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

Question 1

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

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Artifact Registry

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Cloud Storage

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Data Catalog

Question 2

A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?

Accepted Answer

Use IAM roles with custom permissions on the Vertex AI Model Registry. Vertex AI Model Registry supports IAM roles with custom permissions, allowing fine-grained access control over who can promote or deploy models to production. By assigning specific roles (e.g., `roles/aiplatform.modelDeployer`) to only authorized team members, you can restrict deployment actions while still permitting others to view or register models. This approach directly addresses the need to control production deployments without affecting other pipeline stages.

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Use Vertex AI Experiments to track models

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Store model artifacts in a bucket with bucket-level permissions

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Create separate projects for dev and prod

Question 3

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

Accepted Answer

Create shared Vertex AI Pipelines components. Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

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Use Cloud Composer to orchestrate preprocessing

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Write shared Python packages in Artifact Registry

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Use Cloud Dataflow templates

Question 4

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.

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Vertex AI Feature Store

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Vertex AI Experiments

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Vertex AI Model Registry

Question 5

An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?

Accepted Answer

Cloud Functions triggered by Cloud Storage events. Vertex AI Pipelines does not natively support event-driven triggers. The recommended pattern is to use Cloud Functions, which can be triggered by Cloud Storage events (e.g., object finalize/create) when new training data is uploaded. The Cloud Function then programmatically submits the pipeline run via the Vertex AI Pipelines client library or REST API, enabling an automated retraining workflow.

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Use Model Evaluation to decide

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Set up a trigger in Vertex AI Pipelines

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Cloud Scheduler on a daily basis

Question 6

A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?

Accepted Answer

Create a single Feature Store in a central project and grant fine-grained IAM roles. Creating a single Feature Store in a central project with fine-grained IAM roles is the best practice because it centralizes feature management while allowing cross-team access control at the feature group or feature level. Vertex AI Feature Store supports IAM roles like `aiplatform.featureStoreAdmin` and `aiplatform.featureStoreDataViewer` to grant granular permissions, enabling teams to share features without duplicating data or exposing sensitive information. This approach avoids data silos and ensures consistent governance across the organization.

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Export features to Cloud Storage

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Create separate Feature Stores per team project

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Use BigQuery authorized views

Question 7

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

Accepted Answer

Use Cloud Storage. Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.

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Copy the dataset to each worker's local disk

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Use NFS

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Use Google Drive

Question 8

A team is using Vertex AI Experiments to compare different hyperparameters. They want to automatically record the hyperparameters. What is the correct way?

Accepted Answer

Use the `aiplatform.start_run()` context manager. Vertex AI Experiments provides a native `aiplatform.start_run()` context manager that automatically captures hyperparameters passed as key-value arguments, logging them to the experiment run metadata without manual intervention. This integrates directly with the Vertex AI SDK, ensuring consistency and traceability across runs.

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Manually log to console

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Write to a CSV file

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Use BigQuery

Question 9

A data engineering team uses Dataflow for preprocessing and wants to integrate with Vertex AI Pipelines. They need to pass the preprocessed data location to the training step. What is the best practice?

Accepted Answer

Use PipelineParam to pass the output path. PipelineParam is the native mechanism in Vertex AI Pipelines (Kubeflow Pipelines SDK) to pass runtime outputs—such as a Cloud Storage path—between components. It creates a dependency graph that ensures the training step receives the exact output path from the preprocessing step, enabling dynamic, reproducible pipelines without hardcoding.

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Store the path in Data Catalog

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Use Cloud Pub/Sub

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Write the output to a fixed Cloud Storage path and hardcode it in the pipeline

Question 10

Which TWO practices help ensure reproducible ML experiments?

Accepted Answer

Use Vertex AI Experiments to track parameters and metrics. Vertex AI Experiments automatically logs parameters, metrics, and artifacts for each run, creating a complete lineage that enables exact reproduction of results. By tracking these details alongside the code version, you can recreate the exact environment and configuration that produced a given outcome, which is essential for reproducibility.

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Store all artifacts in a temporary bucket

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Use a random seed for each run

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Use preemptible VMs

Question 11

Which TWO tools can be used to collaborate on feature definitions across teams?

Accepted Answer

Vertex AI Feature Store. The correct options are B (Vertex AI Feature Store) and E (Data Catalog). Vertex AI Feature Store allows teams to share and reuse feature definitions across projects. Data Catalog provides metadata management and can catalog feature definitions, making them discoverable and understandable across teams. Cloud Storage (A) is a blob storage service, not a feature collaboration tool. Cloud Build (D) is for CI/CD pipelines. Cloud Logging (C) is for log management.

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Cloud Storage

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Cloud Logging

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Cloud Build

Question 12

Which THREE actions should be taken to manage model versions effectively?

Accepted Answer

Use Vertex AI Model Registry. Vertex AI Model Registry is a centralized repository that tracks, versions, and manages ML models. It enables you to organize models, assign aliases (like 'champion' or 'experiment'), and control deployment, ensuring reproducibility and governance across the model lifecycle.

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Delete old versions immediately

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Use the same model name for all versions

Question 13

Refer to the exhibit. A team member complains they cannot deploy a model to Vertex AI Endpoints. What is the most likely reason?

Accepted Answer

The policy lacks `roles/aiplatform.deployer`. Deploying a model to Vertex AI Endpoints requires the `roles/aiplatform.deployer` role on the service account. This role grants the necessary permissions to create and manage endpoint deployments. Without it, the deployment operation will fail with an access denied error, even if other roles are present.

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The policy is missing a condition

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The policy lacks `roles/aiplatform.specialist`

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The service account needs `roles/aiplatform.user`

Question 14

Refer to the exhibit. The team wants to automatically deploy the best-performing model version to production. They have set up a Cloud Function triggered by Model Registry events. Which alias should they use in the function to get the latest champion?

Accepted Answer

'champion'. The 'champion' alias is specifically reserved in Vertex AI Model Registry to denote the best-performing model version in production. By configuring the Cloud Function to trigger on the assignment of the 'champion' alias, the team ensures that only the model version promoted as the production champion is automatically deployed, aligning with MLOps best practices for staged model promotion.

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''

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'experiment'

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'latest'

Question 15

Refer to the exhibit. The team notices that the pipeline fails to read data from the specified Cloud Storage path. What is the most likely issue?

Accepted Answer

The service account lacks `storage.objectViewer` permission. The pipeline fails to read data from Cloud Storage because the service account lacks the `storage.objectViewer` IAM role, which grants the `storage.objects.get` and `storage.objects.list` permissions required to read objects. Without this role, the pipeline cannot authenticate or authorize the read operation, even if the bucket and path are correct.

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The bucket does not exist

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The pipeline runner is incorrect

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The region is mismatched