PMLE Collaborating to manage data and models Practice Test 2 — 15 Questions

Question 1

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

Accepted Answer

Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.. Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.

Answer

Store all models in a Cloud Storage bucket and manually control access via IAM permissions.

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Deploy models directly from training jobs to an endpoint without version tracking.

Answer

Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.

Question 2

A company uses a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?

Accepted Answer

Create a custom service account with required permissions and assign it to the Dataflow job.. The most efficient way to resolve insufficient permissions for Dataflow workers accessing Cloud Storage is to create a custom service account with the required roles (e.g., roles/storage.objectViewer) and assign it to the Dataflow job via the --serviceAccount option. This follows the principle of least privilege and ensures that only the Dataflow workers have the necessary permissions, without affecting other pipeline components or exposing the bucket publicly.

Answer

Grant the 'roles/storage.objectViewer' role to 'allUsers' on the Cloud Storage bucket.

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Use the Composer environment's service account for all pipeline components.

Answer

Move the Dataflow job to run after the pipeline so that data is already processed.

Question 3

A data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?

Accepted Answer

Grant the IAM role 'roles/aiplatform.viewer' to the team members on the project.. Option A is correct because Vertex AI Model Registry uses Cloud IAM, and granting the 'roles/aiplatform.viewer' role provides read access to all model versions. Option B is wrong because too broad. Option C is wrong because Cloud Storage IAM is separate and not sufficient for Vertex AI models. Option D is wrong because the bucket ACL does not apply to Vertex AI.

Answer

Grant the IAM role 'roles/aiplatform.admin' to the team members.

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Export the model as a local file and share it via a shared drive.

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Add the team members to the Cloud Storage bucket ACL with 'READER' access.

Question 4

Your team is using Vertex AI Feature Store for online predictions. You notice that feature values for some entities are missing in production, leading to failed predictions. Upon investigation, you find that the ingestion pipeline has been failing intermittently. What is the best immediate course of action to prevent prediction failures?

Accepted Answer

Configure default values for missing features in the feature store so that the model can fall back on them.. Option D is correct because using default values in the serving layer ensures predictions can still be made when features are missing. Option A is wrong because recreating features takes time and does not fix the ingestion issue. Option B is wrong because it does not address the missing values. Option C is wrong because monitoring alone does not prevent failures.

Answer

Set up monitoring alerts on the ingestion pipeline to get notified of failures.

Answer

Change the prediction request to ignore missing features.

Answer

Manually re-ingest all missing features by running the ingestion pipeline again.

Question 5

A team of ML engineers is building a real-time fraud detection system. They use Cloud Pub/Sub to stream transactions, Dataflow for feature engineering, and Vertex AI to get predictions. They want to ensure that the data used for training matches the data used for serving to avoid training-serving skew. Which approach should they take?

Accepted Answer

Use Vertex AI Feature Store to store features computed during training and retrieve them in the serving pipeline.. Vertex AI Feature Store ensures that the same feature engineering logic is applied consistently during both training and serving. By storing precomputed features in the Feature Store, the serving pipeline retrieves the exact same feature values that were used during training, eliminating the risk of training-serving skew. This approach is specifically designed for real-time systems where streaming data (via Pub/Sub and Dataflow) must be served with identical transformations.

Answer

Use a batch processing system for both training and serving to ensure identical feature calculations.

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Implement separate feature engineering pipelines for training and serving, but document them carefully.

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Ensure that both training and serving read from the same Cloud Storage location.

Question 6

You are using Cloud Datalab for collaborative data exploration with your team. However, some team members cannot access the Datalab instances. What is the most likely issue?

Accepted Answer

The team members have not been granted the necessary IAM roles (e.g., roles/datalab.user) on the project.. Cloud Datalab uses IAM permissions to control access to instances. The most common reason team members cannot access Datalab instances is that they lack the necessary IAM role, such as `roles/datalab.user`, which grants permission to view and connect to Datalab instances. Without this role, even if the instances exist and are running, users will receive permission-denied errors when trying to access them via the Datalab UI or API.

Answer

The Datalab instances have been deleted by another team member.

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The team members need to install the Cloud Datalab SDK locally.

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The Datalab instances were created using an incompatible notebook type.

Question 7

A company trains models using Vertex AI Training and wants to share the resulting model artifacts with a different team in another Google Cloud project. What is the most secure way to grant access?

Accepted Answer

Create a new service account in the other project, then grant it the 'roles/storage.objectViewer' role on the bucket.. Option D is correct because it follows the principle of least privilege and cross-project access best practices. By creating a dedicated service account in the target project and granting it the 'roles/storage.objectViewer' role on the specific Cloud Storage bucket, you avoid exposing the bucket publicly and avoid sharing the Vertex AI model resource directly, which would grant broader permissions than necessary. This approach ensures that only the service account can read the model artifacts, and the other team can use that service account to access the bucket securely.

Answer

Use BigQuery to copy the model artifacts and share the BigQuery dataset.

Answer

Share the Vertex AI model resource directly by adding the other project's members to the IAM policy on the model.

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Set the Cloud Storage bucket containing the artifacts to 'public' access.

Question 8

A company uses Cloud Composer to orchestrate an ML pipeline. They notice that the pipeline occasionally fails because the Composer environment runs out of disk space on the worker nodes. The pipeline uses many large dependencies. What is the most effective long-term solution?

