PMLE Practice Test 35 — 15 Questions

Question 1

An ML engineer is designing a CI/CD pipeline for ML models using Cloud Build and Cloud Deploy. They want to automatically test model performance on a validation set before promoting to production. Which step should be included in the CI/CD pipeline?

Accepted Answer

Run a Vertex AI Pipeline for model evaluation and register the model only if metrics exceed thresholds. Option E is correct because it directly integrates model evaluation into the CI/CD pipeline using Vertex AI Pipelines, which allows automated validation of model performance against predefined thresholds before promotion. This ensures that only models meeting quality criteria are deployed, aligning with MLOps best practices for gated promotions.

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Run unit tests on the training code

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Use Cloud Composer to schedule evaluation

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Deploy to production immediately after training

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Train the model in the CI/CD pipeline

Question 2

An MLOps team wants to automate the retraining of a model each time new data arrives in a BigQuery table. What is the most efficient Google Cloud service to orchestrate this pipeline?

Accepted Answer

Vertex AI Pipelines with a schedule trigger. Vertex AI Pipelines is purpose-built for orchestrating ML workflows, including model retraining. It integrates natively with BigQuery for data ingestion and supports schedule triggers to automate retraining upon new data arrival, making it the most efficient and managed option for this ML-specific task.

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Cloud Composer with an Airflow DAG

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Dataflow pipeline with a periodic trigger

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Cloud Functions triggered by BigQuery events

Question 3

An ML engineer is using Vertex AI Pipelines and wants to reuse a trained model across multiple pipeline runs without retraining each time. Which artifact management strategy should be used?

Accepted Answer

Use Vertex AI ML Metadata to track and retrieve model artifacts. Vertex AI ML Metadata is the correct artifact management strategy because it is purpose-built for tracking and retrieving model artifacts across pipeline runs. It stores metadata about models, datasets, and other artifacts in a lineage graph, enabling you to query and reuse a specific model version without retraining. This integrates natively with Vertex AI Pipelines, allowing you to pass model artifacts between components and retrieve them by ID or custom properties.

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Store the model in BigQuery as a ML model

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Use Cloud Functions to cache the model

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Save the model to a Cloud Storage bucket and reference by path

Question 4

A team wants to implement CI/CD for their ML models using Cloud Build. They have a pipeline that trains a model and deploys it. What is the best practice for triggering the pipeline when a new commit is pushed to the source repository?

Accepted Answer

Configure a Cloud Build trigger on the source repository (e.g., Cloud Source Repositories, GitHub). Option D is correct because Cloud Build natively supports triggers that automatically start a pipeline when a new commit is pushed to a connected source repository (e.g., Cloud Source Repositories, GitHub, Bitbucket). This is the simplest, most event-driven approach, requiring no polling, custom services, or additional messaging infrastructure. It directly maps the git push event to a build invocation, ensuring near-instantaneous pipeline execution.

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Set up a Cloud Scheduler job to poll the repository periodically

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Deploy a custom web service on App Engine to call Cloud Build API

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Use Pub/Sub to notify Cloud Build of new commits

Question 5

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

Accepted Answer

Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob. Option A is correct because Cloud Build's cloudbuild.yaml can define a step that builds a custom training container and submits it as a Vertex AI PipelineJob. This directly automates the ML pipeline by using Cloud Build to trigger a Vertex AI pipeline, which is the recommended pattern for CI/CD of ML workflows.

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Use Cloud Functions to retrain the model each time a build completes

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Set up a Cloud Scheduler job to poll for new build artifacts

Question 6

A data scientist wants to automate the retraining of a model when new data arrives in Cloud Storage. Which Google Cloud service is most appropriate for orchestrating this workflow?

Accepted Answer

Cloud Composer. Cloud Composer (D) is the most appropriate service for orchestrating a retraining workflow because it is a fully managed workflow orchestration service built on Apache Airflow. It allows you to define a Directed Acyclic Graph (DAG) that triggers model retraining when new data arrives in Cloud Storage, handling dependencies, scheduling, and monitoring across multiple steps such as data validation, training, and deployment.

