Q. What is the purpose of monitoring a deployed model?
-
A.
To ensure it is still accurate and performing well
-
B.
To retrain the model automatically
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C.
To visualize data inputs
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D.
To reduce model complexity
Solution
Monitoring a deployed model is essential to ensure it continues to perform well and remains accurate over time.
Correct Answer:
A
— To ensure it is still accurate and performing well
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Q. What is the role of a model registry in deployment?
-
A.
To store raw data
-
B.
To manage model versions and metadata
-
C.
To visualize model performance
-
D.
To train models automatically
Solution
A model registry helps manage different versions of models and their associated metadata, facilitating easier deployment and tracking.
Correct Answer:
B
— To manage model versions and metadata
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Q. Which of the following is a common tool used for model deployment?
-
A.
TensorFlow Serving
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B.
Pandas
-
C.
NumPy
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D.
Matplotlib
Solution
TensorFlow Serving is a popular tool specifically designed for deploying machine learning models in production environments.
Correct Answer:
A
— TensorFlow Serving
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Q. Which of the following is NOT a challenge in model deployment?
-
A.
Integration with existing systems
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B.
Data privacy concerns
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C.
Model training time
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D.
Monitoring model performance
Solution
Model training time is not a challenge in deployment; rather, it is a concern during the model development phase.
Correct Answer:
C
— Model training time
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Q. Which of the following is NOT a common deployment strategy?
-
A.
Blue-Green deployment
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B.
Canary deployment
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C.
Rolling deployment
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D.
Random deployment
Solution
Random deployment is not a common deployment strategy; the others (Blue-Green, Canary, and Rolling) are widely used.
Correct Answer:
D
— Random deployment
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