PMLE Automating and Orchestrating ML Pipelines Practice Test 7 — 15 Questions

Question 1

An ML engineer is building a pipeline component that takes a dataset URI and a model URI as inputs, and outputs a classification metrics artifact. Which KFP SDK v2 type should the output artifact be annotated with?

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

Accepted Answer

ClassificationMetrics. In KFP SDK v2, the `ClassificationMetrics` type is specifically designed to output classification metrics such as confusion matrix, ROC curve, and AUC. The question asks for a component that outputs classification metrics, so `ClassificationMetrics` is the correct artifact type. Using `Metrics` would be too generic and not provide the structured schema needed for classification-specific visualizations in the KFP UI.

Answer

Dataset

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Metrics

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Model

Question 2

A company is using Vertex AI Pipelines for ML workflows. They want to implement best practices for idempotent components and data passing. Which THREE practices should they adopt?

Accepted Answer

Pass large datasets between components using GCS URIs instead of in-memory values.. Option A is correct because Vertex AI Pipelines components exchange data through artifacts, and passing large datasets as GCS URIs (e.g., gs://bucket/path) avoids serializing huge in-memory objects into the pipeline's metadata/execution store, keeping runs efficient and reproducible. Option B is correct because hard-coded paths break portability and reproducibility across environments; using pipeline parameters (or input artifacts) to supply URIs lets the same pipeline definition run against dev, test, or prod buckets without code changes. Option E is correct because idempotent components—where identical inputs always yield identical outputs and re-execution has no additional side effects—are a core Vertex AI Pipelines best practice, enabling safe retries and caching. Option C is not appropriate because passing in-memory objects between components requires serialization, can exceed metadata limits, and undermines the artifact-based data-passing model. Option D is not appropriate because global variables introduce hidden state, are not tracked as pipeline parameters or artifacts, and break reproducibility and caching across pipeline runs.

Answer

Read data into memory in the first component and pass the in-memory object to subsequent components.

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Use global variables in the pipeline code to store intermediate results.

Question 3

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 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 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 7

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 8

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 9

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 10

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.

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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 11

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. Option B is correct because the Vertex AI Evaluation component is the mechanism that computes model performance metrics (such as accuracy, AUC, or RMSE) on a validation set, which is exactly what is needed to compare the newly trained model against the current production model and detect a degradation greater than 5%. Option C is correct because Cloud Functions can be configured with a Cloud Storage trigger (via Eventarc) to fire when new data objects arrive in a bucket, thereby automatically kicking off the retraining pipeline without manual intervention. Option D is correct because the Vertex AI Model Registry uses aliases (for example, a 'production' alias) to point to a specific model version, and updating that alias is how a newly validated model gets promoted to production once it passes the performance threshold. Option A is not required by the scenario, since the question focuses on triggering, evaluating, and promoting models rather than on data cleaning, and no data quality issue is stated. Option E is incorrect because Cloud Scheduler runs jobs on a fixed time-based schedule, whereas the requirement is event-driven retraining triggered by the arrival of new data.

Answer

Dataflow pipeline to clean the new data before training

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Cloud Scheduler to run the pipeline on a fixed schedule

Question 12

A machine learning team uses Vertex AI Pipelines for model training. They want to implement a conditional step that runs additional evaluation if the model accuracy exceeds 0.9, otherwise it runs a data augmentation component. Which two Kubeflow Pipelines SDK v2 constructs can they use to achieve this? (Choose two.)

Accepted Answer

dsl.Else. The team needs branching logic in a Vertex AI Pipelines (Kubeflow Pipelines SDK v2) graph, and dsl.If (option E) is the SDK v2 construct that creates a conditional branch whose condition is evaluated at runtime, so it correctly expresses 'if accuracy > 0.9, run the evaluation component.' dsl.Else (option B) is the companion construct used together with dsl.If to define the alternative branch, so it correctly expresses 'otherwise run the data augmentation component.' Together, dsl.If and dsl.Else form the if/else control-flow pattern required for this scenario. dsl.ParallelFor (option A) is for iterating over a list and launching parallel tasks, not for conditional branching. dsl.ExitHandler (option C) is for defining cleanup or exit tasks that run when a scope completes or fails, not for choosing between two branches. dsl.Collected (option D) is a type annotation used to collect outputs from loops, not a conditional construct.

Answer

dsl.ParallelFor

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dsl.ExitHandler

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dsl.Collected

Question 13

A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?

Accepted Answer

Python function component with @dsl.component. The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.

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Importer component

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Container component

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Vertex AI Training job

Question 14

An organization runs a Vertex AI pipeline that includes a model evaluation step. Team members want to reuse previously computed evaluation metrics when re-running the pipeline with unchanged code and hyperparameters. Which feature should they enable?

Accepted Answer

Enable pipeline caching (default behavior). Vertex AI Pipelines has caching enabled by default. When a pipeline is re-run with unchanged code, hyperparameters, and inputs, the evaluation step will reuse the cached output from the previous run, saving time and cost. Team members do not need to manually store outputs or disable caching.

Answer

Manually store outputs in Cloud Storage and check for existence

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Use the importer component to fetch previous results

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Disable caching for the evaluation component

Question 15

A company wants to automatically retrain their model every night at 2 AM using Vertex AI Pipelines. Which approach should they use to trigger the pipeline on a schedule?

Accepted Answer

Use Cloud Scheduler to call the Vertex AI pipeline creation API. Cloud Scheduler is the correct approach because it can directly invoke the Vertex AI Pipeline creation API via an HTTP trigger at a specified cron schedule (e.g., 2 AM daily). This integrates natively with Vertex AI's pipeline orchestration, allowing the scheduler to submit a pipeline run without additional infrastructure. The other options either lack native Vertex AI pipeline support or introduce unnecessary complexity.

Answer

Deploy the pipeline as a Cloud Run job with a cron trigger

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Use Vertex AI Experiments to schedule runs

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Configure a cron job inside the pipeline definition