Minnesota Machine Learning Systems
Compliance Analyst Exam Practice
Questions And Correct Answers
(Verified Answers) Plus Rationales 2026
Q&A | Instant Download Pdf
1. What is the primary purpose of model governance in machine learning
systems?
A. Increasing model size
B. Ensuring models are entertaining
C. Ensuring models meet legal, ethical, and performance standards
D. Maximizing GPU usage
Correct Answer: C
Rationale: Model governance ensures machine learning systems comply
with regulatory, ethical, and operational standards throughout their
lifecycle.
, 2. Which regulation is most directly related to data privacy in ML systems
handling EU citizen data?
A. HIPAA
B. GDPR
C. SOX
D. FERPA
Correct Answer: B
Rationale: The General Data Protection Regulation (GDPR) governs data
privacy and protection for EU residents.
3. What is model drift in machine learning?
A. A hardware failure
B. A change in model architecture
C. Degradation of model performance over time
D. A type of encryption
Correct Answer: C
Rationale: Model drift occurs when the statistical properties of input data
change, reducing model accuracy over time.
4. Which technique is used to detect bias in ML models?
A. Overclocking
B. Fairness metrics evaluation
, C. Data compression
D. Gradient boosting
Correct Answer: B
Rationale: Fairness metrics help identify bias across protected attributes
such as gender or race.
5. What is the purpose of audit logging in ML systems?
A. Improve training speed
B. Store random model outputs
C. Track system activities for accountability and compliance
D. Increase dataset size
Correct Answer: C
Rationale: Audit logs ensure traceability of actions for regulatory and
compliance review.
6. Which is an example of supervised learning?
A. K-means clustering
B. Principal component analysis
C. Linear regression with labeled data
D. Association rules mining
Correct Answer: C
Rationale: Supervised learning uses labeled datasets, such as linear
regression.
, 7. What is data lineage in machine learning compliance?
A. GPU tracking
B. Documentation of data origin and transformation
C. Model compression method
D. Hyperparameter tuning
Correct Answer: B
Rationale: Data lineage tracks how data is sourced, transformed, and used
in ML pipelines.
8. Which concept ensures that ML models treat individuals fairly?
A. Overfitting
B. Fairness
C. Backpropagation
D. Tokenization
Correct Answer: B
Rationale: Fairness ensures unbiased treatment across different
demographic groups.
9. What is the main risk of using biased training data?
A. Faster training
B. Improved generalization
C. Discriminatory or inaccurate predictions
D. Lower storage cost
Compliance Analyst Exam Practice
Questions And Correct Answers
(Verified Answers) Plus Rationales 2026
Q&A | Instant Download Pdf
1. What is the primary purpose of model governance in machine learning
systems?
A. Increasing model size
B. Ensuring models are entertaining
C. Ensuring models meet legal, ethical, and performance standards
D. Maximizing GPU usage
Correct Answer: C
Rationale: Model governance ensures machine learning systems comply
with regulatory, ethical, and operational standards throughout their
lifecycle.
, 2. Which regulation is most directly related to data privacy in ML systems
handling EU citizen data?
A. HIPAA
B. GDPR
C. SOX
D. FERPA
Correct Answer: B
Rationale: The General Data Protection Regulation (GDPR) governs data
privacy and protection for EU residents.
3. What is model drift in machine learning?
A. A hardware failure
B. A change in model architecture
C. Degradation of model performance over time
D. A type of encryption
Correct Answer: C
Rationale: Model drift occurs when the statistical properties of input data
change, reducing model accuracy over time.
4. Which technique is used to detect bias in ML models?
A. Overclocking
B. Fairness metrics evaluation
, C. Data compression
D. Gradient boosting
Correct Answer: B
Rationale: Fairness metrics help identify bias across protected attributes
such as gender or race.
5. What is the purpose of audit logging in ML systems?
A. Improve training speed
B. Store random model outputs
C. Track system activities for accountability and compliance
D. Increase dataset size
Correct Answer: C
Rationale: Audit logs ensure traceability of actions for regulatory and
compliance review.
6. Which is an example of supervised learning?
A. K-means clustering
B. Principal component analysis
C. Linear regression with labeled data
D. Association rules mining
Correct Answer: C
Rationale: Supervised learning uses labeled datasets, such as linear
regression.
, 7. What is data lineage in machine learning compliance?
A. GPU tracking
B. Documentation of data origin and transformation
C. Model compression method
D. Hyperparameter tuning
Correct Answer: B
Rationale: Data lineage tracks how data is sourced, transformed, and used
in ML pipelines.
8. Which concept ensures that ML models treat individuals fairly?
A. Overfitting
B. Fairness
C. Backpropagation
D. Tokenization
Correct Answer: B
Rationale: Fairness ensures unbiased treatment across different
demographic groups.
9. What is the main risk of using biased training data?
A. Faster training
B. Improved generalization
C. Discriminatory or inaccurate predictions
D. Lower storage cost
