CI/CD Essentials for Machine Learning Projects: A Beginner’s Quiz Quiz

1. Understanding CI/CD

   What does CI/CD stand for with respect to machine learning project workflows?

   1. Continuous Integration / Continuous Delivery
   2. Computer Instructions / Code Division
   3. Continuous Improvement / Code Deployment
   4. Central Integration / Continuous Development
   5. Continuous Innovation / Coded Delivery

2. Main CI Purpose

   In a machine learning context, what is the primary goal of Continuous Integration (CI)?

   1. Automatically merging and testing code changes
   2. Manually combining all new models
   3. Integrating cloud storage data
   4. Delivering updates to end-users only
   5. Initiating continuous data collection

3. Detecting Errors Early

   How does implementing CI/CD in ML projects help with the early detection of code errors?

   1. By running automated tests on every code commit
   2. By letting all errors accumulate before release
   3. By deploying only final version models
   4. By requiring manual code review each time
   5. By avoiding any code testing processes

4. CD and Deployment

   Which best describes Continuous Delivery (CD) in a machine learning project?

   1. Ensuring model and code are always ready to deploy automatically
   2. Delivering data sets continuously to the pipeline
   3. Deploying models once per year only
   4. Discontinuing outdated models manually
   5. Delaying deployments until all branches are merged

5. Model Versioning

   Why is model versioning important in a CI/CD pipeline for machine learning?

   1. To track changes and reproduce previous results accurately
   2. To skip testing on each update
   3. To merge model and code changes directly without logs
   4. To hide data transformations
   5. To reduce the number of pipeline steps

6. Reproducibility

   Suppose a team member updates the training script; how can CI/CD pipelines assist reproducibility?

   1. By automatically running the training and logging results each time
   2. By preventing any changes to training scripts
   3. By skipping retraining models
   4. By randomly changing parameters for each run
   5. By using manual notebook execution

7. Common Pipeline Steps

   Which is typically NOT a standard step in a machine learning CI/CD pipeline?

   1. Sending automated bug reports to clients
   2. Automated testing and validation of code
   3. Building model artifacts
   4. Deploying models to production
   5. Data validation steps

8. Benefits of Automation

   What is one key benefit of automating model deployment through CI/CD for ML projects?

   1. Reducing manual errors during deployment
   2. Increasing the number of bugs introduced
   3. Removing all data preprocessing
   4. Eliminating the need for model evaluation
   5. Limiting the frequency of deployments

9. Testing Data Pipelines

   Which aspect can a CI/CD pipeline automatically check in the data pipeline of a machine learning project?

   1. Whether data meets expected format and quality standards
   2. If the pipeline only works with one dataset
   3. How users visualize the data
   4. The color scheme of project reports
   5. The font size of code comments

10. Rollback Safety

    If a new model version deployed by a CI/CD pipeline performs worse than before, what should ideally happen?

    1. The system automatically rolls back to the previous stable model
    2. The deployment process is permanently stopped
    3. All previous models are deleted
    4. The system ignores performance differences
    5. Developers manually email users