PMLE Practice Test 3 — 25 Questions

Question 1

A data engineer wants to orchestrate a complex workflow that includes running a Vertex AI pipeline, then a BigQuery job, and finally a Dataflow pipeline. The workflow must handle dependencies, retries, and monitoring. Which Google Cloud service is most suitable for this orchestration?

Accepted Answer

Cloud Composer. Cloud Composer (based on Apache Airflow) is the most suitable service for orchestrating a complex workflow with dependencies, retries, and monitoring across Vertex AI, BigQuery, and Dataflow. It provides a managed Airflow environment that natively supports DAG-based orchestration, built-in retry logic, and integration with Google Cloud services via operators like VertexAIPipelineOperator, BigQueryOperator, and DataflowTemplatedJobStartOperator.

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Cloud Tasks

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Cloud Scheduler

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Workflows

Question 2

A team uses Cloud Build to automatically trigger a Vertex AI pipeline when changes are pushed to the model code repository. They have a cloudbuild.yaml file that builds a container image and submits the pipeline. However, they want to run the pipeline only if the commit includes changes to the 'training/' directory. Which Cloud Build configuration option should be used to filter the trigger?

Accepted Answer

Set the 'includedFiles' field to 'training/**' in the trigger configuration.. Option D is correct because Cloud Build triggers support an `includedFiles` field that specifies a glob pattern. When set to `training/**`, the trigger will only fire if the commit includes changes to files under the `training/` directory. This is the native, declarative way to filter triggers based on changed file paths without additional infrastructure.

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Add a 'ignoreFiles' field with 'training/**' to the trigger.

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Use a 'substitutions' field with a regex pattern to filter commits.

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Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.

Question 3

A machine learning team uses Vertex AI Pipelines to run a multi-step training pipeline. They want to implement a continuous delivery (CD) process where a model is automatically promoted from staging to production only if it passes an evaluation gate. Which TWO actions should they include in their CI/CD pipeline? (Choose two.)

Accepted Answer

Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production').. Option B is correct because Vertex AI Model Registry supports version aliases like 'staging' and 'production', which allow the CI/CD pipeline to automatically promote a model from staging to production only after it passes the evaluation gate. This enables a controlled, automated CD process without manual intervention.

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Use Cloud Functions to periodically check for new model versions and deploy.

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Manually approve each model version before deployment.

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Deploy every model version directly to production without evaluation.

Question 4

A data scientist wants to create a Vertex AI pipeline component that uses a custom container image stored in Artifact Registry. The component should accept a dataset artifact as input and output a model artifact. Which component type should they use?

Accepted Answer

A container component defined with the ContainerSpec class.. Option C is correct because a container component defined with the `ContainerSpec` class is the only Vertex AI component type that allows you to specify a custom container image from Artifact Registry. This component type directly wraps a Docker container, enabling you to define inputs (e.g., a dataset artifact) and outputs (e.g., a model artifact) via the `ContainerSpec` interface, which maps to the container's command-line arguments and environment variables. Lightweight Python components and Python function components cannot use a custom container image without a base image, and pre-built components from GCPC are fixed and do not support custom containers.

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A Lightweight Python component without base_image.

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A Python function component with a custom base_image.

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A pre-built component from GCPC.

Question 5

A company wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in Cloud Storage, and after training, the model should be automatically deployed to a staging endpoint if evaluation metrics pass a threshold. They also need to detect skew between training data and serving data. Which two services should they use for skew detection?

Accepted Answer

Vertex AI Model Monitoring and Vertex AI Pipelines. Vertex AI Model Monitoring provides built-in skew detection by comparing training data statistics with serving data statistics, alerting when distribution shifts exceed thresholds. Vertex AI Pipelines orchestrates the continuous training workflow, including triggering on new data arrival, model evaluation, and conditional deployment to a staging endpoint, making C the correct pair for both skew detection and pipeline automation.

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Cloud Monitoring and Cloud Logging

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Cloud DLP and Cloud KMS

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BigQuery and Dataflow

Question 6

A data scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)

Accepted Answer

Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.. Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are explicitly defined as input arguments to the pipeline function that is decorated with @dsl.pipeline. These parameters serve as the primary mechanism for passing configuration values (e.g., model name, learning rate, number of epochs) into the pipeline at creation time and can be consumed by downstream components.

