PMLE Automating and Orchestrating ML Pipelines Practice Test 6 — 15 Questions

Question 1

A company has a CI/CD pipeline that retrains a model every time new training data is available. They want to automatically deploy the new model to production only if it passes a set of evaluation tests on a staging environment. Which approach best implements this?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement a two-stage pipeline: train and deploy to staging, run evaluation tests, and if passed, deploy to production using conditional logic.. The correct approach is a two-stage pipeline: train and deploy to a staging environment, run automated evaluation tests against the staged model, and use conditional logic to promote to production only if the tests pass. This implements a proper ML CI/CD gate that prevents regressions from reaching production.

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Use Cloud Build to trigger a training job and then a separate deployment job without evaluation.

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Use a single Vertex AI pipeline that trains and deploys to staging, then manually promote.

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Train and deploy directly to production in one pipeline.

Question 2

Which of the following is a best practice when designing idempotent pipeline components in Vertex AI?

Accepted Answer

Pass data through Cloud Storage URIs rather than in-memory.. Passing data through Cloud Storage URIs ensures that component outputs are stored persistently and can be retrieved by downstream components, even if the original component instance is terminated or scaled down. This aligns with the principle of idempotency because the same input will always produce the same output stored at the same URI, and re-running the component will not cause side effects or data loss. In contrast, in-memory data is ephemeral and tied to a specific runtime instance, breaking idempotency across retries or parallel executions.

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Use global variables to share state between components.

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Write component outputs to a database with timestamps.

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Use the same output name for all runs to avoid duplication.

Question 3

An ML team wants to run a hyperparameter tuning job on Vertex AI using a pre-built pipeline component. Which component should they use?

Accepted Answer

HyperparameterTuningJobRunOp. The HyperparameterTuningJobRunOp is the correct pre-built Vertex AI pipeline component specifically designed to launch a hyperparameter tuning job. It wraps the Vertex AI HyperparameterTuningJob API, allowing you to specify the worker pool spec, metric target, and parameter specifications directly within a Kubeflow Pipelines (KFP) or Vertex AI Pipelines orchestration context.

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AutoMLTabularTrainingJobRunOp

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CustomTrainingJobRunOp with hyperparameter arguments.

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ModelTrainComponent

Question 4

What is the purpose of the 'importer' component in Vertex AI Pipelines?

Accepted Answer

To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.. The Importer component in Vertex AI Pipelines (a Kubeflow Pipelines component) brings existing ML artifacts — such as pre-trained models, datasets, or other resources already registered in Vertex AI — into a pipeline as inputs. It creates an artifact reference so downstream components can consume it without regenerating it. This is essential for reusing models or datasets across pipeline runs.

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To import data from external sources into the pipeline.

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To import Python libraries into the pipeline environment.

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To import pipeline definitions from other projects.

Question 5

Your organization wants to automate the retraining of a model when new data is available and also on a weekly schedule. Which TWO services would you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Functions. Cloud Scheduler (E) is the correct service for the time-based trigger, since it can invoke a target on a cron schedule such as weekly, which satisfies the 'weekly schedule' requirement. Cloud Functions (A) is correct because it provides the serverless compute that actually executes the retraining logic, and it can be triggered both by Cloud Scheduler for the weekly run and by an event (for example, a Cloud Storage object finalize event when new data lands) for the data-availability requirement. Together, Cloud Scheduler fires on the cron schedule and Cloud Functions runs the retraining code, covering both triggers. Cloud Composer (B) is a managed Airflow workflow orchestrator and could schedule DAGs, but it is heavier than needed and is not the marked pairing for this scenario. Cloud Tasks (C) is a queue for asynchronous task dispatch, not a cron scheduler or compute runtime, and Dataflow (D) is a managed Apache Beam service for data processing pipelines, not for triggering or hosting model-retraining logic.

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Cloud Composer

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Cloud Tasks

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Dataflow

Question 6

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.)

Accepted Answer

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.

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Manual approval via Cloud Console

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Cloud SQL for storing intermediate results

Question 7

An ML engineer is building a continuous training pipeline that retrains a model when new data arrives. The pipeline should also detect skew between training and serving data. Which TWO Google Cloud services should they use? (Choose two.)

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Pipelines (D) is correct because it is the managed service for orchestrating and automating ML workflows, allowing the engineer to define a pipeline that triggers retraining when new data arrives. Vertex AI Model Monitoring (B) is correct because it is specifically designed to detect training-serving skew and drift by comparing the statistical distribution of incoming prediction requests against the training data baseline. Together, these two services directly satisfy both requirements: continuous training orchestration and skew detection. Cloud Logging (A) only stores and queries log entries and does not orchestrate pipelines or compute skew. Cloud Functions (C) is a lightweight event-driven compute service that could trigger code but lacks native ML pipeline orchestration and skew detection. Cloud Monitoring (E) tracks operational metrics and alerts but does not perform training-serving skew analysis for ML models.

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Cloud Logging

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Cloud Functions

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Cloud Monitoring

Question 8

A data scientist wants to define a lightweight Python function component in Vertex AI Pipelines using Kubeflow Pipelines SDK v2. Which decorator should be applied to the function to make it a pipeline component?

Accepted Answer

@dsl.component. In Kubeflow Pipelines SDK v2, the @dsl.component decorator converts a lightweight Python function into a pipeline component. This is the standard v2 approach for defining function-based components. @dsl.pipeline is used to define the pipeline itself, not individual components.

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@dsl.pipeline

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@kfp.v2.components.func_to_component

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@kfp.dsl.component

Question 9

An ML engineer wants to containerize a custom training script and use it as a component in a Vertex AI Pipeline. The component should accept a dataset URI and a learning rate parameter, and output a trained model artifact. Which approach should the engineer use to define the component?