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Cloud Run

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Vertex AI Predictions

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Cloud Scheduler

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Cloud Functions

Question 7

A team is using Cloud Composer to orchestrate ML workflows. They have a DAG that triggers a Vertex AI Training job, then a prediction deployment. The deployment step occasionally fails due to quota limits. What is the best way to handle this?

Accepted Answer

Implement exponential backoff retry in the DAG task. Option E is correct because Cloud Composer (Apache Airflow) provides built-in retry mechanisms via task parameters like `retries` and `retry_delay`. Implementing exponential backoff in the DAG task is the best practice for handling transient quota errors, as it automatically retries the deployment step with increasing delays, reducing load on the quota system and increasing the chance of success without manual intervention. This approach aligns with Airflow's native error-handling capabilities and avoids unnecessary complexity or resource waste.

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Increase the quota manually

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Use Vertex AI Pipelines instead of Cloud Composer

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Create a custom sensor to wait for quota to be available

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Catch the exception in the DAG and send an alert

Question 8

A large e-commerce company uses Vertex AI Pipelines to orchestrate its recommendation model training. The pipeline has several parallel components: feature engineering, model training, and model evaluation. Recently, they noticed that the pipeline often fails due to resource exhaustion in the Vertex AI custom training job for the model training component. The training job consumes significant memory and occasionally exceeds the allocated memory limit, causing the pod to be OOMKilled. The team has already increased the memory to the maximum allowed for the chosen machine type. They need to prevent the pipeline from failing while still using the same machine type. Which approach should they take?

Accepted Answer

Split the training component into multiple smaller steps that process data in chunks to reduce peak memory usage.. Option A is correct because splitting the training component into smaller steps that process data in chunks directly addresses the root cause of OOMKilled failures—peak memory usage exceeding the allocated limit. By reducing the memory footprint per step, the pipeline can stay within the maximum memory of the existing machine type without requiring a larger instance. This approach aligns with best practices for Vertex AI custom training jobs, where resource limits are fixed per machine type and cannot be exceeded.

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Use a larger machine type with more memory to accommodate the peaks.

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Add a memory check step before training that estimates memory usage and skips training if it exceeds the limit.

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Implement a retry policy with exponential backoff for the training component, so it automatically retries on failure.

Question 9

A data science team uses Vertex AI Pipelines to build a training pipeline. They notice that when the pipeline fails due to a transient error in a component, the entire pipeline restarts from the beginning, taking a long time. What is the best practice to handle transient errors efficiently?

Accepted Answer

Configure Vertex AI Pipelines to automatically restart from the last successful state by enabling checkpointing.. Option B is correct because Vertex AI Pipelines supports checkpointing, which allows a pipeline to resume from the last successful state after a transient failure, avoiding a full restart. This is the most efficient approach for handling transient errors in a managed pipeline service, as it minimizes wasted compute time and resources.

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Use Vertex AI Experiment to track runs and manually restart failed components.

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Wrap the component code in a try-except block and retry indefinitely.

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Set the component's retry count to 3 in the pipeline definition.

Question 10

A company uses Vertex AI Pipelines to train and deploy models. The pipeline has a step that runs a custom container. The step fails intermittently with a timeout error. Which approach should be taken to robustly handle this?

Accepted Answer

Increase the timeout for the step in the pipeline definition. Option D is correct because Vertex AI Pipelines (built on Kubeflow Pipelines) allows you to define a `timeout` parameter for each pipeline step. Increasing this timeout directly addresses the intermittent timeout error by giving the custom container more time to complete its work, without changing the pipeline architecture or introducing external monitoring components. This is the most robust and minimal-change solution for a step that occasionally exceeds its current time limit.

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Switch to Kubeflow Pipelines

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Set up a Cloud Composer DAG to monitor and rerun the pipeline

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Reduce the size of the training data

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Use Cloud Functions to retry the step

Question 11

An ML engineer is using Cloud Build to trigger a Vertex AI Pipeline on every commit to a repository. The pipeline takes 2 hours. The engineer wants to only run the pipeline when changes are made to specific directories. How can this be achieved?

