PMLE Automating and Orchestrating ML Pipelines Practice Test 3 — 15 Questions

Question 1

You need to orchestrate a complex ML workflow that involves multiple Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines. The workflow must handle dependencies, retries, and monitoring. Which two services are best suited for this orchestration?

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

Accepted Answer

Cloud Composer. Cloud Composer (based on Apache Airflow) is the correct choice because it provides a managed environment for orchestrating complex workflows with dependencies, retries, and monitoring across heterogeneous services like Vertex AI pipelines, BigQuery, and Dataflow. Airflow's DAGs allow you to define task dependencies, set retry policies, and integrate with Cloud Monitoring for observability, making it ideal for multi-service ML workflows.

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Cloud Scheduler

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BigQuery scheduled queries

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Cloud Functions

Question 2

A data science team wants to deploy a ML pipeline on Vertex AI Pipelines that includes a component to train a model using a custom container. The component should be reusable across different pipelines and accept hyperparameters as inputs. Which approach should they take?

Accepted Answer

Create a container component by specifying a container image and input/output artifacts using the Kubeflow Pipelines SDK.. Container components are designed for custom containers and can be defined once and reused across pipelines. Python function components are simpler but limited to Python code; they cannot easily encapsulate custom containers.

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Package the training code as a Vertex AI Training custom job and call it from a Python function component.

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Define a Python function component using @dsl.component and pass hyperparameters as function arguments.

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Use a pre-built Google Cloud Pipeline Component for custom training and override the image.

Question 3

An ML engineer is building a pipeline on Vertex AI Pipelines and wants to pass a dataset artifact from one component to another without incurring additional cost for intermediate storage. How should they define the input and output types?

Accepted Answer

Use the Dataset artifact type available in the KFP SDK for inputs and outputs.. Using the KFP SDK's Dataset artifact type allows Vertex AI Pipelines to manage the data as a lineage-tracked artifact, automatically handling the underlying GCS storage reference without incurring additional intermediate storage costs. The artifact type enables the pipeline to pass the metadata (URI, type, etc.) between components efficiently, leveraging the native artifact management of the KFP SDK.

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Store the data in BigQuery and pass the table reference.

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Define output as a string and pass the GCS path manually.

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Use in-memory Python objects as function return values.

Question 4

A team runs a Vertex AI pipeline daily. They notice that a component that downloads a file from a public URL always executes even when the URL and parameters haven't changed. They want to avoid unnecessary re-execution and reduce costs. What should they do?

Accepted Answer

Use a pre-built Google Cloud Pipeline Component for file download.. Using a pre-built Google Cloud Pipeline Component for file download ensures the component is deterministic and has caching enabled by default, which prevents unnecessary re-execution when inputs haven't changed. Option A is a valid approach but less direct; ensuring determinism and checking caching settings is generic advice, whereas using a pre-built component immediately resolves both issues. Option B is incorrect because 'enable_caching=True' is not a valid parameter on the pipeline decorator. Option D is unnecessary because custom cache keys are not needed if the default caching works.

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Ensure the component is deterministic and that caching is not disabled for that component.

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Enable pipeline caching by setting 'enable_caching=True' on the pipeline decorator.

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Use the 'dsl.CachingOptions' to set a custom cache key for the component.

Question 5

An organization wants to trigger a Vertex AI pipeline whenever new data arrives in a Cloud Storage bucket. Which approach should they use?

Accepted Answer

Use Cloud Functions triggered by Cloud Storage events to call the Vertex AI pipeline API.. Cloud Functions can be directly triggered by Cloud Storage events (e.g., `google.storage.object.finalize`) and can then call the Vertex AI pipeline API using the Cloud SDK or client libraries. This provides a serverless, event-driven architecture that reacts immediately to new data without polling or additional infrastructure.

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Configure a Vertex AI pipeline trigger directly on the bucket using the GCP Console.

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Use Pub/Sub notifications from the bucket and a Dataflow job to start the pipeline.

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Set up a Cloud Scheduler job that runs every minute and checks for new files in the bucket.

Question 6

An ML engineer is using Cloud Composer (Airflow) to orchestrate a ML workflow. They need to run a Vertex AI pipeline as one of the tasks in the DAG. Which Airflow operator should they use?

Accepted Answer

VertexAIPipelineRunOperator or the Google Cloud Pipeline operator.. Cloud Composer (Airflow) natively supports the `VertexAIPipelineRunOperator` (or its alias `GoogleCloudPipelineOperator`), which is specifically designed to trigger and monitor a Vertex AI pipeline run as a task within a DAG. This operator handles authentication, pipeline job submission, and status polling without requiring custom code, making it the idiomatic choice for orchestrating Vertex AI pipelines from Airflow.

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BigQueryOperator to run the pipeline as a query.

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PythonOperator with a custom script using the google-cloud-aiplatform library.

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VertexAIPipelineRunOperator (or Airflow's GCSToGCSOperator) for pipeline orchestration.

Question 7

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

Accepted Answer

Use dsl.Condition (deprecated) or dsl.If to check metrics and conditionally run deployment.. KFP provides `dsl.Condition` (deprecated) and `dsl.If` as first-class pipeline constructs to conditionally execute pipeline components based on runtime metrics or other pipeline outputs. By wrapping the deployment step inside a `dsl.If` block that checks whether evaluation metrics exceed a threshold, the pipeline can deploy the model to a staging endpoint only when the condition is met, avoiding unnecessary deployments for underperforming models.

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Use dsl.ParallelFor to evaluate and deploy in parallel.

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Use an exit handler to deploy regardless of metrics.

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Split the pipeline into two separate pipelines and run the second only if metrics are good.

Question 8

What is the primary benefit of using pipeline caching in Vertex AI Pipelines?

Accepted Answer

It reduces execution time and cost by reusing unchanged component outputs.. Pipeline caching in Vertex AI Pipelines automatically detects when a component's inputs and code have not changed from a previous execution and reuses the cached output artifacts. This avoids redundant computation, directly reducing both execution time and cost by skipping re-execution of unchanged steps.

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It encrypts data at rest.

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It automatically scales the pipeline resources.

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It enables parallel execution of components.

Question 9

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

Accepted Answer

kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json'). The Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.

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gcloud ai pipelines compile command.

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kfp.Client().upload_pipeline()

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dsl.pipeline decorator automatically compiles at runtime.

Question 10

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?

Accepted Answer

Implement a two-stage pipeline: train and deploy to staging, run evaluation tests, and if passed, deploy to production using conditional logic.. A continuous delivery pipeline with evaluation gates uses separate stages: staging deployment → evaluation → promotion to 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 11

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 12

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 13

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 allows you to bring existing artifacts (e.g., a model in Vertex AI Model Registry) into a pipeline for downstream use.

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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 14

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 used to trigger the retraining on a weekly schedule by sending a message to a Pub/Sub topic or making an HTTP request. Cloud Functions (A) is the serverless compute service that executes the retraining code in response to that trigger, and it can also be triggered directly when new data arrives (e.g., via Cloud Storage or Pub/Sub). Together, they provide both event-driven and scheduled automation without managing infrastructure.

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Cloud Composer

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Cloud Tasks

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Dataflow

Question 15

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. dsl.If for conditionals, Artifacts for passing model/data, and pre-built Vertex AI components for training.

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Manual approval via Cloud Console

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Cloud SQL for storing intermediate results