WGU D501 – Machine Learning DevOps | Objective Assessment | OA |
Questions and Answers | 2026 Update | 100% Correct.
Complete Study Guide with Questions & Answers
(Aligned to Western Governors University OA + performance expectations)
DOMAIN 1: ML DevOps Fundamentals
Q1. What is ML DevOps (MLOps)?
Answer:
MLOps is a set of practices that combines machine learning, DevOps, and data engineering to
automate, deploy, monitor, and maintain ML models in production.
Q2. How is MLOps different from traditional DevOps?
Answer:
Traditional DevOps manages code, while MLOps manages code + data + models, including
retraining and model drift.
Q3. Why is MLOps necessary for machine learning systems?
Answer:
Because ML models degrade over time due to data drift, require retraining, and depend on
data pipelines, not just application code.
Q4. What are the core components of MLOps?
Answer:
Data pipelines
Model training
Model versioning
Deployment
Monitoring
, Retraining
DOMAIN 2: Data Management & Pipelines
Q5. What is a data pipeline?
Answer:
An automated process that ingests, validates, transforms, and stores data for ML training or
inference.
Q6. Why is data validation critical in MLOps?
Answer:
Because poor or unexpected data leads to incorrect predictions and model failure.
Q7. What is feature engineering?
Answer:
The process of transforming raw data into meaningful inputs (features) for ML models.
Q8. What is feature store and why is it used?
Answer:
A centralized repository that stores and serves consistent features for both training and
inference.
Q9. What is data drift?
Answer:
When the statistical properties of incoming data change over time compared to training data.
Q10. What is concept drift?
,Answer:
When the relationship between input data and target output changes over time.
DOMAIN 3: Model Training & Versioning
Q11. What is model versioning?
Answer:
Tracking different versions of trained models so they can be compared, rolled back, or
redeployed.
Q12. Why is model reproducibility important?
Answer:
To ensure results can be recreated using the same code, data, and parameters.
Q13. What is an experiment in MLOps?
Answer:
A controlled training run with specific data, parameters, and metrics.
Q14. What is hyperparameter tuning?
Answer:
The process of optimizing model parameters that are not learned during training.
Q15. What is a training pipeline?
Answer:
An automated workflow that handles data ingestion, training, evaluation, and model storage.
DOMAIN 4: Model Evaluation & Validation
, Q16. Why is model evaluation required before deployment?
Answer:
To ensure the model meets accuracy, fairness, and performance requirements.
Q17. What is overfitting?
Answer:
When a model performs well on training data but poorly on unseen data.
Q18. What is underfitting?
Answer:
When a model is too simple and fails to capture patterns in the data.
Q19. What is cross-validation?
Answer:
A technique that splits data into multiple training and validation sets to improve reliability.
Q20. Why are baseline models important?
Answer:
They provide a minimum performance benchmark to justify more complex models.
DOMAIN 5: Model Deployment
Q21. What does it mean to deploy a model?
Answer:
Making a trained model available for real-time or batch predictions.
Q22. What is batch inference?
Questions and Answers | 2026 Update | 100% Correct.
Complete Study Guide with Questions & Answers
(Aligned to Western Governors University OA + performance expectations)
DOMAIN 1: ML DevOps Fundamentals
Q1. What is ML DevOps (MLOps)?
Answer:
MLOps is a set of practices that combines machine learning, DevOps, and data engineering to
automate, deploy, monitor, and maintain ML models in production.
Q2. How is MLOps different from traditional DevOps?
Answer:
Traditional DevOps manages code, while MLOps manages code + data + models, including
retraining and model drift.
Q3. Why is MLOps necessary for machine learning systems?
Answer:
Because ML models degrade over time due to data drift, require retraining, and depend on
data pipelines, not just application code.
Q4. What are the core components of MLOps?
Answer:
Data pipelines
Model training
Model versioning
Deployment
Monitoring
, Retraining
DOMAIN 2: Data Management & Pipelines
Q5. What is a data pipeline?
Answer:
An automated process that ingests, validates, transforms, and stores data for ML training or
inference.
Q6. Why is data validation critical in MLOps?
Answer:
Because poor or unexpected data leads to incorrect predictions and model failure.
Q7. What is feature engineering?
Answer:
The process of transforming raw data into meaningful inputs (features) for ML models.
Q8. What is feature store and why is it used?
Answer:
A centralized repository that stores and serves consistent features for both training and
inference.
Q9. What is data drift?
Answer:
When the statistical properties of incoming data change over time compared to training data.
Q10. What is concept drift?
,Answer:
When the relationship between input data and target output changes over time.
DOMAIN 3: Model Training & Versioning
Q11. What is model versioning?
Answer:
Tracking different versions of trained models so they can be compared, rolled back, or
redeployed.
Q12. Why is model reproducibility important?
Answer:
To ensure results can be recreated using the same code, data, and parameters.
Q13. What is an experiment in MLOps?
Answer:
A controlled training run with specific data, parameters, and metrics.
Q14. What is hyperparameter tuning?
Answer:
The process of optimizing model parameters that are not learned during training.
Q15. What is a training pipeline?
Answer:
An automated workflow that handles data ingestion, training, evaluation, and model storage.
DOMAIN 4: Model Evaluation & Validation
, Q16. Why is model evaluation required before deployment?
Answer:
To ensure the model meets accuracy, fairness, and performance requirements.
Q17. What is overfitting?
Answer:
When a model performs well on training data but poorly on unseen data.
Q18. What is underfitting?
Answer:
When a model is too simple and fails to capture patterns in the data.
Q19. What is cross-validation?
Answer:
A technique that splits data into multiple training and validation sets to improve reliability.
Q20. Why are baseline models important?
Answer:
They provide a minimum performance benchmark to justify more complex models.
DOMAIN 5: Model Deployment
Q21. What does it mean to deploy a model?
Answer:
Making a trained model available for real-time or batch predictions.
Q22. What is batch inference?
