Q. In which scenario would you prefer using a serverless architecture for model deployment?
-
A.
When you need constant high traffic
-
B.
When you want to minimize operational overhead
-
C.
When you require low latency
-
D.
When you need to manage complex infrastructure
Solution
A serverless architecture is preferred when you want to minimize operational overhead, as it automatically scales based on demand.
Correct Answer:
B
— When you want to minimize operational overhead
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Q. In which scenario would you use a shadow deployment strategy?
-
A.
When you want to completely replace an old model
-
B.
When you want to test a new model without affecting users
-
C.
When you want to gather user feedback
-
D.
When you want to scale the model
Solution
Shadow deployment allows testing a new model in production without affecting the user experience, as it runs alongside the existing model.
Correct Answer:
B
— When you want to test a new model without affecting users
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Q. What is a common evaluation metric used to assess the performance of a deployed classification model?
-
A.
Mean Squared Error
-
B.
Accuracy
-
C.
Silhouette Score
-
D.
R-squared
Solution
Accuracy is a common evaluation metric for classification models, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. What is a common method for monitoring deployed machine learning models?
-
A.
Cross-validation
-
B.
A/B testing
-
C.
Grid search
-
D.
K-fold validation
Solution
A/B testing is commonly used to monitor deployed models by comparing the performance of two different versions in real-world scenarios.
Correct Answer:
B
— A/B testing
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Q. What is a key consideration when deploying a machine learning model in a production environment?
-
A.
The model's training time
-
B.
The model's accuracy on the training set
-
C.
The model's ability to handle unseen data
-
D.
The model's complexity
Solution
A model's ability to handle unseen data is crucial in production, as it must generalize well to new inputs.
Correct Answer:
C
— The model's ability to handle unseen data
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Q. What is shadow deployment?
-
A.
Deploying a model without user interaction
-
B.
Deploying multiple models simultaneously
-
C.
Deploying a model alongside the current version to compare performance
-
D.
Deploying a model in a different environment
Solution
Shadow deployment involves deploying a new model alongside the current version to compare performance without affecting user experience.
Correct Answer:
C
— Deploying a model alongside the current version to compare performance
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Q. What is the purpose of A/B testing in the context of model deployment?
-
A.
To compare two different models
-
B.
To evaluate model performance on training data
-
C.
To tune hyperparameters
-
D.
To visualize model predictions
Solution
A/B testing is used to compare the performance of two different models or versions of a model in a real-world setting.
Correct Answer:
A
— To compare two different models
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Q. What is the purpose of versioning in model deployment?
-
A.
To improve model accuracy
-
B.
To track changes and manage different model iterations
-
C.
To enhance data preprocessing
-
D.
To optimize model training time
Solution
Versioning helps track changes and manage different iterations of models, ensuring that the correct version is used in production.
Correct Answer:
B
— To track changes and manage different model iterations
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Q. What is the role of APIs in model deployment?
-
A.
To train the model
-
B.
To provide a user interface
-
C.
To allow external applications to interact with the model
-
D.
To store model data
Solution
APIs (Application Programming Interfaces) allow external applications to interact with the deployed model, enabling predictions and data exchange.
Correct Answer:
C
— To allow external applications to interact with the model
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Q. What is the significance of containerization in model deployment?
-
A.
It improves model accuracy
-
B.
It simplifies the deployment process and ensures consistency
-
C.
It reduces the need for data preprocessing
-
D.
It eliminates the need for model monitoring
Solution
Containerization simplifies the deployment process by packaging the model and its dependencies, ensuring consistency across different environments.
Correct Answer:
B
— It simplifies the deployment process and ensures consistency
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Q. Which deployment strategy allows for gradual rollout of a new model version?
-
A.
Blue-green deployment
-
B.
A/B testing
-
C.
Canary deployment
-
D.
Shadow deployment
Solution
Canary deployment allows for a gradual rollout of a new model version to a small subset of users before full deployment.
Correct Answer:
C
— Canary deployment
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Q. Which of the following best describes the concept of 'model drift'?
-
A.
The model's performance improves over time
-
B.
The model's predictions become less accurate due to changes in data distribution
-
C.
The model's architecture changes during deployment
-
D.
The model is retrained with new data
Solution
Model drift occurs when the model's predictions become less accurate due to changes in the underlying data distribution over time.
Correct Answer:
B
— The model's predictions become less accurate due to changes in data distribution
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Q. Which of the following is a common challenge in model deployment?
-
A.
Data preprocessing
-
B.
Model interpretability
-
C.
Scalability and performance
-
D.
Feature selection
Solution
Scalability and performance are common challenges in model deployment, as models must handle varying loads and respond quickly in production.
Correct Answer:
C
— Scalability and performance
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Q. Which of the following is a key consideration when deploying a model for real-time predictions?
-
A.
Model complexity
-
B.
Data quality
-
C.
Latency requirements
-
D.
Training data size
Solution
Latency requirements are crucial for real-time predictions, as the model must respond quickly to user requests.
Correct Answer:
C
— Latency requirements
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Q. Which of the following is NOT a typical deployment environment for machine learning models?
-
A.
Cloud services
-
B.
Edge devices
-
C.
Local servers
-
D.
Data warehouses
Solution
Data warehouses are primarily used for data storage and analysis, not for deploying machine learning models.
Correct Answer:
D
— Data warehouses
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