PMLE Practice Test 4 — 25 Questions

Question 1

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.

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BigQueryExportData

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BigQueryQueryJobOp

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BigqueryRunQuery

Question 2

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.

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

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

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It provides a serverless execution environment for ML pipelines.

Question 3

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.. Option D is correct because 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.

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

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

Question 4

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.

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Dataset

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Metrics

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Model

Question 5

A team uses Vertex AI Feature Store to serve features for real-time predictions. They notice that feature values are frequently updated from multiple source systems, leading to inconsistencies. They need to ensure that feature values are consistent across all serving endpoints. What should they do?

Accepted Answer

Use a streaming ingestion pipeline with exactly-once semantics. Option D is correct because streaming ingestion with exactly-once semantics ensures that each feature update is applied precisely once, preventing duplicates or missed updates that cause inconsistencies. This approach synchronizes feature values across all serving endpoints in near real-time, directly addressing the problem of frequent updates from multiple source systems.

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Use batch ingestion with weekly updates to reduce update frequency

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Increase the offline storage TTL to retain historical feature values

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Implement a manual approval process for feature updates

Question 6

An organization uses Cloud Composer to orchestrate ML workflows. A DAG that triggers Vertex AI training jobs fails because the training job exceeds the 7-day maximum runtime. What is the best way to handle long-running training jobs in Cloud Composer?

Accepted Answer

Use Vertex AI Pipeline to manage the training job asynchronously. Option B is correct because Vertex AI Pipelines natively supports asynchronous execution, allowing Cloud Composer to trigger a pipeline and monitor its status without blocking the Airflow worker for the entire duration of the training job. This decouples the DAG execution timeout from the training runtime, enabling workflows that exceed the 7-day Airflow task timeout limit.

Answer

Increase the DAG execution timeout to 14 days in the Airflow configuration

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Refactor the training job to run on Dataflow, which supports longer runtimes

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Set max_active_runs=1 in the DAG to prevent overlapping runs

Question 7

A company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?

Accepted Answer

Enable autoscaling for the deployment. Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.

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Increase the machine type of the node

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Decrease the min replicas to 0

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Enable automatic batching of requests

Question 8

A company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. What is the most likely cause?

Accepted Answer

Traffic is not split to the new model version. In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.

Answer

The custom container is not compatible with Vertex AI

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The model is cached and needs cache invalidation

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The new model version was not deployed to the same endpoint

Question 9

A company needs to serve a model with strict latency requirements (<100ms). They are using Vertex AI Prediction with CPU. During testing, latency is 150ms. What should they do?

Accepted Answer

Switch to a GPU machine type. The model's latency of 150ms exceeds the 100ms requirement. Switching to a GPU machine type (Option D) is correct because GPUs are optimized for parallel computation, significantly reducing inference latency for many ML models, especially deep learning models, compared to CPUs. Vertex AI Prediction supports GPU machine types, and this change directly addresses the latency bottleneck without altering the model or its serving configuration.

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Enable batching to improve throughput

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Use a smaller machine type with more replicas

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Export the model to TensorFlow Lite

Question 10

You are monitoring a classification model that predicts loan default. The model was trained on data from 2020-2022. In 2023, the economic conditions changed, and the model's accuracy dropped significantly. Which monitoring approach would best help you detect this issue early?

Accepted Answer

Monitor the prediction distribution for significant shift from training distribution. Option C is correct because monitoring the prediction distribution for a significant shift from the training distribution directly detects changes in the model's output behavior, which is the earliest indicator of concept drift or data drift caused by economic changes. Unlike accuracy monitoring, this approach does not require labeled data, enabling real-time detection of performance degradation before ground truth labels become available.

Answer

Monitor the accuracy of the model on the latest batch of labeled data

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Monitor feature distribution drift using KS test

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Monitor the freshness of the training data

Question 11

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.. Option A is correct because 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.

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

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

Question 12

A team is building a continuous training pipeline that retrains a model when new data arrives. They want to detect data drift between the training dataset and the serving data. Which approach should they integrate into the pipeline to compare the distributions of the two datasets?

Accepted Answer

Use Vertex AI Model Monitoring with skew detection enabled.. Vertex AI Model Monitoring with skew detection enabled is the correct approach because it is specifically designed to detect data drift between training and serving datasets in a continuous training pipeline. It automatically computes distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) for each feature and compares the training data distribution against the serving data distribution, triggering alerts when drift exceeds a configured threshold. This integrates natively with Vertex AI Pipelines, enabling automated retraining workflows.

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Export metrics to Cloud Monitoring and set up alerting on mean values.

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Use Cloud DLP to inspect the datasets and generate summary statistics.

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Compute histograms of features in BigQuery ML and compare them manually.

Question 13

An ML engineer is creating a Vertex AI Pipeline that includes a loop to train multiple models in parallel on different hyperparameter sets. Which TWO KFP SDK v2 constructs can be used to implement this parallel execution?