Accepted Answer

Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.. ContainerComponent from kfp.v2.components allows you to define a custom container component by specifying the container image, command, inputs, and outputs directly. This is the appropriate approach when you have a custom training script that you want to containerize and use as a component in a Vertex AI Pipeline, as it gives you full control over the container configuration and artifact handling.

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Use a pre-built Google Cloud Pipeline Component for Vertex AI Training with custom container configuration.

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Define a Python function component with @dsl.component and include the container code inline.

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Use the importer component to import the script and then run it as a task.

Question 10

An ML engineer needs to trigger a Vertex AI Pipeline on a recurring schedule, every 24 hours, to retrain a model with the latest data. Which approach should they use to set up this schedule?

Accepted Answer

Create a Cloud Scheduler job that calls the Vertex AI API to create a pipeline job.. Cloud Scheduler is the native Google Cloud service for cron-based job scheduling. By configuring a Cloud Scheduler job to call the Vertex AI API (e.g., via a HTTP POST to the projects.locations.pipelineJobs.create endpoint), the engineer can trigger a pipeline run every 24 hours. This approach is reliable, supports authentication via OAuth, and integrates directly with Vertex AI Pipelines without requiring additional orchestration code.

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Use Cloud Tasks to queue pipeline runs daily.

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Set a cron expression in the pipeline definition file using the 'schedule' parameter.

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Use the Vertex AI Pipelines UI to set a schedule directly on the pipeline.

Question 11

A company wants to implement continuous delivery (CD) for ML models, where a model is automatically deployed to a staging environment and only promoted to production after passing an evaluation gate. Which combination of GCP services is BEST suited for orchestrating this CD pipeline?

Accepted Answer

Cloud Build with Vertex AI Pipelines and Cloud Deploy. Cloud Build can trigger on code/model changes and run a pipeline that deploys to staging. After evaluation, if successful, it can promote to production using Cloud Deploy or directly update Vertex AI endpoints. Cloud Composer (Airflow) is also a good option for complex orchestration, but for CI/CD, Cloud Build is a natural fit. The combination of Cloud Build, Cloud Deploy, and Vertex AI provides a robust CD pipeline.

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Cloud Scheduler and Pub/Sub

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Cloud Composer (Airflow) with Cloud Functions

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Vertex AI Pipelines with Cloud Run

Question 12

An organization wants to use Cloud Composer (Airflow) to orchestrate a machine learning workflow that includes running a Vertex AI Pipeline, followed by a BigQuery job, and then a Dataflow pipeline. What is the primary advantage of using Cloud Composer for this orchestration?

Accepted Answer

It allows orchestrating heterogeneous workflows across multiple GCP services with dependencies and retries.. Cloud Composer (Apache Airflow) is designed to orchestrate heterogeneous workflows across multiple GCP services. In this scenario, it can define a Directed Acyclic Graph (DAG) that runs a Vertex AI Pipeline, then a BigQuery job, and finally a Dataflow pipeline, with built-in support for dependency management, retries, and failure handling. This is the primary advantage because it allows you to coordinate disparate services in a single, reliable workflow.

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It automatically caches the outputs of each step to avoid recomputation.

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It integrates natively with the Vertex AI Model Registry for model versioning.

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It provides a serverless execution environment for ML pipelines.

Question 13

An ML engineer is designing a pipeline that should run only when new training data arrives in a Cloud Storage bucket. Which event-driven approach should they use to trigger the Vertex AI Pipeline?

Accepted Answer

Use Cloud Storage Pub/Sub notifications to send events to a Cloud Function that triggers the pipeline.. The best approach is to use Cloud Storage notifications via Pub/Sub, then a Cloud Function that receives the event and calls the Vertex AI API to create a pipeline job. This is a common event-driven pattern. Cloud Scheduler is for scheduled triggers, not event-driven. Cloud Tasks and Cloud Run are not typically used for this purpose.

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Use Cloud Tasks to queue a pipeline run whenever a new file is uploaded.

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Configure the pipeline to run on a schedule and check for new data inside the pipeline.

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Set up a Cloud Scheduler job that runs every minute to check for new files.

Question 14

A team is implementing CI/CD for ML using Cloud Build. They want to trigger a training pipeline in Vertex AI whenever a new model code is pushed to the main branch of the repository. Which Cloud Build configuration should they use to achieve this?

Accepted Answer

Set up a Cloud Build trigger that runs on push to main branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.. Cloud Build triggers can be configured to fire specifically on pushes to the main branch. The build step then uses the gcloud command to submit a Vertex AI Pipeline job, which directly integrates the CI/CD pipeline with Vertex AI's orchestration. This approach is event-driven, immediate, and requires no additional services or polling.

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Set up a Cloud Build trigger that runs on push to any branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.

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Use a Cloud Scheduler job to periodically check for new commits on main and trigger Cloud Build.

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Use Cloud Functions to watch the repository and call Cloud Build on push to main.

Question 15

In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?

Accepted Answer

Add a component that reads the artifact's metadata and outputs the metric as a parameter, then use that parameter in the condition.. Vertex AI Pipeline conditions require pipeline parameters (typed values) to evaluate expressions like `dsl.If`. A Metrics artifact's metadata is stored as an artifact property, not a pipeline parameter, so a custom component must read that metadata and output the metric as a parameter. This parameter can then be used in the `dsl.If` condition to control downstream deployment.

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Configure the component that produces the Metrics artifact to also output the metric as a pipeline parameter.

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Use the importer component to convert the artifact into a parameter.

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Use the artifact directly in the dsl.If condition, as artifacts are comparable.