PMLE Automating and orchestrating ML pipelines Practice Test 2 — 15 Questions

Question 1

A company uses Cloud Composer to orchestrate their ML pipelines. They notice that tasks are being queued but not executed, causing delays. What is the most likely cause?

Accepted Answer

The Airflow worker resources are exhausted. When tasks are queued but not executed, it typically indicates that the Airflow workers have no available slots to pick up new tasks. In Cloud Composer, the Celery executor distributes tasks to workers; if all worker concurrency slots are saturated or the worker node pool is under-provisioned, tasks remain in the 'queued' state until a worker becomes free. This is the most likely cause given the symptom of tasks being queued without execution.

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The Airflow web server is down

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The DAG file is corrupted

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The Cloud Storage bucket containing DAGs is not accessible

Question 2

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 3

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 4

A data-processing pipeline using Dataflow needs to incorporate a custom ML prediction step. The team wants to maintain fast processing and minimize latency. What is the optimal approach?

Accepted Answer

Use a custom ParDo transform in Dataflow that calls Vertex AI Prediction API directly. Option B is correct because using a custom ParDo transform in Dataflow allows the pipeline to call the Vertex AI Prediction API synchronously within each worker, avoiding the overhead of external triggers, intermediate storage, or asynchronous messaging. This keeps the data in-memory and minimizes latency by processing predictions inline with the Dataflow streaming or batch pipeline.

Answer

Write the data to Cloud Storage, trigger a Cloud Function to call the model, and write results back

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Send data to a Pub/Sub topic and have a separate subscriber that runs predictions

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Stream data through Cloud Functions that serve predictions and write to BigQuery

Question 5

A company is using Vertex AI Pipelines with reusable components. They observe that a component that performs hyperparameter tuning is failing intermittently with a 'ResourceExhausted' error. The component is configured with a small custom service account. What is the most likely cause?

Accepted Answer

The service account does not have sufficient quotas or permissions to create the required number of trials or workers. The 'ResourceExhausted' error in Vertex AI Pipelines typically indicates that the component is trying to create more resources (e.g., trials or workers for hyperparameter tuning) than allowed by the assigned service account's quotas or permissions. A small custom service account often has restricted quotas for AI Platform services, such as the number of concurrent trials or training workers, leading to this failure.

Answer

The component code has a bug causing infinite recursion

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The KFP executor is not properly configured

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The pipeline system memory is insufficient for the component

Question 6

An organization has multiple ML pipelines running on Vertex AI. They want to centralize monitoring and alerting for pipeline failures, including root cause analysis. Which combination of services should they use?

Accepted Answer

Cloud Logging + Cloud Monitoring + Error Reporting. Option B is correct because Cloud Logging captures pipeline execution logs, Cloud Monitoring provides metrics and alerting on pipeline failures, and Error Reporting aggregates and analyzes errors with stack traces for root cause analysis. Together, they form a centralized observability stack that meets the requirement for monitoring, alerting, and root cause analysis of ML pipeline failures on Vertex AI.

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Cloud Trace + Cloud Debugger

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Cloud Operations for GKE + Stackdriver

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Cloud Audit Logs + Cloud Functions

Question 7

A team uses Cloud Composer to orchestrate a complex ML pipeline with many tasks. They notice that the DAG parsing time is very high, causing delays in task scheduling. Which action would most effectively reduce DAG parsing time?

Accepted Answer

Optimize DAG files to avoid heavy top-level imports and database queries. Option C is correct because heavy top-level imports and database queries in DAG files are executed every time the scheduler parses the DAG, which happens frequently (default every 30 seconds). By moving imports inside Python callables or using lazy loading, the parsing time is drastically reduced, allowing the scheduler to process DAGs faster and trigger tasks without delay.

Answer

Remove all DAG files that are not currently needed from the bucket

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Increase the parallelism of the Airflow scheduler

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Combine all DAGs into a single file

Question 8

Which TWO options are best practices for building ML pipelines on Vertex AI?

