PMLE Collaborating within and across teams to manage data and models Practice Test 3 — 15 Questions

Question 1

A data science team is using Vertex AI Feature Store for online serving. They notice that the online serving latency is high. What is the most likely cause?

Accepted Answer

The feature values are stored in Cloud Storage.. Option C is correct because Vertex AI Feature Store requires feature values to be stored in a low-latency online store (such as a Bigtable or Redis cluster) for serving. When features are stored in Cloud Storage, each online serving request must read from object storage, which introduces significant latency due to network overhead and lack of indexing. This design violates the fundamental architecture of Feature Store, which expects precomputed features in a key-value store optimized for sub-millisecond lookups.

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The features are being computed on the fly instead of being precomputed.

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The feature table has too many rows.

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The online store is not configured for high throughput.

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The serving endpoint is in a different region than the client.

Question 2

A company uses Vertex AI Pipelines for model training and deployment. The pipeline includes a model evaluation step that produces metrics. If the metrics are below a threshold, the pipeline should fail and not deploy. Which component should they use?

Accepted Answer

Use a conditional operator in the pipeline to skip or fail based on metrics.. Option A is correct because Vertex AI Pipelines supports conditional execution via the `Condition` component or `if/else` operators within the pipeline DAG. This allows you to evaluate model metrics (e.g., accuracy, AUC) and, if they fall below a defined threshold, either skip the deployment step or explicitly fail the pipeline using `PipelineTask.fail()` or a conditional branch that raises an error. This is the native, declarative way to control pipeline flow based on evaluation results without relying on external services or manual intervention.

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A Python component that uses the SDK to raise an exception if metrics are low.

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A Vertex AI Model Evaluation component configured with a threshold.

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Use Cloud Monitoring to trigger an alert and manually stop deployment.

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A custom container that returns a non-zero exit code on failure.

Question 3

A company has multiple business units using the same Vertex AI environment. They need to enforce that models deployed to production have passed a validation pipeline, and only the ML Engineering team can deploy to production. Which IAM configuration should they use?

Accepted Answer

Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions.. Option E is correct because it combines Vertex AI Model Registry (which enforces that only validated models are promoted to production) with Cloud Build for automated deployment, and uses IAM conditions to restrict deployment permissions exclusively to the ML Engineering team. This ensures that models must pass the validation pipeline before deployment, and only authorized personnel can trigger the deployment process.

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Use Vertex AI Workbench with user-managed notebooks.

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Use custom roles with permissions to deploy models, and use Cloud Audit Logs to monitor deployments.

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Use Binary Authorization to ensure models are signed.

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Use organization policies to restrict deployment to specific locations.

Question 4

A company uses Vertex AI Experiments to track ML training runs. They want to enforce that all training runs use only approved libraries from a central Artifact Registry to ensure compliance. Which approach should they take?

Accepted Answer

Create a custom Vertex AI training container that installs libraries from Artifact Registry at build time and restrict training job submission to that container using IAM.. Option C is correct because it enforces compliance at the image level: by building a custom container that installs only approved libraries from Artifact Registry at build time, and then restricting training job submission to that specific container using IAM, you ensure that no unauthorized libraries can be introduced at runtime. This approach eliminates the risk of developers injecting unapproved dependencies via startup scripts or runtime pulls, and it aligns with the principle of immutable infrastructure for ML training.

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Use a startup script in the training VM to install libraries from Artifact Registry.

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Use Vertex AI Pipelines with a component that pulls libraries from Artifact Registry at runtime.

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Configure Vertex AI Training with a custom job configuration that specifies the library sources.

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Use Cloud Build to build the training image with approved libraries and push to Container Registry, then restrict training jobs to that image.

Question 5

A team uses Vertex AI Experiments to track ML training runs. They want to automatically trigger a retraining pipeline when new labeled data arrives in BigQuery, and ensure the pipeline uses only approved libraries from a central artifact registry. Which combination of services should they use?

Accepted Answer

Vertex AI Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry.. Option B is correct because Vertex AI Pipelines provides a managed orchestration service for ML workflows, and a scheduled trigger can be set to run the pipeline when new labeled data arrives in BigQuery (e.g., via a Cloud Scheduler or Eventarc trigger). Cloud Build is used to pull approved libraries from Artifact Registry, ensuring only vetted dependencies are used during pipeline execution, which meets the security and compliance requirement.

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Cloud Composer to orchestrate, with Cloud Storage for libraries.

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Cloud Functions triggered by BigQuery, Cloud Build to run training, and Artifact Registry for libraries.

