Model Lifecycle Management MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which action is an example of a promotion mechanism in a model lifecycle pipeline?

   • AChanging the model version's stage from 'Staging' to 'Production' in the Model Registry
   • BQuerying the model registry to list all registered models
   • CRecording the model's training metrics in MLflow
   • DDeleting a model version that failed validation
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2. 12expert · medium

   A company is required to maintain a complete lineage of model training data for regulatory compliance. They use Delta Lake for data storage and MLflow for tracking. What is the most effective way to ensure that the exact training data version is recorded for each model run?

   • ALog the Delta Lake table path and version as parameters in the MLflow run.
   • BRegister the model in the Model Registry with the dataset name.
   • CUse Delta Lake time travel to query the data at the time of training.
   • DStore a copy of the training data in the MLflow artifact store for every run.
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4. 13application · medium

   A team uses a CI/CD pipeline to deploy models. They want to ensure that the model version deployed to production is exactly the one that was tested in staging. Which practice should they follow?

   • AUse the same model version from the registry for both staging and production deployments.
   • BRe-train the model in production to ensure it is up-to-date.
   • CDeploy the latest model version from the registry regardless of stage.
   • DCopy the model artifact from staging to production manually.
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5. 14expert · medium

   A team has a model in production that is performing well, but they have trained a new model with better metrics. They want to deploy the new model to a subset of users (canary deployment) before full rollout. Which approach should they use?

   • ATransition the new model to 'Production' and immediately replace the old model.
   • BUse Delta Lake to store the new model and serve it from there.
   • CRegister the new model version and use a serving endpoint that routes a percentage of traffic to the new version.
   • DDeploy the new model to a separate endpoint and manually redirect some users.
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6. 15application · medium

   A team deploys a model to production using a CI/CD pipeline. After deployment, they notice a data drift issue. They want to automatically roll back to the previous model version if the model's performance metric drops below a threshold. Which approach should they use?

   • ACreate a new model version with the old code and deploy it.
   • BSet up a monitoring job that checks the metric and triggers a stage transition to the previous version.
   • CUse Delta Lake to revert the training data to an earlier version.
   • DManually monitor the model and roll back when issues are noticed.
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