PMLE Automating and Orchestrating ML Pipelines Practice Test 4 — 15 Questions

Question 1

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.)

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

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring (B) is correct because it is purpose-built to detect skew between training and serving data by continuously comparing feature distributions and alerting on statistically significant drift. Vertex AI Pipelines (D) is correct because it provides a serverless, scalable orchestration service for building continuous training pipelines that automatically retrain models when new data arrives, integrating with Cloud Build and other services.

Answer

Cloud Logging

Answer

Cloud Functions

Answer

Cloud Monitoring

Question 2

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 KFP SDK v2, the @dsl.component decorator is used to define a Python function component. @dsl.pipeline is for defining a pipeline that composes multiple components. The other options are not valid decorators.

Answer

@dsl.pipeline

Answer

@kfp.v2.components.func_to_component

Answer

@kfp.dsl.component

Question 3

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.

Answer

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

Answer

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

Answer

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

Question 4

A team notices that a Vertex AI Pipeline step re-executes every time the pipeline runs, even though its inputs and code have not changed. They want to enable caching for this component to avoid redundant computation. However, caching is currently disabled globally. Which configuration change will enable caching for that specific component?

Accepted Answer

Add 'caching=True' to the @dsl.component decorator for that component.. Caching can be enabled per component by setting the 'caching' parameter to True in the component decorator or by setting the environment variable. Setting it in the pipeline decorator affects all components. In KFP SDK v2, you can use @dsl.component(caching=True) or set the task-level caching. The correct answer is to set caching=True in the component definition.

Answer

Use the 'enable_caching' parameter when creating the pipeline job via the SDK.

Answer

Add 'caching=True' to the @dsl.pipeline decorator.

Answer

Set the environment variable 'CACHE_ENABLED=True' on the Vertex AI Pipeline job.

Question 5

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.

Answer

Use Cloud Tasks to queue pipeline runs daily.

Answer

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

Answer

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

Question 6

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.

Answer

Cloud Scheduler and Pub/Sub

Answer

Cloud Composer (Airflow) with Cloud Functions

Answer

Vertex AI Pipelines with Cloud Run

Question 7

An ML engineer is building a pipeline that includes a step to run a BigQuery query and pass the results to the next step. They want to use a pre-built Google Cloud Pipeline Component for BigQuery. Which component should they use to execute a query and output the results to a destination table?

Accepted Answer

BigQueryExecuteQuery. The correct component is BigQueryExecuteQuery because it is the pre-built Google Cloud Pipeline Component (from the `google-cloud-pipeline-components` package) specifically designed to run a BigQuery SQL query and write the results to a destination table. It returns a `DatasetArtifact` or `Table` artifact that can be passed to downstream pipeline steps, fulfilling the requirement of outputting results to a destination table.

Answer

BigQueryExportData

Answer

BigQueryQueryJobOp

Answer

BigqueryRunQuery

Question 8

A Vertex AI Pipeline contains a task that may produce outputs that are not always needed. The engineer wants to conditionally execute downstream tasks only if a specific artifact is produced. Which KFP SDK v2 construct allows the engineer to implement this conditional execution?

Accepted Answer

dsl.If. The dsl.If construct in KFP SDK v2 allows conditional execution of tasks based on the presence or value of an output from a previous task. dsl.Collected is for parallel loops, exit handlers are for cleanup, and dsl.Condition is not a valid construct.

Answer

dsl.ExitHandler

Answer

dsl.Collected

Answer

dsl.Condition

Question 9

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.

Answer

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

Answer

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

Answer

It provides a serverless execution environment for ML pipelines.

Question 10

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.

Answer

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

Answer

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

Answer

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

Question 11

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.

Answer

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.

Answer

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

Answer

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

Question 12

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.

Answer

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

Answer

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

Answer

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

Question 13

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?

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

Answer

Metrics

Answer

Model

Question 14

An organization is building a continuous training (CT) pipeline that retrains a model whenever one of the following conditions is met: (1) new training data is available in Cloud Storage, (2) it's the first day of the month, or (3) the model's performance degrades below a threshold. Which TWO mechanisms should they combine to trigger the pipeline?

Accepted Answer

Cloud Scheduler to trigger the pipeline on a cron schedule (e.g., first day of month). Cloud Scheduler can trigger the pipeline on a cron schedule, such as the first day of every month, directly satisfying condition (2). Option E is correct because Cloud Storage Pub/Sub notifications can be configured to fire a Cloud Function whenever new training data is uploaded, satisfying condition (1). Condition (3), performance degradation, is not directly covered by any single option in the list; it would require a separate mechanism like Vertex AI Model Monitoring with Cloud Monitoring alerts. Therefore, among the given options, A and E are the best combination for the conditions that have direct native support.

Answer

Monitoring alerts from Cloud Monitoring to trigger a Cloud Function that starts the pipeline

Answer

Cloud Build trigger on code push to repository

Answer

BigQuery scheduled query to trigger the pipeline after data transformation

Question 15

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.. Vertex AI Pipelines components run in isolated containers; passing large datasets in-memory would exceed memory limits and cause failures. Using GCS URIs allows components to read/write data directly from Cloud Storage, which is the recommended pattern for handling large artifacts in Kubeflow Pipelines (the underlying orchestrator). This approach also enables caching and parallel execution since components only depend on the URI, not on the state of previous containers.

Answer

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

Answer

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