Q. What does A/B testing in model deployment help to determine?
-
A.
The best hyperparameters for the model
-
B.
The performance of two different models
-
C.
The training time of the model
-
D.
The data preprocessing steps
Solution
A/B testing compares the performance of two different models to determine which one performs better in a production environment.
Correct Answer:
B
— The performance of two different models
Learn More →
Q. What is a common strategy for handling model updates in production?
-
A.
Immediate replacement of the old model
-
B.
Rolling updates
-
C.
No updates allowed
-
D.
Training a new model from scratch
Solution
Rolling updates allow for gradual deployment of new model versions while minimizing disruption to the service.
Correct Answer:
B
— Rolling updates
Learn More →
Q. What is a key consideration when deploying a model in a cloud environment?
-
A.
Data privacy regulations
-
B.
Model training time
-
C.
Feature selection
-
D.
Hyperparameter tuning
Solution
Data privacy regulations are crucial when deploying models in the cloud, as sensitive data may be involved.
Correct Answer:
A
— Data privacy regulations
Learn More →
Q. What is the purpose of a model monitoring system post-deployment?
-
A.
To retrain the model automatically
-
B.
To track model performance and detect issues
-
C.
To optimize hyperparameters
-
D.
To visualize training data
Solution
A model monitoring system tracks performance metrics and detects issues such as data drift or model degradation after deployment.
Correct Answer:
B
— To track model performance and detect issues
Learn More →
Q. What is the purpose of a model serving framework?
-
A.
To train models faster
-
B.
To manage and serve models in production
-
C.
To visualize model performance
-
D.
To preprocess data
Solution
A model serving framework is designed to manage and serve machine learning models in a production environment, facilitating inference.
Correct Answer:
B
— To manage and serve models in production
Learn More →
Q. What is the significance of feature engineering in the context of model deployment?
-
A.
It is only important during model training
-
B.
It helps in improving model interpretability
-
C.
It ensures the model can handle new data effectively
-
D.
It is irrelevant to model performance
Solution
Feature engineering is crucial for ensuring that the model can effectively handle new data it encounters in production.
Correct Answer:
C
— It ensures the model can handle new data effectively
Learn More →
Q. Which evaluation metric is commonly 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
Learn More →
Showing 1 to 7 of 7 (1 Pages)
