PMLE Automating and Orchestrating ML Pipelines Practice Test 5 — 15 Questions

Question 1

An ML engineer is creating a Vertex AI Pipeline that includes a loop to train multiple models in parallel on different hyperparameter sets. Which TWO KFP SDK v2 constructs can be used to implement this parallel execution?

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

Accepted Answer

A for loop in the pipeline function that creates multiple tasks. In KFP SDK v2, you can use a Python for loop inside the pipeline function to dynamically create multiple task instances, each with different hyperparameter sets. These tasks are then executed in parallel by the Vertex AI Pipelines orchestrator, as long as there are no data dependencies between them. This pattern leverages the SDK's ability to compile Python control flow into a directed acyclic graph (DAG) of pipeline steps.

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dsl.If

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dsl.Parallel

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dsl.Pipeline

Question 2

A data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?

Accepted Answer

Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.. Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.

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Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.

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Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.

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Define each step as a separate Cloud Build step and chain them via build triggers.

Question 3

A machine learning engineer has a Vertex AI pipeline that trains a model. The pipeline uses caching to avoid re-running components that have not changed. After updating the training code, the engineer notices that the pipeline still uses cached outputs from the previous run. What could be the reason?

Accepted Answer

The base image used for the component has not changed, so the cache key matches despite code changes inside the container.. In Vertex AI Pipelines, caching uses a cache key derived from the component source code, input parameters, and the base image digest. If only the training code inside the container changes but the base image digest remains the same, the cache key does not change, resulting in a cache hit from the previous run. This explains why the pipeline still uses cached outputs. Option A is incorrect because changing parameter values would change the cache key, causing a miss. Option B is incorrect because pre-built components support caching unless explicitly disabled. Option D would disable caching entirely, which is not the case here.

Answer

The pipeline parameter values have changed, causing a cache hit.

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The pipeline is using a pre-built component that ignores caching.

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The component has caching disabled via the @dsl.component decorator.

Question 4

A team is building a CI/CD pipeline for an ML model. They want to automatically trigger a Vertex AI pipeline for retraining whenever new training data arrives in a Cloud Storage bucket, but only if a specific Pub/Sub notification is published by a data ingestion process. Which approach meets these requirements with minimal operational overhead?

Accepted Answer

Use Eventarc to route the Pub/Sub notification to a Cloud Function that calls the Vertex AI pipeline creation API.. Eventarc can directly listen to a Pub/Sub topic and route matching messages to a Cloud Function, which then calls the Vertex AI pipeline creation API. This serverless approach triggers the pipeline only when the specific Pub/Sub notification is published, meeting the requirement with zero infrastructure to manage and no polling overhead.

Answer

Use Cloud Scheduler to run a job every hour that checks for new files in Cloud Storage and starts the pipeline if new files exist.

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Configure a Cloud Build trigger that listens to the Pub/Sub topic and executes a build step that submits the pipeline run.

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Create a Dataflow streaming pipeline that reads from Pub/Sub and triggers the Vertex AI pipeline via a custom sink.

Question 5

A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?

Accepted Answer

@dsl.component. The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.

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@dsl.task

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@dsl.pipeline

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@dsl.container

Question 6

A company uses Cloud Composer to orchestrate a nightly ML workflow that includes running a Vertex AI pipeline, querying BigQuery, and running a Dataflow job. The Airflow DAG must run only if the previous day's Dataflow job succeeded. Which Airflow concept should they use to implement this dependency?

Accepted Answer

Use the bitshift operators (>>) to set the execution order: Dataflow_task >> VertexAI_pipeline.. Airflow's bitshift operators (>>) define task dependencies in a DAG. By setting `Dataflow_task >> VertexAI_pipeline`, the Vertex AI pipeline task will only execute after the Dataflow task has completed successfully. This directly enforces the required dependency without additional logic or branching.

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Use a BranchPythonOperator to check the status of the Dataflow job before proceeding.

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Nest the tasks in a SubDAG with a schedule_interval that starts after the expected Dataflow completion time.

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Set a TriggerRule on the Vertex AI pipeline task to 'all_done' and reference the previous task.

Question 7

A machine learning team wants to implement a continuous delivery pipeline for their ML models using Vertex AI Pipelines. The pipeline should automatically deploy a model to a staging endpoint after evaluation passes, and then after manual approval, promote it to production. Which strategy should they use to manage model versions in the Vertex AI Model Registry?

Accepted Answer

Use Vertex AI Model Registry aliases: assign the model version an alias 'staging' initially, and after approval, change the alias to 'production' via the API.. Vertex AI Model Registry supports versioning and alias management. By uploading models with an alias like 'staging' and later changing the alias to 'production', the team can control promotion. The other options either lack the required functionality or add unnecessary complexity.

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Store model artifacts in Cloud Storage with versioned paths and deploy directly from Storage without using the Model Registry.

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Create two separate models in the registry: one for staging and one for production, and copy artifacts between them after approval.

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Upload each model version with the same model ID and use labels to differentiate staging vs production.

Question 8

A data scientist is defining a Vertex AI pipeline and needs to include a step that imports a pre-existing model from Cloud Storage into the pipeline as an artifact. Which Kubeflow Pipelines SDK v2 component should they use?

