleted customer records
- D. Providing alternate sources for reporting systems
- E. The maintenance of database performance
Explanation
Explanation
Periodic archiving helps maintain the performance of the database by removing older, less frequently accessed transaction data from the active production environment. This reduces the volume of data in the live system, improving query performance, backups, and general database operations. While archiving can be useful for reporting and distribution, its primary benefit is ensuring that the active database remains efficient and responsive by preventing it from becoming overloaded with historical data.
Periodic archiving helps maintain the performance of the database by removing older, less frequently accessed transaction data from the active production environment. This reduces the volume of data in the live system, improving query performance, backups, and general database operations. While archiving can be useful for reporting and distribution, its primary benefit is ensuring that the active database remains efficient and responsive by preventing it from becoming overloaded with historical data.
10.
The ability of a photo app to share its images with various social media applications is an example of:
- A. Rendering
- B. Integration
- C. Interoperability
- D. Metadata
- E. Replication
Explanation
Explanation
Interoperability refers to the ability of different systems, applications, or platforms to exchange and use information seamlessly. When a photo app can share images directly to various social media platforms, it demonstrates interoperability—each system can “understand” and accept the data without requiring manual conversion or custom handling. This capability depends on shared protocols, APIs, and standards that allow systems to work together smoothly and consistently across technological boundaries.
Interoperability refers to the ability of different systems, applications, or platforms to exchange and use information seamlessly. When a photo app can share images directly to various social media platforms, it demonstrates interoperability—each system can “understand” and accept the data without requiring manual conversion or custom handling. This capability depends on shared protocols, APIs, and standards that allow systems to work together smoothly and consistently across technological boundaries.
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