Accepted Answer

Use custom container components to encapsulate reusable logic. Option C is correct because custom container components allow you to encapsulate reusable logic with specific dependencies, libraries, and environments, enabling consistent execution across pipeline steps. This is a best practice for building modular, maintainable ML pipelines on Vertex AI, as it decouples step logic from the pipeline orchestration and supports versioning and testing.

Answer

Use Cloud Functions to execute individual pipeline steps

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Hardcode pipeline parameters in the component definitions

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Always use the same compute environment for training and serving to ensure consistency

Question 9

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.

Answer

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 10

Which TWO strategies can help reduce the cost of running ML pipelines on Vertex AI?

Accepted Answer

Use Vertex AI managed datasets to reduce storage costs. Options B and D are correct. Option B is correct because preemptible VMs are cheaper. Option D is correct because using managed datasets avoids duplicates. Option A is wrong because larger machines increase cost. Option C is wrong because manual scaling is not cost-effective. Option E is wrong because hyperparameter tuning can increase cost due to many trials.

Answer

Run hyperparameter tuning jobs with a large search space

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Manually scale up resources during peak times and scale down during off-peak

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Use a larger machine type for training to complete faster

Question 11

The exhibit shows a Cloud Build configuration. An ML engineer wants to automate the deployment of a model to Vertex AI after training. What is missing in this config to successfully deploy the model?

Accepted Answer

A step to build the serving container image. The Cloud Build configuration shown is for training a model, but to deploy it to Vertex AI, a serving container image must be built and pushed to Artifact Registry. Vertex AI requires a custom serving container (or a prebuilt one) to host the model for predictions. Without a step to build the serving container image (e.g., using a Dockerfile that includes the model and serving dependencies), the deployment will fail because there is no runnable image to deploy to the endpoint.

Answer

A step to upload the training image to Artifact Registry

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A step to run unit tests

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A step to create the Vertex AI Endpoint

Question 12

The exhibit shows a Cloud Composer environment variable configuration. An ML pipeline DAG fails with an authentication error when trying to access Vertex AI. What is the most likely cause?

Accepted Answer

The Airflow worker does not have the proper scopes to access Vertex AI. The authentication error when accessing Vertex AI from Cloud Composer most likely occurs because the Airflow worker's service account lacks the necessary OAuth scopes or IAM permissions. Cloud Composer uses a worker service account to execute tasks; if this account does not have the `https://www.googleapis.com/auth/cloud-platform` scope or the `aiplatform.user` role, the Airflow worker cannot authenticate to Vertex AI APIs, resulting in a 403 or 401 error.

Answer

The service account key in the environment variable is expired

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The DAG file is missing a required Python library

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The Cloud Composer environment is in a different project than Vertex AI

Question 13

The exhibit shows a Vertex AI PipelineJob submission command. The pipeline fails because the component cannot find the input data. What is the most likely cause?

Accepted Answer

The input data path is not accessible by the Vertex AI Pipelines service account. Option C is correct because the most likely cause of the pipeline failing to find input data is that the Vertex AI Pipelines service account lacks the necessary permissions to access the specified input data path. Vertex AI Pipelines uses the Compute Engine default service account (or a custom service account) to read data from Cloud Storage or other sources; if this account does not have the `storage.objectViewer` role (or equivalent) on the bucket or object, the component will fail with a permission-denied error, even if the path is syntactically correct.

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The pipeline root path is incorrect

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The pipeline name is misspelled

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The region does not support the component

Question 14

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 15

An ML team is using Vertex AI Pipelines to automate model training and deployment. They want to reuse components across multiple pipelines. What is the best practice for managing component code?

Accepted Answer

Store components as container images in Artifact Registry and reference them from pipelines. Option E is correct because Vertex AI Pipelines natively supports reusable components by packaging them as container images stored in Artifact Registry. This allows teams to version, share, and reference components across multiple pipelines without duplicating code, ensuring consistency and reducing maintenance overhead. Container images encapsulate the component's runtime environment and logic, making them portable and independently deployable.

Answer

Define components inline in the pipeline definition

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Embed component code in Cloud Composer DAGs

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Copy the component definitions into each pipeline's YAML file

Answer

Use Cloud Functions to define components