PMLE Automating and orchestrating ML pipelines Practice Test 3 — 15 Questions

Question 1

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.

Answer

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 2

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.

Answer

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 3

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.

Answer

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 4

A company uses Vertex AI Pipelines with Kubeflow DSL for hyperparameter tuning. They notice that some trials fail due to OOM errors. How should they configure the pipeline to automatically handle this?

Accepted Answer

Add a retry policy to the hyperparameter tuning step with backoff. Option D is correct because Vertex AI Pipelines supports retry policies on individual pipeline steps, including hyperparameter tuning jobs. By adding a retry policy with exponential backoff, the pipeline can automatically re-run failed trials caused by transient OOM errors without manual intervention, while avoiding immediate retries that could overload resources.

Answer

Use a larger machine type for the whole pipeline

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Use Cloud Composer to catch failures and resubmit

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Reduce the number of trials

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Increase the memory for all trials in the pipeline definition

Question 5

An organization wants to implement continuous training for a model that serves predictions via Vertex AI Endpoints. Which approach best automates the retrain-deploy cycle?

Accepted Answer

Schedule a Vertex AI Pipeline to retrain and conditionally deploy. Option A is correct because Vertex AI Pipelines can be scheduled to run a retraining workflow and include a conditional step that deploys the new model to the endpoint only if it passes validation (e.g., evaluation metrics meet a threshold). This fully automates the retrain-deploy cycle without manual intervention, leveraging the pipeline's orchestration capabilities.

Answer

Use Vertex AI Model Registry to auto-deploy on new model upload

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Manually retrain and deploy monthly

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Use Cloud Composer to schedule retraining only

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Use a Cloud Function to retrain the model and update the endpoint

Question 6

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.

Answer

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 7

A company uses Vertex AI Pipelines with prebuilt components for data processing, training, and deployment. They need to integrate a custom validation step written in Python. What is the correct way to include this as a component?

Accepted Answer

Create a custom component using the Vertex AI Pipelines SDK @component decorator. Option C is correct because the Vertex AI Pipelines SDK provides a `@component` decorator that allows you to define a custom Python function as a pipeline component. This decorator automatically handles packaging the Python code into a container image, generating the component specification, and integrating it seamlessly with the pipeline orchestration engine. It is the idiomatic and recommended way to add custom validation logic without manually managing Docker or infrastructure.

Answer

Package the code in a Docker container and reference it as a custom job

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Define the step in the YAML pipeline definition using arbitrary Python commands

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Use a Cloud Function as a pipeline step

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Write a standalone Python script and call it using a Cloud Shell step

Question 8

Which TWO are benefits of using Vertex AI Pipelines for ML workflow orchestration over deploying custom Airflow DAGs in Cloud Composer? (Choose TWO.)

Accepted Answer

Automatic artifact lineage tracking. Option C is correct because Vertex AI Pipelines automatically captures and tracks artifact lineage (inputs, outputs, and their relationships) as part of the ML metadata store. This built-in lineage tracking is a key differentiator from custom Airflow DAGs, where you must manually implement artifact tracking using external tools or custom code.

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Managed infrastructure without manual configuration

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Built-in scheduling capabilities

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Support for arbitrary Python code in steps

Question 9

Which THREE are best practices for implementing CI/CD for ML pipelines on Google Cloud? (Choose THREE.)

Accepted Answer

Maintain separate environments for dev, staging, and production. Maintaining separate environments for dev, staging, and production is a core CI/CD best practice because it isolates changes, prevents accidental breakage in production, and allows thorough validation at each stage. On Google Cloud, this aligns with using distinct Vertex AI Pipelines instances or separate projects to enforce environment-specific configurations and access controls.

Answer

Design pipelines with low-code components to reduce development time

Answer

Write unit tests for every training job

Question 10

Which THREE should be considered when setting up an automated retraining pipeline using Vertex AI Pipelines and Cloud Composer? (Choose THREE.)

Accepted Answer

Setting performance thresholds for new models to decide deployment. Option A is correct because in an automated retraining pipeline, you must set performance thresholds (e.g., accuracy, precision, recall) for new models to decide whether to deploy them. Vertex AI Pipelines can evaluate model metrics against these thresholds and conditionally deploy only if the new model meets or exceeds the current production model's performance, preventing regressions.

Answer

Including hyperparameter tuning in every retraining run

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Frequency of code commits to the repository

Question 11

The exhibit shows part of a Vertex AI Pipeline definition. The pipeline fails at the training step with an error: 'Missing required input: train_data'. What is the most likely cause?