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Vertex AI Experiments with continuous evaluation, and a Cloud Run job for training.

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Dataflow to preprocess, then trigger a Cloud Run job.

Question 6

Which TWO of the following are best practices for versioning ML models and datasets?

Accepted Answer

Use Vertex AI Model Registry for model versioning and lineage tracking.. Vertex AI Model Registry is a managed service that automatically tracks model versions, artifacts, and lineage metadata (e.g., training runs, evaluation metrics, and source datasets). It provides a centralized hub for model governance, enabling reproducibility and auditability without manual versioning overhead. This makes it a best practice for versioning ML models in a production MLOps workflow.

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Store datasets and models in the same Cloud Storage bucket with version prefixes.

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Use Git LFS for dataset versioning.

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Use Cloud Data Catalog to tag dataset versions.

Question 7

Which TWO of the following are recommended methods to ensure data privacy when collaborating with external partners on ML projects?

Accepted Answer

Use Cloud DLP to de-identify data before sharing.. Cloud DLP (Data Loss Prevention) is a recommended method to de-identify sensitive data before sharing it with external partners. It can automatically detect and mask, tokenize, or redact PII, PCI, or other sensitive elements, ensuring that only anonymized data leaves your environment. This aligns with the principle of least privilege and data minimization for external collaboration.

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Use Vertex AI Feature Store with access controls.

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Grant the partner project's service account direct access to the raw data in BigQuery.

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Share data via email.

Question 8

Which THREE of the following are valid ways to share a Vertex AI model across two different Google Cloud projects?

Accepted Answer

Use Vertex AI Model Registry's cross-project sharing feature with IAM conditions.. Option A is correct because Vertex AI Model Registry supports cross-project sharing by allowing you to grant IAM roles with conditions on the model resource. This enables a model registered in one project to be accessed by a service account from another project without moving or copying the model artifacts.

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Publish the model to Google Cloud Marketplace.

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Use the gcloud ai models copy command to copy the model across projects.

Question 9

Refer to the exhibit. A team runs the command above and sees only two models. They know there is a model 'model-v3' created three days ago. What is the most likely reason it is not listed?

Accepted Answer

The model was created in a different region.. The `gcloud ai models list` command lists models within a specific region, as Vertex AI models are regional resources. If 'model-v3' was created in a different region, it would not appear in the output unless the `--region` flag is set to that region. This is the most likely reason the model is missing from the list.

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The model is in a different project.

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The model is not deployed to an endpoint.

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The model's display name contains a hyphen.

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The model was created by a different user.

Question 10

Refer to the exhibit. A team runs this Vertex AI Pipeline definition but the deploy component never executes, even though the evaluate step outputs a metric of 0.9. What is the most likely cause?

Accepted Answer

The condition should reference the evaluate component's output directly instead of using an input variable.. Option D is correct because in Vertex AI Pipelines, the `condition` block evaluates a boolean expression at pipeline compile time, not runtime. If the condition references an input variable rather than the actual output of the `evaluate` component, the pipeline will use the default or placeholder value (often `False`), causing the deploy step to be skipped even when the runtime metric is 0.9. The condition must directly reference the `evaluate` component's output (e.g., `evaluate.outputs['metric']`) to be evaluated correctly at runtime.

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The deploy component depends on the gate component, but the gate is not producing an output.

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The deploy container image does not exist.

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The evaluate component must be run before train, but the pipeline order is incorrect.

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The pipeline should use a custom component for the condition instead of the built-in type.

Question 11

A large e-commerce company uses Vertex AI to train a recommendation model daily. The training pipeline is built with Vertex AI Pipelines and involves three steps: data preprocessing, training, and model evaluation. The pipeline is triggered by a Cloud Scheduler job every morning at 8 AM. Recently, the pipeline has been failing intermittently during the data preprocessing step, with an error message indicating 'ResourceExhausted: Quota limits exceeded for read api requests.' The team has checked and confirmed that the quota for BigQuery read requests is not exceeded at the project level. The preprocessing step reads data from a BigQuery table with billions of rows. The team has also noticed that the pipeline runs on a custom machine type (n1-standard-4) with a persistent disk. What is the most likely cause of this error?

Accepted Answer

The preprocessing component is using a BigQuery client library that does not use exponential backoff for retries.. Option C is correct because the error 'ResourceExhausted: Quota limits exceeded for read api requests' indicates that the BigQuery API is throttling requests from the client, even though the project-level quota is not exceeded. The preprocessing component likely uses a BigQuery client library that lacks exponential backoff retry logic, causing rapid, repeated requests that exhaust the per-client or per-connection quota. Implementing exponential backoff would allow the client to back off and retry, preventing quota exhaustion.

