Mlops — Google Cloud ML Engineer Practice Questions
MLOps is the practice of applying DevOps principles to machine learning workflows, covering the full lifecycle from data versioning and model training to deployment, monitoring, and retraining. On Google Cloud, this encompasses Vertex AI Pipelines, Model Registry, Feature Store, and CI/CD integrations that automate the path from experimentation to production. The ML Engineer exam heavily emphasizes MLOps maturity levels, expecting candidates to design reproducible training pipelines, set up continuous evaluation and drift detection, and implement automated retraining triggers when model performance degrades.
Free questions on mlops
What is the purpose of Vertex AI Pipelines?
Free question · medium · full answer + explanation
Which approach should be used when deploying machine learning models to production?
Free question · medium · full answer + explanation
Which approach is used to detect when a deployed model's performance degrades due to changes in input data distribution?
Free question · medium · full answer + explanation
In MLOps, what is a model registry used for?
Free question · easy · full answer + explanation
Which Google Cloud service provides a unified platform for building, deploying, and managing ML models?
Free question · easy · full answer + explanation
More mlops questions in the full bank
- What does TFX Evaluator component do? Unlock answer & explanation →
- What is the primary objective of A/B testing ML models in production? Unlock answer & explanation →
- When monitoring production ML models, which metric indicates potential data drift? Unlock answer & explanation →