Q. In a case study, which metric is often used to evaluate the success of a deployed model?
A.
Accuracy
B.
F1 Score
C.
Return on Investment (ROI)
D.
Confusion Matrix
Solution
Return on Investment (ROI) is often used to evaluate the success of a deployed model, as it measures the financial benefits gained from the model compared to its costs.
Q. What is a potential benefit of using cloud services for model deployment?
A.
Increased hardware costs
B.
Scalability and flexibility
C.
Limited access to resources
D.
Complex setup process
Solution
Using cloud services for model deployment offers scalability and flexibility, allowing models to handle varying loads and be easily updated or modified.
Q. What is a potential risk of deploying a machine learning model without proper validation?
A.
Increased training time
B.
Overfitting
C.
Poor user experience
D.
Data leakage
Solution
Deploying a machine learning model without proper validation can lead to a poor user experience, as the model may not perform as expected in real-world scenarios.
Q. What is the primary goal of model deployment in machine learning?
A.
To train the model on new data
B.
To make the model available for use in production
C.
To evaluate the model's performance
D.
To visualize the model's predictions
Solution
The primary goal of model deployment is to make the model available for use in production environments, allowing it to provide predictions or insights based on new data.
Correct Answer:
B
— To make the model available for use in production
Q. What is the role of a 'model registry' in the deployment process?
A.
To store raw data
B.
To manage model versions and metadata
C.
To visualize model performance
D.
To preprocess data
Solution
A model registry is used to manage model versions and metadata, ensuring that different versions of models can be tracked and accessed during deployment.
Correct Answer:
B
— To manage model versions and metadata
Q. Which deployment strategy involves gradually rolling out a model to a subset of users before full deployment?
A.
Blue-green deployment
B.
Canary deployment
C.
Rolling deployment
D.
A/B testing
Solution
Canary deployment is a strategy where a new model is gradually rolled out to a small subset of users to monitor its performance before full deployment.
Q. Which of the following best describes 'shadow deployment'?
A.
Deploying a model alongside the current model without affecting users
B.
Completely replacing the old model with a new one
C.
Deploying a model only during off-peak hours
D.
Using a model for training while another is in production
Solution
Shadow deployment involves deploying a new model alongside the current model without affecting users, allowing for performance comparison without risk.
Correct Answer:
A
— Deploying a model alongside the current model without affecting users
Q. Which of the following is a common challenge faced during model deployment?
A.
Data preprocessing
B.
Model interpretability
C.
Integration with existing systems
D.
Feature selection
Solution
Integration with existing systems is a common challenge during model deployment, as it requires ensuring that the model can work seamlessly with other software and data pipelines.
Correct Answer:
C
— Integration with existing systems
Q. Which of the following is NOT a common method for deploying machine learning models?
A.
REST API
B.
Batch processing
C.
Embedded systems
D.
Data warehousing
Solution
Data warehousing is not a common method for deploying machine learning models; instead, models are typically deployed via REST APIs, batch processing, or embedded systems.
