Generative AI Leader Fundamentals of Generative AI Practice Question
A company is migrating an on-premises NLP pipeline to Vertex AI. Which three capabilities of Vertex AI align with common MLOps best practices for generative AI? (Choose THREE)
⚠ Common exam trap
Google Cloud often tests the misconception that MLOps for generative AI requires on-premises execution or manual-only labeling, but the correct answer emphasizes cloud-native automation and versioning as core best practices.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Automatic model retraining based on performance degradation
Option A is correct because Vertex AI Model Monitoring can detect training-serving skew and prediction drift (e.g., via drift/skew thresholds on feature distributions) and trigger automated retraining workflows, which is a core MLOps practice for keeping generative AI models performant over time. Option C is correct because Vertex AI Pipelines (built on Kubeflow Pipelines/Vertex AI Pipelines SDK) enables continuous training by orchestrating reproducible DAGs that ingest new data, retrain, evaluate, and redeploy models as part of a CI/CD/CT pipeline. Option E is correct because Vertex AI Model Registry provides centralized versioning, lineage, and metadata tracking of model artifacts, allowing teams to manage model versions, roll back, and promote models to endpoints—essential for governed MLOps. Option B is not correct because Vertex AI is a managed cloud service on Google Cloud, not a local on-premises execution environment. Option D is not correct because Vertex AI supports automated and programmatic labeling (e.g., Vertex AI Data Labeling Service with human-in-the-loop and active learning), so restricting to manual labeling only contradicts MLOps best practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Automatic model retraining based on performance degradation
Why this is correct
Automatic retraining triggered by performance degradation satisfies the stem's MLOps monitoring constraint, closing the loop between evaluation and remediation. Vertex AI pipelines can schedule retraining when drift or quality metrics breach thresholds, keeping the migrated NLP models current without manual intervention.
- ✗
Local on-premises execution
Why it's wrong here
Vertex AI is a managed cloud platform; local on-premises execution contradicts the migration's purpose and forfeits its managed training, pipelines and monitoring. It is tempting because on-premises tooling may already exist, and would be correct only where data residency or air-gapped rules forbid cloud processing.
- ✓
Continuous training with Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines automates retraining triggers, satisfying the MLOps requirement for continuous training as data drifts. Unlike static batch jobs, pipelines orchestrate reproducible DAGs across preprocessing, tuning and deployment, keeping generative models current without manual intervention. This directly addresses the migration's need for governed, repeatable retraining workflows.
- ✗
Manual data labeling only
Why it's wrong here
Manual data labelling alone provides no automation, versioning or reproducibility, so it cannot satisfy MLOps best practice; Vertex AI offers managed labelling and pipeline integration instead. It is tempting because labelled data underpins supervised training, and would be correct for a small, high-precision dataset needing human judgement.
- ✓
Model registry for versioning
Why this is correct
A model registry provides centralised versioning, lineage and lifecycle tracking for trained models, satisfying the migration's need for reproducible, auditable deployments. Unlike ad-hoc artefact storage, it records each model version's metadata and stage, enabling rollback and governance across the on-premises-to-Vertex AI transition.
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