Accepted Answer

A for loop in the pipeline function that creates multiple tasks. Option C is correct because in KFP SDK v2, you can use a Python for loop inside the pipeline function to dynamically create multiple task instances, each with different hyperparameter sets. These tasks are then executed in parallel by the Vertex AI Pipelines orchestrator, as long as there are no data dependencies between them. This pattern leverages the SDK's ability to compile Python control flow into a directed acyclic graph (DAG) of pipeline steps.

Answer

dsl.If

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dsl.Parallel

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dsl.Pipeline

Question 14

A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?

Accepted Answer

@dsl.component. The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.

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@dsl.task

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@dsl.pipeline

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@dsl.container

Question 15

A data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?

Accepted Answer

Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.. Option D is correct because Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.

Answer

Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.

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Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.

Answer

Define each step as a separate Cloud Build step and chain them via build triggers.

Question 16

A team is building a CI/CD pipeline for an ML model. They want to automatically trigger a Vertex AI pipeline for retraining whenever new training data arrives in a Cloud Storage bucket, but only if a specific Pub/Sub notification is published by a data ingestion process. Which approach meets these requirements with minimal operational overhead?

Accepted Answer

Use Eventarc to route the Pub/Sub notification to a Cloud Function that calls the Vertex AI pipeline creation API.. Option C is correct because Eventarc can directly listen to a Pub/Sub topic and route matching messages to a Cloud Function, which then calls the Vertex AI pipeline creation API. This serverless approach triggers the pipeline only when the specific Pub/Sub notification is published, meeting the requirement with zero infrastructure to manage and no polling overhead.

Answer

Use Cloud Scheduler to run a job every hour that checks for new files in Cloud Storage and starts the pipeline if new files exist.

Answer

Configure a Cloud Build trigger that listens to the Pub/Sub topic and executes a build step that submits the pipeline run.

Answer

Create a Dataflow streaming pipeline that reads from Pub/Sub and triggers the Vertex AI pipeline via a custom sink.

Question 17

A data scientist is defining a Vertex AI pipeline and needs to include a step that imports a pre-existing model from Cloud Storage into the pipeline as an artifact. Which Kubeflow Pipelines SDK v2 component should they use?

Accepted Answer

dsl.importer. The `dsl.importer` component in Kubeflow Pipelines SDK v2 is specifically designed to import existing artifacts (such as models, datasets, or metrics) from external storage (e.g., Cloud Storage) into a pipeline as a pipeline artifact. It allows you to reference a pre-existing model without retraining or re-uploading, making it the correct choice for this use case.

Answer

dsl.Collected

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dsl.Importer

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dsl.Artifact

Question 18

A company uses Cloud Composer to orchestrate a nightly ML workflow that includes running a Vertex AI pipeline, querying BigQuery, and running a Dataflow job. The Airflow DAG must run only if the previous day's Dataflow job succeeded. Which Airflow concept should they use to implement this dependency?

Accepted Answer

Use the bitshift operators (>>) to set the execution order: Dataflow_task >> VertexAI_pipeline.. Option D is correct because Airflow's bitshift operators (>>) define task dependencies in a DAG. By setting `Dataflow_task >> VertexAI_pipeline`, the Vertex AI pipeline task will only execute after the Dataflow task has completed successfully. This directly enforces the required dependency without additional logic or branching.

Answer

Use a BranchPythonOperator to check the status of the Dataflow job before proceeding.

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Nest the tasks in a SubDAG with a schedule_interval that starts after the expected Dataflow completion time.

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Set a TriggerRule on the Vertex AI pipeline task to 'all_done' and reference the previous task.

Question 19

A machine learning engineer needs to create a pipeline that runs a custom container component on Vertex AI. The container expects a Cloud Storage path as input and outputs a model artifact. Which component type should they define using the Kubeflow Pipelines SDK v2?

Accepted Answer

Container component using @dsl.container_component. Option D is correct because the Kubeflow Pipelines SDK v2 provides the @dsl.container_component decorator specifically for defining components that wrap custom container images. This allows the engineer to specify the container image, input/output paths (like a Cloud Storage path), and artifact metadata, enabling Vertex AI to execute the container as a pipeline step and capture the model artifact.

Answer

Google Cloud Pipeline Components (GCPC) for custom containers

Answer

Python function component using @dsl.component

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Importer component to load the container as an artifact

Question 20

A machine learning pipeline includes a conditional branch: if model accuracy exceeds 0.95, deploy to production; otherwise, send a notification. Which KFP SDK feature allows implementing this logic within the pipeline definition?

Accepted Answer

dsl.If. C is correct because KFP SDK's `dsl.If` is the native construct for implementing conditional branching within a pipeline definition, allowing you to conditionally execute a task (like deploying to production) based on the output of a previous component (e.g., model accuracy > 0.95). It works by creating a `Condition` object that wraps the downstream tasks, ensuring the pipeline DAG is correctly compiled and executed only when the condition evaluates to true.

Answer

dsl.ParallelFor

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Using a Python if statement inside the component

Answer

dsl.Condition