Accepted Answer

Configure Cloud Build trigger with included file globs. Cloud Build triggers support 'included file globs' and 'ignored file globs' to filter which file changes should invoke the trigger. By specifying glob patterns for the directories of interest, the trigger will only fire when commits modify files matching those patterns, avoiding unnecessary pipeline runs for unrelated changes.

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Use Cloud Composer to poll the repository periodically

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Use a Cloud Function to evaluate changes and invoke the pipeline

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Modify the pipeline to ignore unrelated changes

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Add a conditional step in the pipeline to abort if no relevant changes

Question 12

An ML team is scaling a prototype to production. The data pipeline currently reads from Cloud Storage and transforms data with a custom Python script. They need to handle higher throughput and add monitoring. Which approach should they take?

Accepted Answer

Migrate the pipeline to Apache Beam on Dataflow with Cloud Monitoring. Apache Beam on Dataflow provides a unified programming model for batch and streaming data processing, enabling automatic scaling to handle higher throughput. Cloud Monitoring integrates natively with Dataflow to track pipeline metrics, latency, and error rates, addressing the monitoring requirement. This approach is purpose-built for production-grade data pipelines, unlike ad-hoc solutions.

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Deploy the Python script on a large Compute Engine instance with a cron job

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Rewrite the pipeline to use Pub/Sub and Cloud Functions for processing

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Use Cloud Composer to orchestrate the Python script at scale

Question 13

A company has a prototype ML model that achieves 85% accuracy on historical data. In production, accuracy drops to 70% after two weeks due to data drift. They need an automated retraining pipeline with minimal manual oversight. Which solution is most cost-effective?

Accepted Answer

Set up Vertex AI Model Monitoring to detect drift, which triggers a Cloud Function that submits a Vertex AI Training job with new data. Option C is correct because it combines automated drift detection via Vertex AI Model Monitoring with a serverless retraining trigger (Cloud Function) that submits a Vertex AI Training job, minimizing manual oversight while only incurring costs when drift is detected. This avoids the expense of continuous retraining or always-on GPU instances, making it the most cost-effective solution for the described scenario.

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Use Cloud Functions to trigger a Dataflow job that trains the model using custom containers

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Deploy the model on a GPU-equipped Compute Engine VM and run retraining every time new data arrives

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Schedule a weekly Cloud Composer DAG that runs a new training job with all available data

Question 14

Which TWO services are commonly used together to implement an end-to-end ML pipeline that automatically retrains and deploys models on Vertex AI? (Choose two.)

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines (B) is the correct choice because it provides a serverless, scalable orchestration service specifically designed to build, run, and manage ML pipelines on Vertex AI. It enables you to define a directed acyclic graph (DAG) of steps—including data preprocessing, training, evaluation, and deployment—and can be triggered automatically to retrain and deploy models. Cloud Scheduler (E) is commonly used together with Vertex AI Pipelines to schedule pipeline runs at fixed intervals or in response to time-based triggers, forming a complete end-to-end automated retraining and deployment workflow.

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Cloud Dataflow

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Cloud Composer

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Cloud Source Repositories

Question 15

A data scientist has trained a scikit-learn model locally and wants to deploy it to Vertex AI for online predictions with low latency. The model is a small RandomForestClassifier (100 MB). What is the recommended way to deploy this model?

Accepted Answer

Upload the model to Vertex AI Model Registry using the pre-built scikit-learn serving container.. Option C is correct because Vertex AI provides a pre-built container for scikit-learn that is optimized for serving predictions with low latency. For a small RandomForestClassifier (100 MB), this container handles model loading, request routing, and scaling automatically, eliminating the need for custom infrastructure. This is the recommended approach for deploying scikit-learn models to Vertex AI for online predictions.

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Deploy the model on a Kubernetes cluster with Istio.

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Package the model as a Docker container with a custom prediction routine.

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Export the model as a TensorFlow SavedModel and use the pre-built TF serving container.