Q. What does A/B testing in model deployment help to evaluate?
-
A.
Model training time
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B.
User engagement
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C.
Model performance against a baseline
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D.
Data quality
Solution
A/B testing helps to evaluate model performance against a baseline by comparing two versions.
Correct Answer:
C
— Model performance against a baseline
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Q. What is the purpose of containerization in model deployment?
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A.
To improve model accuracy
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B.
To ensure consistent environments across deployments
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C.
To reduce model size
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D.
To enhance data preprocessing
Solution
Containerization ensures consistent environments across deployments, reducing compatibility issues.
Correct Answer:
B
— To ensure consistent environments across deployments
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Q. What is the role of a REST API in model deployment?
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A.
To train the model
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B.
To serve predictions from the model
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C.
To visualize model performance
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D.
To preprocess input data
Solution
A REST API serves predictions from the deployed model, allowing applications to interact with it.
Correct Answer:
B
— To serve predictions from the model
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Q. Which metric is commonly used to evaluate the performance of a deployed classification model?
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A.
Mean Squared Error
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B.
Accuracy
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C.
Silhouette Score
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D.
R-squared
Solution
Accuracy is a common metric used to evaluate the performance of a deployed classification model.
Correct Answer:
B
— Accuracy
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Q. Which metric is often used to monitor the performance of a deployed model?
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A.
Accuracy
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B.
F1 Score
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C.
Latency
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D.
All of the above
Solution
All of the above metrics can be important for monitoring the performance of a deployed model.
Correct Answer:
D
— All of the above
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Q. Which of the following tools is commonly used for model deployment?
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A.
TensorFlow Serving
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B.
Pandas
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C.
NumPy
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D.
Matplotlib
Solution
TensorFlow Serving is a popular tool specifically designed for deploying machine learning models.
Correct Answer:
A
— TensorFlow Serving
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