MLOps Best Practices MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which statement best describes the primary role of an MLOps strategy in managing the machine learning lifecycle?

   • ATo provide a structured approach for managing the entire ML lifecycle, from development to deployment and ongoing monitoring
   • BTo ensure that all machine learning models are deployed using a single, standardized algorithm
   • CTo replace data scientists with automated pipelines that require no human intervention
   • DTo automate the deployment of models to production and ensure they remain operational
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2. 2application · medium

   An e-commerce company has a recommendation model that is retrained weekly. The team notices that model performance in production has been declining over time, but they are unsure whether it is due to changes in user behavior or a bug in the training pipeline. What should they implement to diagnose the issue?

   • ARely on the data science team to manually review the model each week.
   • BIgnore the decline and wait for the next retraining cycle.
   • CSet up monitoring for both model performance metrics and data drift detection.
   • DOnly monitor the model's accuracy on the training data.
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4. 3application · medium

   A financial services company is adopting MLOps to manage multiple models that are retrained monthly. The team wants to ensure that only validated models are deployed to production and that any model can be quickly rolled back if performance degrades. Which practice is most important to implement?

   • ADeploy every new model version directly to production to keep the system up-to-date.
   • BRetrain all models on the first day of every month without any validation.
   • CUse the model registry to manage model versions and transition models through stages like Staging and Production.
   • DKeep all model versions in a shared folder and manually select the best one.
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5. 4expert · hard

   A financial company has a fraud detection model that must be updated quickly when new fraud patterns emerge. However, the compliance team requires that every model change go through a rigorous validation process. The team is struggling to balance speed and compliance. What is the best approach?

   • AAutomate the validation process so that models can be tested and approved quickly while still meeting compliance standards.
   • BDeploy the model to production and validate it after a week of live traffic.
   • CUse a manual validation process that takes several weeks.
   • DSkip validation for urgent updates to deploy faster.
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6. 5expert · hard

   A manufacturing company is implementing MLOps for its predictive maintenance models. The team has multiple models for different equipment types, and each model is retrained on a different schedule. They want to centralize monitoring and management. What is the most effective strategy?

   • AFocus on the most important model and ignore the others.
   • BUse the same retraining schedule for all models to simplify management.
   • CManage each model separately with its own tools and processes.
   • DUse a central model registry and monitoring dashboard to manage all models consistently.
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