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Pipeline parameters must be serialized to JSON before use.

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Pipeline parameters can only be of type str.

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Pipeline parameters can be used to pass large datasets between components.

Question 7

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

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Use the component's temporary directory to share data between containers.

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Pass the data as a serialized Python object in memory.

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Store the data in a BigQuery table and pass the table reference.

Question 8

You are defining a Python function component in KFP SDK v2. Which decorator should you use?

Accepted Answer

@dsl.component. In KFP SDK v2, the `@dsl.component` decorator is used to define a Python function as a lightweight, reusable pipeline component that can be executed independently. This decorator automatically generates a containerized component from the function's signature and type annotations, enabling type-safe inputs and outputs without requiring a separate component YAML specification.

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@dsl.task

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@component

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@dsl.pipeline

Question 9

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

Accepted Answer

Make the component idempotent by checking for existing output before processing.. Option A is correct because making the component idempotent by checking for existing output before processing prevents redundant work and avoids timeout failures when the file has already been downloaded. In Vertex AI pipelines, idempotent components can safely skip processing if the output already exists in Cloud Storage, reducing the risk of hitting timeout limits on subsequent pipeline runs.

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Reduce the size of the file being downloaded.

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Increase the machine type for the component.

Question 10

A data science team wants to build a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it if the accuracy exceeds 0.9. They want to use the Kubeflow Pipelines SDK v2. Which construct allows them to conditionally execute the deployment step based on the evaluation metric?

Accepted Answer

dsl.If. In Kubeflow Pipelines SDK v2, `dsl.If` is the correct construct for conditionally executing pipeline steps based on runtime metrics or parameters. It allows you to define a condition that, when evaluated to true, triggers the deployment step only if the model accuracy exceeds 0.9. This is the standard way to implement branching logic in v2 pipelines.

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dsl.Conditional

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dsl.ExitHandler

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dsl.Collected

Question 11

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

Accepted Answer

Use dsl.Condition (deprecated) or dsl.If to check metrics and conditionally run deployment.. Option A is correct because KFP provides `dsl.Condition` (deprecated) and `dsl.If` as first-class pipeline constructs to conditionally execute pipeline components based on runtime metrics or other pipeline outputs. By wrapping the deployment step inside a `dsl.If` block that checks whether evaluation metrics exceed a threshold, the pipeline can deploy the model to a staging endpoint only when the condition is met, avoiding unnecessary deployments for underperforming models.

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Use dsl.ParallelFor to evaluate and deploy in parallel.

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Use an exit handler to deploy regardless of metrics.

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Split the pipeline into two separate pipelines and run the second only if metrics are good.

Question 12

You need to orchestrate a complex ML workflow that involves multiple Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines. The workflow must handle dependencies, retries, and monitoring. Which two services are best suited for this orchestration?

Accepted Answer

Cloud Composer. Cloud Composer (based on Apache Airflow) is the correct choice because it provides a managed environment for orchestrating complex workflows with dependencies, retries, and monitoring across heterogeneous services like Vertex AI pipelines, BigQuery, and Dataflow. Airflow's DAGs allow you to define task dependencies, set retry policies, and integrate with Cloud Monitoring for observability, making it ideal for multi-service ML workflows.

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Cloud Scheduler

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BigQuery scheduled queries

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Cloud Functions

Question 13

A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?

Accepted Answer

Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.. AutoML Tables has built-in techniques to handle class imbalance, such as automatically adjusting class weights and using stratified sampling during training. This allows the model to learn from the minority class without requiring manual data preprocessing, making it the most effective and simplest approach for users with limited ML expertise.

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Downsample the majority class to balance the dataset.

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Use a custom loss function in the training configuration.

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Oversample the minority class using SQL before training.

Question 14

An organization wants to trigger a Vertex AI pipeline whenever new data arrives in a Cloud Storage bucket. Which approach should they use?