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The BigQuery table is partitioned on a date column, and the pipeline is querying a specific partition that exceeds the quota.

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The Cloud Scheduler job is triggering multiple pipeline runs that overlap, causing concurrent quota usage.

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The pipeline is using a shared VPC that has traffic shaping limits.

Question 12

A data science team is collaborating on a project to build a churn prediction model. They use Vertex AI Workbench instances for development. Each data scientist has their own instance with a persistent disk. They share code via a GitHub repository. They want to ensure that the model training is reproducible across different team members' environments. Currently, they manually install Python packages in their instances, and they have noticed that the model metrics differ slightly between runs on different instances. Which of the following is the best action to ensure reproducibility?

Accepted Answer

Use Vertex AI Experiments with a fixed environment by specifying a prebuilt container.. Option C is correct because Vertex AI Experiments with a prebuilt container ensures a fixed, reproducible environment by pinning the exact OS, Python version, and all dependencies. This eliminates the variability introduced by manual package installations and differing instance configurations, directly addressing the team's issue of inconsistent model metrics across runs.

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Standardize the instance machine type and ensure all have the same number of CPUs.

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Use Cloud Functions to run the training code instead.

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Create a custom Docker image with all dependencies and use it in Vertex AI Training jobs.

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Ask all team members to use the same Python virtual environment and install packages from a requirements.txt file.

Question 13

A team of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?

Accepted Answer

Require code reviews for all changes to feature definitions before merging to the main branch.. Option B is correct because requiring code reviews for all changes to feature definitions before merging to the main branch enforces a peer-review gate, ensuring that modifications are validated for correctness, consistency, and compliance before they reach production. This aligns with MLOps best practices for governance and reduces the risk of introducing errors or breaking changes into the feature store.

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Allow data scientists to edit feature definitions directly in the Vertex AI Feature Store console.

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Define multiple feature views in Vertex AI Feature Store for different environments and manage access via IAM.

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Use scheduled batch jobs to synchronize feature definitions from a shared spreadsheet to Vertex AI Feature Store.

Question 14

A Vertex AI pipeline is triggered from Cloud Build using the configuration above. The pipeline fails with an error: 'Unable to submit build: The source code is not available.' What is the most likely cause?

Accepted Answer

The 'gcloud builds submit' command does not have access to the source code in the Cloud Build environment.. The error 'Unable to submit build: The source code is not available' indicates that the Cloud Build environment cannot locate the source code when the 'gcloud builds submit' command is executed. This typically happens when the pipeline is triggered from Cloud Build but the source code is not properly staged or accessible in the build context, often because the build configuration does not include the source directory or the source is not uploaded to Cloud Storage. Option B correctly identifies that the command lacks access to the source code in the Cloud Build environment.

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The Docker build step failed silently due to a missing dependency.

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The Docker image tag does not include a hash, causing the push to fail.

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The Cloud Build service account lacks permission to access the Vertex AI Pipeline API.

Question 15

Your team manages multiple ML models in Vertex AI Model Registry. Each model has several versions deployed to different endpoints for testing and production. You need to implement a process where a model version can be promoted from a staging environment to production only after it has passed automated validation tests and been approved by a designated reviewer. The team uses CI/CD pipelines (Cloud Build) for training and deployment. Currently, model versions are deployed to endpoints using Vertex AI Endpoints with a single traffic split configuration. You want to track promotion requests and enforce approval gates. What should you do?

Accepted Answer

Use Vertex AI Model Registry version aliases ('staging', 'production') and configure Cloud Build to trigger a Cloud Run service that handles approval logic, then update the alias upon approval.. Option D is correct because Vertex AI Model Registry version aliases (e.g., 'staging', 'production') are designed to track model version lifecycle stages. By integrating Cloud Build to trigger a Cloud Run service that enforces approval logic before updating the alias, you create a clear promotion gate. This approach natively supports tracking promotion requests and enforcing approval without custom databases or manual scripts, aligning with CI/CD best practices.

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Deploy each model version to a separate endpoint, and use a custom database to track which endpoint is 'production'. Then use migration scripts to switch traffic.

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Store the model version metadata in a BigQuery table and use a scheduled query to automatically update the endpoint deployment based on validation results.

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Use Vertex AI Model Registry labels to mark versions as 'staging' or 'production', and create a Cloud Function that checks the label before deploying to the endpoint.