Model Versioning and Deployment Basics Quiz

1. A deployed model's performance drops significantly. What is the first step in DataX troubleshooting?

Check for data drift and validate recent prediction results

Immediately retrain the model with old data

Delete the model and deploy a random baseline

Increase the model's complexity

2. Which metric best indicates whether a model needs retraining in production?

Sustained decline in accuracy on recent validation data

The total number of days since deployment

The size of the training dataset

The programming language used to build the model

3. In DataX operations, what is the purpose of shadow deployment?

Run new model version in parallel without affecting live predictions to validate performance

Hide the model from all users

Deploy models only at night

Create a backup copy of the database

4. What does model explainability serve in production DataX systems?

Helps users understand and trust model predictions

Automatically fixes incorrect predictions

Increases model inference speed

Reduces the number of features needed

5. Which practice prevents unauthorized changes to deployed models?

Access control and audit logging of all model modifications

Keeping models in public repositories

Removing all monitoring systems

Sharing credentials with all team members

6. What is the main advantage of containerizing models with Docker?

Isolates dependencies and ensures consistent runtime across environments

Automatically improves model predictions

Eliminates the need for version control

Reduces the cost of cloud infrastructure

7. In DataX, what is the relationship between feature stores and model deployment?

Feature stores provide consistent, versioned features for reproducible predictions

Feature stores are only used during data exploration

Models cannot access feature stores in production

Feature stores replace the need for model versioning

8. What does rollback capability enable in model deployment?

Quick reversion to a previous model version if issues occur

Permanent deletion of all historical models

Automatic improvement of model accuracy

Simultaneous deployment of all versions

9. Which component is essential for automated model retraining in DataX?

Triggers that detect performance degradation and initiate pipelines

Manual email notifications to data scientists

Spreadsheets tracking model age

Monthly calendar reminders

10. What is the purpose of A/B testing in model deployment?

Compare performance of two model versions on real user traffic

Determine which color to use in model visualizations

Choose between two random hyperparameters

Decide whether to use Python or Java

11. What is the primary purpose of model versioning in DataX operations?

Track changes and maintain reproducibility of models

Increase model accuracy automatically

Reduce storage requirements

Eliminate the need for testing

12. In blue-green deployment, what do the two environments represent?

Current production (blue) and new version staging (green)

Training environment and testing environment

Development and backup servers

Data input and model output

13. What does a model registry serve in DataX operations?

Central repository to track model metadata, versions, and lineage

A database for storing raw training data

A tool for writing Python code

A payment system for cloud services

14. Which metric is most important for monitoring a deployed classification model?

Inference latency and prediction accuracy on recent data

The total number of predictions made

The size of the model file

The date the model was created

15. In DataX operations, what is a canary deployment?

Releasing a new model version to a small subset of users first

Rolling back all model versions simultaneously

Deploying models only on weekends

Using bird-themed naming conventions for versions

16. What is model drift in production environments?

Degradation of model performance due to changes in input data

Unintended movement of model files across servers

Slow network connectivity affecting predictions

Random fluctuations in processing speed

17. Which practice ensures that model versions can be reproduced exactly?

Storing data, code, and hyperparameters together with metadata

Using the latest version of all libraries

Deploying models immediately after training

Running models on different hardware each time

18. What does CI/CD stand for in the context of model deployment?

Continuous Integration / Continuous Deployment

Code Iteration / Continuous Development

Continuous Improvement / Code Design

Compilation Integration / Continuous Delivery

19. In DataX deployment pipelines, what role does containerization play?

Ensures consistency across development and production environments

Automatically improves model performance

Eliminates the need for monitoring

Reduces the number of models needed

20. Which of the following best describes semantic versioning for ML models?

MAJOR.MINOR.PATCH format based on changes and compatibility

Random alphanumeric strings assigned by the system

Sequential numbering starting from version 1.0

Timestamp-based version identification

DataX Model Versioning and Deployment Basics Quiz