Accepted Answer

The data_processing step does not define any outputs. The error 'Missing required input: train_data' indicates that the training step expects an input artifact named 'train_data', but no upstream step provides it. In Vertex AI Pipelines, a component's output must be explicitly defined and connected to the downstream component's input. Since the data_processing step does not define any outputs, it cannot produce the 'train_data' artifact, causing the training step to fail.

Answer

The evaluation step expects a metric output but training does not produce it

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The training step uses the wrong image tag

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The container command for data_processing is incorrect

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The pipeline is missing a deployment step

Question 12

A large financial company uses a complex ML pipeline to detect fraudulent transactions. The pipeline consists of multiple steps: data ingestion from Pub/Sub, feature engineering using Dataflow, model training with Vertex AI, and deployment to an endpoint. They currently use Cloud Composer to orchestrate the pipeline with separate DAGs for each step. Recently, they have been experiencing failures in the Dataflow job due to schema changes in the incoming transactions, causing the pipeline to stall. The team manually fixes the schema and re-runs the pipeline, which is time-consuming. They want to improve the robustness of the pipeline. The pipeline is run on a schedule but also triggered by the arrival of new data. The team is considering moving to Vertex AI Pipelines to unify the workflow. They also want to automatically detect schema changes and handle them without manual intervention. Which approach should they take?

Accepted Answer

Migrate to Vertex AI Pipelines and add a pre-processing step that validates incoming data schema against a schema registry; if schema change is detected, the pipeline sends an alert and uses a default schema to continue processing. Option B is correct because it directly addresses the need for automated schema change detection and handling within a unified orchestration framework. By migrating to Vertex AI Pipelines, the team gains a managed, end-to-end ML workflow service that can include a pre-processing step to validate incoming data against a schema registry. When a schema change is detected, the pipeline can automatically apply a default schema and continue, eliminating manual intervention and reducing downtime.

Answer

Keep using Cloud Composer but add retries with exponential backoff to the Dataflow task, and set up a Cloud Monitoring alert to notify the team if the task fails repeatedly

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Use Cloud Scheduler to trigger the pipeline more frequently to reduce the impact of failures

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Create a separate Dataflow pipeline to handle schema detection and run it before the main pipeline; if schema changes, send an email to the team

Question 13

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.

Answer

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

Answer

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

Answer

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

Question 14

A company deploys a training pipeline on Vertex AI using custom containers. The pipeline includes a hyperparameter tuning job that uses Bayesian optimization. After several runs, they observe that the tuning job is not converging and the search space is large. They want to reduce the number of trials while still finding good hyperparameters. Which strategy should they use?

Accepted Answer

Reduce the search space by applying feature selection and using prior knowledge.. Option D is correct because reducing the search space using prior knowledge directly decreases the number of trials needed. Option A is wrong because increasing parallel trials does not reduce the total number of trials. Option B is wrong because grid search generally requires more trials than Bayesian optimization. Option C is wrong because early stopping reduces time per trial but does not reduce the number of trials.

Answer

Increase the number of parallel trials to explore more points simultaneously.

Answer

Use Grid search instead of Bayesian optimization to systematically cover the search space.

Answer

Implement early stopping by using the 'early_stopping' flag in the hyperparameter tuning job.

Question 15

A machine learning engineer is designing an ML pipeline on Vertex AI. The pipeline includes multiple steps: data validation, preprocessing, training, evaluation, and deployment. The engineer wants to ensure that if the data validation step fails due to schema mismatch, the pipeline stops immediately and does not proceed. Additionally, they want to reuse the preprocessed data from a previous successful run if the source data hasn't changed. Which two configurations should they use? (Choose two.)

Accepted Answer

Set the 'on_failure' parameter of the data validation component to 'Stop'.. Options A and C are correct. Option A: Enabling caching (cache=True) on the preprocessing step allows reuse of outputs when inputs are identical. Option C: Setting on_failure='Stop' on the data validation component stops the pipeline immediately on failure. Option B is wrong because custom exit handlers are not a standard feature. Option D is wrong because 'skip_if_successful' is not a standard parameter; caching is the correct way. Option E is wrong because conditional branches add unnecessary complexity; the on_failure parameter is simpler.

Answer

Use a custom exit handler in the data validation step to abort the pipeline.

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Use conditional branches to check the output of data validation before proceeding.

Answer

Enable 'skip_if_successful' on the preprocessing step.