Accepted Answer

dsl.importer. The `dsl.importer` component in Kubeflow Pipelines SDK v2 is specifically designed to import existing artifacts (such as models, datasets, or metrics) from external storage (e.g., Cloud Storage) into a pipeline as a pipeline artifact. It allows you to reference a pre-existing model without retraining or re-uploading, making it the correct choice for this use case.

Answer

dsl.Collected

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dsl.Importer

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dsl.Artifact

Question 9

A company uses Vertex AI Pipelines to train models on a daily schedule. The pipeline includes a component that runs a BigQuery query to extract features. The team wants to ensure that if the BigQuery component fails due to transient network errors, the pipeline automatically retries it. How can they configure retries in Vertex AI Pipelines?

Accepted Answer

Set the `retry` parameter of the component to a positive integer, for example `retry=3`.. Vertex AI Pipelines natively supports a `retry` parameter on pipeline components. Setting `retry=3` instructs the pipeline to automatically retry the component up to three times if it fails due to transient errors, such as network timeouts. This is the simplest and most direct way to handle retries within the Vertex AI Pipelines orchestration framework.

Answer

Deploy the component as a Cloud Function and configure Cloud Functions retry.

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Wrap the component in a `dsl.If` conditional that checks for failure and re-submits the component.

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Use Cloud Composer with a task retry policy in Airflow.

Question 10

A machine learning engineer needs to create a pipeline that runs a custom container component on Vertex AI. The container expects a Cloud Storage path as input and outputs a model artifact. Which component type should they define using the Kubeflow Pipelines SDK v2?

Accepted Answer

Container component using @dsl.container_component. The Kubeflow Pipelines SDK v2 provides the @dsl.container_component decorator specifically for defining components that wrap custom container images. This allows the engineer to specify the container image, input/output paths (like a Cloud Storage path), and artifact metadata, enabling Vertex AI to execute the container as a pipeline step and capture the model artifact.

Answer

Google Cloud Pipeline Components (GCPC) for custom containers

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Python function component using @dsl.component

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Importer component to load the container as an artifact

Question 11

A team is building a continuous training pipeline that retrains a model when new data arrives. They want to detect data drift between the training dataset and the serving data. Which approach should they integrate into the pipeline to compare the distributions of the two datasets?

Accepted Answer

Use Vertex AI Model Monitoring with skew detection enabled.. Vertex AI Model Monitoring with skew detection enabled is the correct approach because it is specifically designed to detect data drift between training and serving datasets in a continuous training pipeline. It automatically computes distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) for each feature and compares the training data distribution against the serving data distribution, triggering alerts when drift exceeds a configured threshold. This integrates natively with Vertex AI Pipelines, enabling automated retraining workflows.

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Export metrics to Cloud Monitoring and set up alerting on mean values.

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Use Cloud DLP to inspect the datasets and generate summary statistics.

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Compute histograms of features in BigQuery ML and compare them manually.

Question 12

An organization wants to trigger a Vertex AI pipeline whenever a new commit is pushed to the main branch of their Cloud Source Repository. The pipeline should retrain and evaluate the model. Which service should they use to detect the push event and start the pipeline?

Accepted Answer

Cloud Build trigger. Cloud Build triggers are designed to automatically invoke a build pipeline in response to events from Cloud Source Repository, such as a push to a specific branch. This allows the organization to directly start a Vertex AI pipeline for retraining and evaluation without additional infrastructure. Cloud Build triggers natively integrate with Cloud Source Repository, making them the simplest and most reliable choice for this event-driven workflow.

Answer

Vertex AI Pipeline schedule

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Cloud Scheduler on a short interval

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Pub/Sub with push subscription to a Cloud Function

Question 13

A machine learning engineer is building a pipeline with Vertex AI Pipelines and wants to pass a large dataset between components without copying it to the container's memory. What is the best practice for passing data between pipeline components?

Accepted Answer

Use Cloud Storage URIs (gs://) to point to the data location.. Vertex AI Pipelines natively supports passing Cloud Storage URIs (gs://) as artifact references between components, allowing components to read the dataset directly from GCS without copying it into container memory. This avoids memory limits and enables efficient handling of large datasets by leveraging GCS's scalable object storage.

Answer

Mount an NFS volume to all containers and share data via the filesystem.

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Serialize the dataset to JSON and include it as a pipeline parameter.

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Use the importer component to load the data into the pipeline as an in-memory artifact.

Question 14

A data science team is designing a Vertex AI pipeline that includes a loop over a list of hyperparameter sets. They want to run training jobs in parallel for each hyperparameter set and then collect the results for comparison. Which two Kubeflow Pipelines SDK v2 features should they use? (Choose two.)

Accepted Answer

dsl.ParallelFor. `dsl.ParallelFor` is the Kubeflow Pipelines SDK v2 feature that enables iterating over a list of hyperparameter sets and executing the training tasks in parallel. This directly supports the team's requirement to run multiple training jobs concurrently for each hyperparameter configuration.

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dsl.If

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dsl.ExitHandler

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dsl.importer

Question 15

A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)

Accepted Answer

Vertex AI Evaluation component to compute model performance metrics on the validation set. Vertex AI Evaluation component can be used within a pipeline to compute model performance metrics (e.g., accuracy, precision, recall) on a validation set. This allows the pipeline to compare the newly trained model's performance against the current production model's performance, enabling the conditional logic to check if degradation exceeds 5%.

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Dataflow pipeline to clean the new data before training

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Cloud Scheduler to run the pipeline on a fixed schedule