Q. In the context of model deployment, what does 'scalability' refer to?
A.
The ability to handle increased load
B.
The ability to reduce model size
C.
The ability to improve accuracy
D.
The ability to visualize data
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Solution
Scalability refers to the ability of the deployed model to handle increased load or demand.
Correct Answer:
A
— The ability to handle increased load
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Q. What is 'data drift' in the context of deployed models?
A.
Changes in the model architecture
B.
Changes in the data distribution over time
C.
Changes in the model's hyperparameters
D.
Changes in the evaluation metrics
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Solution
Data drift refers to changes in the data distribution over time, which can affect the model's performance and accuracy.
Correct Answer:
B
— Changes in the data distribution over time
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Q. What is a common practice to ensure the reliability of a deployed model?
A.
Regularly retraining the model with new data
B.
Using a single model version indefinitely
C.
Ignoring user feedback
D.
Deploying without monitoring
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Solution
Regularly retraining the model with new data helps ensure its reliability and accuracy in changing environments.
Correct Answer:
A
— Regularly retraining the model with new data
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Q. What is a key consideration when deploying a model for numerical applications?
A.
Model interpretability
B.
Data privacy and security
C.
Scalability and performance
D.
All of the above
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Solution
When deploying a model for numerical applications, it is crucial to consider model interpretability, data privacy, security, scalability, and performance.
Correct Answer:
D
— All of the above
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Q. What is the role of a model serving framework in deployment?
A.
To train the model
B.
To manage model versions and scaling
C.
To preprocess data
D.
To visualize model performance
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Solution
A model serving framework primarily manages model versions and scaling, ensuring that the model can handle requests efficiently in production.
Correct Answer:
B
— To manage model versions and scaling
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Q. What is the role of a model serving framework?
A.
To train models on large datasets
B.
To manage and serve machine learning models in production
C.
To visualize model performance
D.
To preprocess data for training
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Solution
A model serving framework is designed to manage and serve machine learning models in production, facilitating their use in applications.
Correct Answer:
B
— To manage and serve machine learning models in production
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Q. Which cloud service is often used for deploying machine learning models?
A.
Google Cloud Storage
B.
Amazon S3
C.
Microsoft Azure Machine Learning
D.
All of the above
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Solution
Microsoft Azure Machine Learning is specifically designed for deploying machine learning models.
Correct Answer:
C
— Microsoft Azure Machine Learning
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Q. Which deployment strategy allows for quick rollback in case of issues?
A.
Blue-Green Deployment
B.
Canary Deployment
C.
Rolling Deployment
D.
All of the above
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Solution
Blue-Green Deployment allows for quick rollback by maintaining two identical environments, enabling seamless switching between them if issues arise.
Correct Answer:
A
— Blue-Green Deployment
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Q. Which evaluation metric is commonly used for regression models during deployment?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error (MAE)
D.
Confusion Matrix
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Solution
Mean Absolute Error (MAE) is commonly used to evaluate regression models.
Correct Answer:
C
— Mean Absolute Error (MAE)
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Q. Which evaluation metric is most appropriate for regression models during deployment?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error (MAE)
D.
Confusion Matrix
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Solution
Mean Absolute Error (MAE) is a suitable evaluation metric for regression models, as it measures the average magnitude of errors in predictions.
Correct Answer:
C
— Mean Absolute Error (MAE)
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Q. Which of the following is NOT a common challenge in model deployment?
A.
Model versioning
B.
Data drift
C.
Hyperparameter tuning
D.
Latency issues
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Solution
Hyperparameter tuning is typically a part of the model training process, not a challenge faced during model deployment.
Correct Answer:
C
— Hyperparameter tuning
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Q. Which of the following is NOT a deployment strategy for machine learning models?
A.
Blue-Green Deployment
B.
Canary Release
C.
A/B Testing
D.
Data Augmentation
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Solution
Data Augmentation is a technique used during training, not a deployment strategy.
Correct Answer:
D
— Data Augmentation
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