FREE Data Science Model Evaluation & Deployment Questions and Answers

Question 1

When evaluating a classification model for a rare disease diagnosis where false negatives are extremely costly (missing a disease), which metric should be prioritized?

Accepted Answer

Recall (Sensitivity)

Answer

Precision

Answer

Accuracy

Answer

F1-Score

Question 2

A data science team has developed a new fraud detection model and is ready to deploy it. Which of the following is a crucial first step in the model deployment process after initial testing, but before full-scale production rollout?

Accepted Answer

Implementing A/B testing or shadow deployment to compare performance with the existing solution.

Answer

Retraining the model on the full production dataset immediately.

Answer

Discontinuing the old model and switching directly to the new one.

Answer

Archiving the model artifacts and moving on to the next project.

Question 3

Which of the following best describes the primary purpose of a 'champion-challenger' framework in model deployment?

Accepted Answer

To compare the performance of a new model (challenger) against the currently deployed model (champion) in a live environment.

Answer

To allow multiple data scientists to work on the same model simultaneously.

Answer

To establish a competition among different machine learning algorithms to find the best one during development.

Answer

To deploy models in a way that minimizes computational resources.

Question 4

When deploying a real-time prediction service for a machine learning model, which architectural pattern is commonly used to ensure low latency and high availability?

Accepted Answer

A serverless function (e.g., AWS Lambda, Azure Functions) wrapping the model.

Answer

Batch processing on a daily schedule.

Answer

Manual model updates and redeployments every time new data arrives.

Answer

Storing predictions in a static file that is manually downloaded by clients.

Question 5

A data scientist observes that their deployed model's performance has significantly degraded over time, even though the underlying data distribution in training appeared stable. What phenomenon is most likely occurring?

Accepted Answer

Data drift or concept drift.

Answer

Overfitting on the training data.

Answer

Hyperparameter tuning issues.

Answer

Insufficient feature engineering.

Question 6

Which of the following is a critical aspect of model governance and MLOps (Machine Learning Operations) that ensures reproducibility and traceability of deployed models?

Accepted Answer

Version control for model code, data, configurations, and trained model artifacts.

Answer

Minimizing the number of features used in the model.

Answer

Using only open-source machine learning libraries.

Answer

Only deploying models that achieve 100% accuracy on test data.