Q. What is a key consideration when deploying a model in a production environment?
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A.
Model accuracy only
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B.
Scalability and performance
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C.
Data preprocessing steps
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D.
Model training duration
Solution
Scalability and performance are crucial when deploying a model to ensure it can handle the expected load and provide timely predictions.
Correct Answer:
B
— Scalability and performance
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Q. What is a potential challenge when deploying machine learning models?
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A.
Overfitting the model
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B.
Data drift
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C.
Lack of training data
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D.
All of the above
Solution
Data drift, which occurs when the statistical properties of the input data change over time, is a significant challenge in model deployment.
Correct Answer:
B
— Data drift
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Q. What is the primary purpose of model deployment in machine learning?
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A.
To train the model on new data
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B.
To make the model available for use in production
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C.
To evaluate the model's performance
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D.
To visualize the model's architecture
Solution
Model deployment makes the trained model available for use in real-world applications, allowing it to make predictions on new data.
Correct Answer:
B
— To make the model available for use in production
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Q. What is the role of an API in model deployment?
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A.
To train the model
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B.
To provide a user interface
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C.
To allow external applications to interact with the model
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D.
To store the model
Solution
An API allows external applications to interact with the deployed model, enabling them to send data and receive predictions.
Correct Answer:
C
— To allow external applications to interact with the model
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Q. What is the role of monitoring in deployed machine learning models?
-
A.
To ensure the model is trained correctly
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B.
To track model performance and detect issues
-
C.
To visualize model predictions
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D.
To preprocess incoming data
Solution
Monitoring is essential to track the performance of deployed models and detect any issues that may arise over time.
Correct Answer:
B
— To track model performance and detect issues
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Q. What is the significance of versioning in model deployment?
-
A.
To keep track of different model architectures
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B.
To manage updates and changes to models over time
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C.
To ensure data consistency
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D.
To improve model accuracy
Solution
Versioning is important in model deployment to manage updates and changes, ensuring that the correct model is used in production.
Correct Answer:
B
— To manage updates and changes to models over time
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Q. Which deployment strategy allows for gradual rollout of a new model?
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A.
Blue-green deployment
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B.
Canary deployment
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C.
Rolling deployment
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D.
All of the above
Solution
All of the mentioned strategies (blue-green, canary, and rolling deployments) allow for gradual rollout of new models to minimize risk.
Correct Answer:
D
— All of the above
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Q. Which deployment strategy involves gradually rolling out a model to a subset of users?
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A.
Blue-green deployment
-
B.
Canary deployment
-
C.
A/B testing
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D.
Shadow deployment
Solution
Canary deployment involves gradually rolling out a new model to a small subset of users to monitor its performance before a full rollout.
Correct Answer:
B
— Canary deployment
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Q. Which of the following best describes 'A/B testing' in the context of model deployment?
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A.
Training two models simultaneously
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B.
Comparing two versions of a model to determine which performs better
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C.
Deploying a model without testing
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D.
None of the above
Solution
A/B testing involves comparing two versions of a model to evaluate which one performs better in a real-world setting.
Correct Answer:
B
— Comparing two versions of a model to determine which performs better
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Q. Which of the following is NOT a common application of deployed machine learning models?
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A.
Spam detection in emails
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B.
Image recognition in photos
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C.
Training new models
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D.
Recommendation systems
Solution
Training new models is not an application of deployed models; rather, it is part of the model development process.
Correct Answer:
C
— Training new models
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Q. Which of the following tools is commonly used for deploying machine learning models?
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A.
TensorFlow Serving
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B.
Jupyter Notebook
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C.
Pandas
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D.
NumPy
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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