Accepted Answer

Use Cloud Functions triggered by Cloud Storage events to call the Vertex AI pipeline API.. Option D is correct because Cloud Functions can be directly triggered by Cloud Storage events (e.g., `google.storage.object.finalize`) and can then call the Vertex AI pipeline API using the Cloud SDK or client libraries. This provides a serverless, event-driven architecture that reacts immediately to new data without polling or additional infrastructure.

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Configure a Vertex AI pipeline trigger directly on the bucket using the GCP Console.

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Use Pub/Sub notifications from the bucket and a Dataflow job to start the pipeline.

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Set up a Cloud Scheduler job that runs every minute and checks for new files in the bucket.

Question 15

You are building a CI/CD pipeline for an ML model using Cloud Build. When code is pushed to the main branch, you want to automatically build a training image, run a Vertex AI pipeline, and if the model evaluation passes, deploy it to a staging endpoint. Which two components are essential for this CI/CD pipeline?

Accepted Answer

Vertex AI Pipelines to orchestrate training and evaluation.. Option C is correct because Vertex AI Pipelines is the service that orchestrates the entire ML workflow, including training, evaluation, and conditional deployment logic. In a CI/CD pipeline, you need a managed orchestrator to chain steps like model training, evaluation, and deployment, and Vertex AI Pipelines provides that with its Kubeflow Pipelines-based DAG execution.

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Cloud Scheduler to trigger the pipeline on a schedule.

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Cloud Functions to deploy the model.

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Vertex AI Continuous Training service.

Question 16

An ML engineer is using Cloud Composer (Airflow) to orchestrate a ML workflow. They need to run a Vertex AI pipeline as one of the tasks in the DAG. Which Airflow operator should they use?

Accepted Answer

VertexAIPipelineRunOperator or the Google Cloud Pipeline operator.. Option B is correct because Cloud Composer (Airflow) natively supports the `VertexAIPipelineRunOperator` (or its alias `GoogleCloudPipelineOperator`), which is specifically designed to trigger and monitor a Vertex AI pipeline run as a task within a DAG. This operator handles authentication, pipeline job submission, and status polling without requiring custom code, making it the idiomatic choice for orchestrating Vertex AI pipelines from Airflow.

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BigQueryOperator to run the pipeline as a query.

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PythonOperator with a custom script using the google-cloud-aiplatform library.

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VertexAIPipelineRunOperator (or Airflow's GCSToGCSOperator) for pipeline orchestration.

Question 17

Your organization wants to automate the retraining of a model when new data is available and also on a weekly schedule. Which TWO services would you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Functions. Cloud Scheduler (E) is used to trigger the retraining on a weekly schedule by sending a message to a Pub/Sub topic or making an HTTP request. Cloud Functions (A) is the serverless compute service that executes the retraining code in response to that trigger, and it can also be triggered directly when new data arrives (e.g., via Cloud Storage or Pub/Sub). Together, they provide both event-driven and scheduled automation without managing infrastructure.

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Cloud Composer

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Cloud Tasks

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Dataflow

Question 18

An ML engineer is building a pipeline on Vertex AI Pipelines and wants to pass a dataset artifact from one component to another without incurring additional cost for intermediate storage. How should they define the input and output types?

Accepted Answer

Use the Dataset artifact type available in the KFP SDK for inputs and outputs.. Option C is correct because using the KFP SDK's Dataset artifact type allows Vertex AI Pipelines to manage the data as a lineage-tracked artifact, automatically handling the underlying GCS storage reference without incurring additional intermediate storage costs. The artifact type enables the pipeline to pass the metadata (URI, type, etc.) between components efficiently, leveraging the native artifact management of the KFP SDK.

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Store the data in BigQuery and pass the table reference.

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Define output as a string and pass the GCS path manually.

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Use in-memory Python objects as function return values.

Question 19

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.. Option B is correct because 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.

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

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

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

Question 20

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

Accepted Answer

kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json'). Option A is correct because the Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.

Answer

gcloud ai pipelines compile command.

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kfp.Client().upload_pipeline()

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dsl.pipeline decorator automatically compiles at runtime.