Automating Model Retraining PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the purpose of a rollback strategy in an ML deployment pipeline?

   • ATo increase the model's inference speed
   • BTo revert to a previous model version if the new deployment performs poorly
   • CTo delete the old model version from storage
   • DTo automatically retrain the model with new data
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2. 22application · medium

   A team is building an automated ML pipeline that includes data preprocessing, model training, model evaluation, and deployment. They want to orchestrate these steps so that each step runs only after the previous one succeeds. What should they use?

   • AA Cloud Function that triggers each step manually.
   • BA single Cloud Build step that runs all the steps in a shell script.
   • CA Kubernetes cron job that runs the steps in parallel.
   • DVertex AI Pipelines to define the steps as components and run them in sequence.
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   A machine learning engineer wants to automatically retrain a model when the statistical properties of the input data change significantly. Which retraining trigger is most appropriate for this scenario?

   • APerformance degradation monitoring
   • BA fixed weekly schedule
   • CData drift detection
   • DManual retraining after each data release
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5. 24foundation · easy

   Why is monitoring important in a CI/CD/CT pipeline for ML models?

   • ATo replace the need for model validation
   • BTo detect issues such as data drift or performance degradation after deployment
   • CTo automatically fix code bugs in the pipeline
   • DTo reduce the cost of training
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6. 25expert · hard

   A media company has a content recommendation model that is retrained daily. The cost of training is high, and the team is considering reducing the frequency to weekly to save costs. However, they are concerned about model freshness. What is the best way to evaluate this trade-off?

   • AAnalyze the historical performance of the model with different retraining frequencies and compare the cost savings against the impact on key metrics like click-through rate.
   • BKeep daily retraining because model freshness is always more important than cost.
   • CSwitch to weekly retraining immediately and monitor the model's performance for a month.
   • DUse a cost-benefit analysis that only considers the cost of training and ignores the impact on model performance.
   Reveal Solution Discussion Select an answer first