CI/CD for Machine Learning Quiz

Answers and explanations appear as you take the quiz.

1. What does CI/CD for machine learning add beyond traditional software CI/CD?
   • Automation of data and model concerns, not just code
   • Nothing — it is identical to software CI/CD
   • Only faster compilation
   • A new programming language
2. What does the 'CT' in an ML pipeline context usually stand for?
   • Code Transfer
   • Continuous Training
   • Container Testing
   • Central Telemetry
3. What is a common evaluation gate before promoting a candidate ML model?
   • Promote it whenever training completes
   • Compare only model file sizes
   • Compare it with the production baseline on agreed metrics and promote only if policy thresholds pass
   • Skip evaluation if the code tests pass
4. Which of these is a tool commonly used to orchestrate ML pipelines?
   • Microsoft Word
   • VLC Media Player
   • WinRAR
   • Kubeflow Pipelines
5. Why might an ML pipeline include a data schema validation step?
   • To catch malformed or drifted input data before it corrupts training
   • To make the GPU run cooler
   • To compress the dataset
   • To rename the columns randomly
6. In Flux GitOps, what does reconciliation aim to do?
   • Retrain every model whenever code is committed
   • Make the deployed environment match the declared desired state
   • Automatically change a model’s evaluation labels
   • Replace version-controlled configuration with manual edits
7. What is a useful model-artifact versioning practice?
   • Ignore training inputs after a model is deployed
   • Replace validation with a deployment timestamp
   • Version model artifacts with source data, code, and evaluation metadata
   • Use a schema check as a guarantee of model quality
8. What is a typical trigger for continuous training in an ML pipeline?
   • Every inference request must retrain the model
   • A model file being downloaded by a user
   • A CPU core reaching a particular temperature
   • A scheduled run, new approved data, or a monitoring signal such as drift