PMLE Automating and Orchestrating ML Pipelines Practice Question
A team is designing a ML pipeline that includes training, evaluation, and conditional deployment. They want to use Vertex AI Pipelines. Which THREE concepts should they use? (Choose three.)
⚠ Common exam trap
PMLE often tests whether candidates confuse pipeline orchestration concepts with general GCP services, so distractors like Cloud SQL or manual approval must be recognized as non-pipeline constructs.
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
✓
Artifact types (e.g., Model, Metrics) for passing outputs
Option A is correct because Vertex AI Pipelines is built on ML Metadata, and typed artifacts such as Model, Metrics, Dataset, and Artifact let components pass structured outputs between training and evaluation steps so downstream steps and lineage tracking work correctly. Option D is correct because pre-built Google Cloud Pipeline Components (e.g., CustomTrainingJobOp, ModelEvaluationOp) provide ready-made, versioned steps for training and evaluation, reducing boilerplate and integrating natively with Vertex AI services. Option E is correct because conditional deployment requires branching logic in the pipeline graph, which is expressed with the Kubeflow Pipelines DSL construct dsl.If (or dsl.Condition) to run a deployment component only when evaluation metrics meet a threshold. Option B is not appropriate because manual approval via the Cloud Console is not a Vertex AI Pipelines concept for conditional deployment; gating is done programmatically in the pipeline DAG. Option C is not appropriate because intermediate results in Vertex AI Pipelines are passed as artifacts and metadata, not stored in Cloud SQL, which is a relational database service unrelated to pipeline data flow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Artifact types (e.g., Model, Metrics) for passing outputs
Why this is correct
Artifact types such as Model and Metrics carry typed outputs between pipeline steps, letting the evaluation component consume the trained model and emit metrics that the conditional deployment step reads. This satisfies the stem's need to pass outputs across training, evaluation and conditional deployment.
- ✗
Manual approval via Cloud Console
Why it's wrong here
Vertex AI Pipelines has no built-in manual approval step; conditional deployment is expressed through pipeline control flow and component outputs. Console approval suits CI/CD tooling or human-gated releases outside the pipeline graph, not automated Vertex pipelines.
- ✗
Cloud SQL for storing intermediate results
Why it's wrong here
Cloud SQL is a relational database service, not pipeline artefact storage; Vertex AI Pipelines passes intermediate results between components via artefacts in Cloud Storage or Artifact Registry. Cloud SQL suits application data persistence, not pipeline metadata or component outputs.
- ✓
Pre-built Google Cloud Pipeline Components for training and evaluation
Why this is correct
Pre-built Google Cloud Pipeline Components supply ready-made training and evaluation steps, removing boilerplate and ensuring outputs conform to expected artifact types. They satisfy the stem's requirement to assemble training, evaluation and conditional deployment without authoring every component from scratch.
- ✓
dsl.If for conditional execution
Why this is correct
dsl.If provides compile-time conditional branching inside a Vertex AI pipeline, so the deployment step only executes when the evaluation metrics meet the threshold. This directly satisfies the stem's requirement for conditional deployment after evaluation passes.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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