PMLE Collaborating to manage data and models — 10 Questions

Question 1

A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning. Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.

Answer

Dataflow for data access control and Vertex AI Experiments for model tracking

Answer

Cloud Storage with bucket-level IAM and Cloud Build for versioning

Answer

Cloud Composer for data access control and Cloud Source Repositories for model versioning

Question 2

An ML team uses Vertex AI Pipelines to automate model retraining. The pipeline includes a step that queries BigQuery to create a training dataset. The team notices that the pipeline fails intermittently with a '403 Exceeded rate limits' error. What is the most likely cause and solution?

Accepted Answer

The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity. The 403 'Exceeded rate limits' error in BigQuery indicates that the project is hitting the concurrent query rate limit or the rate of bytes read per second. Using a BigQuery reservation guarantees dedicated slot capacity, which prevents rate-limit errors by ensuring the pipeline has consistent compute resources regardless of other workloads in the project. This is the most direct solution because rate limits are enforced at the project level based on available slots, and a reservation provides a fixed number of slots that bypass those limits.

Answer

The training dataset is too large; partition the table and query only the latest partition

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The pipeline step timeout is too short; increase the timeout to 30 minutes

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The SQL query is inefficient; rewrite it using materialized views

Question 3

A company stores training data in Cloud Storage and uses Vertex AI Training for model training. They want to implement a data validation pipeline to detect data drift before retraining. Which service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is designed specifically to detect data drift and feature skew in production ML models by continuously comparing prediction requests against a baseline training dataset. It provides automated alerts when statistical distributions shift beyond a defined threshold, making it the correct choice for a data validation pipeline before retraining.

Answer

BigQuery ML

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Cloud Data Loss Prevention

Answer

Dataflow

Question 4

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.

Answer

Use batch ingestion with weekly updates to reduce update frequency

Answer

Increase the offline storage TTL to retain historical feature values

Answer

Implement a manual approval process for feature updates

Question 5

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

Answer

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

Answer

Set max_active_runs=1 in the DAG to prevent overlapping runs

Question 6

A team wants to share a trained model with other teams within the organization. They need to provide access to the model artifact in Vertex AI Model Registry and ensure that only authorized teams can deploy the model. What should they do?

Accepted Answer

Use IAM to grant the 'aiplatform.models.deploy' role to the other teams on the model resource. Option D is correct because Vertex AI Model Registry uses IAM to control access to model resources. By granting the 'aiplatform.models.deploy' role on the specific model resource, you ensure that only authorized teams can deploy the model, while other operations (like viewing or updating) remain restricted. This follows the principle of least privilege and avoids exposing the model artifact broadly.

Answer

Grant the other teams access to the Cloud Storage bucket where the model is stored

Answer

Set the model to public in Vertex AI Model Registry

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Use Cloud Key Management Service to encrypt the model and share the decryption key

Question 7

A data scientist is using Vertex AI Workbench user-managed notebooks. They need to collaborate with a colleague on the same notebook. The colleague should be able to edit the notebook simultaneously. What should they do?

Accepted Answer

Share the notebook instance URL with the colleague; both can edit simultaneously. Vertex AI Workbench user-managed notebooks support real-time collaboration by sharing the notebook instance URL. When you share the URL with a colleague, both users can edit the notebook simultaneously because the underlying JupyterLab environment is multi-user and supports concurrent editing sessions. This is the intended method for synchronous collaboration on the same notebook instance.

Answer

Store the notebook in Cloud Source Repositories and have the colleague clone it

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Share the underlying Compute Engine VM's SSH access with the colleague

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Export the notebook to Colab and share the link

Question 8

A team uses Vertex AI Pipelines with CustomJob components that pull training code from a Cloud Source Repository. The pipeline fails with a 'Permission denied' error when trying to access the repository. The service account used by the pipeline has the 'Source Repository Viewer' role. What is the likely issue?

Accepted Answer

The 'Source Repository Viewer' role is insufficient; the service account needs 'Source Repository Reader' or higher. The 'Source Repository Viewer' role only allows listing and viewing repository metadata, not reading the actual source code. To clone or pull code from a Cloud Source Repository, the service account needs the 'Source Repository Reader' role (or higher), which grants the `source.repos.get` and `source.repos.read` permissions required for Git operations. The pipeline's CustomJob component fails because the service account lacks these permissions when attempting to access the repository.

Answer

The training code contains a dependency that is not available in the custom container

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The pipeline is running in a different project than the repository; cross-project access is not supported

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The repository URL is incorrectly formatted; use the SSH URL instead of HTTPS

Question 9

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store is designed as a centralized repository that organizes, stores, and serves feature data consistently across different models and pipelines. This centralization ensures feature reuse, consistency, and governance, preventing data silos and duplication across the ML lifecycle.

Answer

Feature Store automatically applies feature engineering transformations.

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Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 10

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.