Accepted Answer

Mount a Cloud Storage bucket to the Composer workers using GCSFuse to store large artifacts externally.. Mounting a Cloud Storage bucket via GCSFuse allows Composer workers to access large artifacts stored externally without consuming local disk space. This provides a scalable, durable, and cost-effective solution for handling large dependencies, as the pipeline can read/write directly to Cloud Storage, eliminating the disk space bottleneck on worker nodes.

Answer

Move the pipeline to Cloud Functions to avoid Composer's disk limitations.

Answer

Reduce the size of the Docker image used by the pipeline.

Answer

Increase the number of worker nodes in the Composer environment.

Question 9

Your team is using Vertex AI Pipelines to build an automated training pipeline. You need to share the pipeline definition with another team so they can run it in their own project. Which format should you use?

Accepted Answer

Export the pipeline as a YAML file using the Kubeflow Pipelines SDK.. Option D is correct because Vertex AI Pipelines is built on Kubeflow Pipelines, and the standard way to share a pipeline definition is to export it as a YAML file using the Kubeflow Pipelines SDK (`kfp.compiler.Compiler().compile()`). This YAML file contains the complete pipeline specification, including all components, dependencies, and execution order, and can be uploaded and run in any Vertex AI project without requiring the original Python code or build environment.

Answer

Copy the pipeline artifacts to a Cloud Storage bucket and share the bucket.

Answer

Package the pipeline as a Docker container image.

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Share the Python code that compiles the pipeline.

Question 10

Which TWO actions are recommended for collaborating on machine learning models using Vertex AI Model Registry?

Accepted Answer

Use version aliases such as 'champion' and 'challenger' to manage model lifecycle.. Option B is correct because Vertex AI Model Registry supports version aliases like 'champion' and 'challenger' to designate which model version should serve as the production candidate and which is under evaluation, enabling controlled lifecycle management and A/B testing without manual version tracking.

Answer

Use Cloud Storage object labels to store model descriptions.

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Deploy all model versions to a single endpoint for comparison.

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Create a separate model entry for each training run.

Question 11

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

Accepted Answer

Define and enforce feature schemas using the Feature Store API.. Option C is correct because defining and enforcing feature schemas using the Vertex AI Feature Store API ensures that all teams adhere to a consistent data structure (e.g., fixed feature names, data types, and value ranges). This prevents schema drift and ingestion conflicts, which are common when multiple teams independently push features to the same feature store. Without schema enforcement, one team might inadvertently change a feature's data type or add unexpected values, breaking downstream models.

Answer

Each team should create their own feature store to avoid conflicts.

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Use only batch ingestion to keep features synchronized.

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Allow each team to independently define feature engineering logic.

Question 12

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

Accepted Answer

Use a shared Cloud Storage bucket for intermediate artifacts with appropriate permissions.. Option B is correct because Cloud Composer workflows often require sharing intermediate data (e.g., transformed datasets, model checkpoints) across multiple DAGs or team members. A shared Cloud Storage bucket with fine-grained IAM permissions enables secure, centralized artifact exchange without duplicating data or exposing it to unauthorized users. This practice avoids hard-coded paths and ensures that all pipeline stages can reliably access the same artifacts, which is critical for reproducibility and collaboration in ML pipelines.

Answer

Keep all pipeline logic in a single large DAG for simplicity.

Answer

Embed service account keys directly in DAG code for authentication.

Question 13

Refer to the exhibit. A user receives the error shown when trying to upload a model to Vertex AI. What is the most likely cause?

Accepted Answer

The user does not have the 'roles/aiplatform.admin' or the 'aiplatform.models.upload' permission on the project.. The error message indicates a permission issue during model upload. The user lacks the 'aiplatform.models.upload' permission or the broader 'roles/aiplatform.admin' role on the project. Vertex AI requires these IAM permissions to authorize the upload action, regardless of other resource accessibility.

Answer

The container image 'gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-12:latest' is not accessible.

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The user specified an incorrect region (us-central1) that does not support Vertex AI.

Answer

The Cloud Storage bucket 'gs://my-model-artifacts/fraud-detection/v2/' does not exist.

Question 14

Refer to the exhibit. A user is trying to upload a Vertex AI pipeline definition. The error indicates an invalid dependency order. What should the user do to fix this?

Accepted Answer

Reorder the tasks in the YAML so that task1 is defined before task2.. Option A is correct because Vertex AI pipeline definitions require that tasks be declared in the order they appear in the dependency graph. The YAML parser validates the `dependentTasks` field by checking that referenced tasks are already defined. Defining `task1` before `task2` ensures that when `task2` declares a dependency on `task1`, `task1` is already in scope, resolving the invalid dependency order error.

Answer

Rename task1 to a name that comes alphabetically before task2.

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Change the dependency of task2 to be independent of task1.

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Remove the dependentTasks field from task2 and rely on implicit ordering.

Question 15

Refer to the exhibit. An ML engineer in the team needs to deploy the model to an endpoint. The engineer is assigned the 'roles/aiplatform.user' role at the project level but still cannot deploy. What is the most likely reason?

Accepted Answer

The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.. The 'roles/aiplatform.user' role at the project level grants permissions to use AI Platform resources, but it does not include the 'aiplatform.models.deploy' permission required to deploy a model to an endpoint. Model deployment is controlled by resource-level IAM policies, and if the model's resource-level policy only grants 'roles/aiplatform.user', the deploy action is denied. The correct role for deployment is 'roles/aiplatform.admin' or a custom role with the deploy permission.

Answer

The service account 'sa-training' is using all the model's quota.

Answer

Alice does not have any IAM role on the project.

Answer

Alice needs to be granted the 'roles/aiplatform.admin' role at the project level.