Model Lifecycle Management MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   In the deploy code strategy, what is the primary purpose of a rollback mechanism?

   • ATo move a model from development to staging
   • BTo automatically update the model in production with the latest training data
   • CTo revert to a previous model version if the new version performs poorly in production
   • DTo delete old model versions from the registry
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2. 2application · medium

   A team needs to share a model with another team for integration testing. The model is in the staging stage in the Model Registry. The other team needs to access the model artifact. What is the best way to provide access?

   • ACopy the model artifact to a shared location and send the path.
   • BGrant the other team read permissions on the registered model in the Model Registry.
   • CExport the model as a pickle file and email it.
   • DTransition the model to 'Production' so the other team can access it.
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4. 3foundation · easy

   Which component of a model lifecycle pipeline is responsible for moving a validated model from the staging environment to the production environment?

   • AModel monitoring
   • BFeature store
   • CModel Registry
   • DCI/CD pipeline
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5. 4application · medium

   A company is building a model lifecycle pipeline. They want to use a single codebase to deploy models to development, staging, and production environments. The code should be identical across environments, but configuration values (e.g., database connections, model paths) should differ. Which approach best supports this?

   • AStore configuration values in the code and change them before each deployment.
   • BUse environment variables and configuration files that are selected based on the deployment target.
   • CCreate separate branches in the repository for each environment with hardcoded configurations.
   • DUse a single configuration file that contains all environment settings and comment out the ones not in use.
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6. 5application · medium

   A team uses MLflow Model Registry to manage models. They have a CI/CD pipeline that deploys a model to the staging environment after it passes tests. The model is registered in the registry with a version number. The team wants to promote the model to production only after manual approval by a data scientist. Which action should the team take to implement this promotion step?

   • ADelete the staging version and create a new version in production.
   • BRe-register the model with a new name and version for production.
   • CChange the model version stage to 'Production' in the registry after approval.
   • DUpdate the model's description to indicate it is in production.
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