PMLE Collaborating to manage data and models — 20 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?

Choose an answer to begin — your selection is scored in the full session.

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

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

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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

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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. 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 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. 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. 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

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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.

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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.

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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.. 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.

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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.. 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.

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Store all raw training data indefinitely for auditability.

Question 11

Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

Accepted Answer

The format of the data: structured vs. unstructured.. BigQuery is optimized for structured, tabular data (e.g., CSV, Avro, Parquet) and supports SQL queries, while Cloud Storage is a better fit for unstructured data (e.g., images, videos, raw text files) that does not require schema enforcement. Choosing the right storage depends on whether the training data has a fixed schema and requires relational querying or is blob-based and needs high-throughput access.

Answer

The requirement for data encryption at rest.

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The need for fine-grained access control at the row level.

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The maximum size of the dataset (BigQuery limit 1 TB).

Question 12

A financial services company uses Vertex AI to deploy multiple models for fraud detection. The ML team has set up a CI/CD pipeline using Cloud Build and Cloud Deploy. The pipeline builds a custom container with the trained model, pushes it to Artifact Registry, and deploys it to a Vertex AI Endpoint. Recently, a new regulation requires that all model deployments be audited and approved by the compliance team before going live. The compliance team wants to review the model's evaluation metrics and approve the deployment via a ticketing system. Currently, the CI/CD pipeline automatically deploys after the container is built. The team needs to implement a gating process without slowing down the development cycle. What should they do?

Accepted Answer

Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step.. Cloud Deploy provides a native approval gate feature that can be inserted into a delivery pipeline to require manual sign-off before a deployment proceeds. This allows the compliance team to review model evaluation metrics and approve via a ticketing system without modifying the CI/CD pipeline's build process, thus maintaining development velocity. The approval gate pauses the deployment at a specific stage, waiting for an external approval signal, which integrates seamlessly with Cloud Deploy's rollout management.

Answer

Use Cloud Composer to orchestrate the deployment and add a sensor that waits for approval from the ticketing system via a custom operator.

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Use Cloud Build's built-in approval gate feature to require compliance team sign-off before deployment.

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Store the model artifacts in Cloud Storage and have the compliance team deploy manually using the gcloud command.

Question 13

A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?

Accepted Answer

Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.. It uses Cloud DLP within a Dataflow pipeline to automatically de-identify PHI data as it is read from the original BigQuery table and written to a new, de-identified table. This satisfies the requirement for automated de-identification, while the original table remains encrypted at rest (BigQuery default) and in transit (TLS), and access to the original data can be logged via Cloud Audit Logs. The data science team only gets access to the de-identified table, ensuring PHI is not exposed during model development.

Answer

Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.

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Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.

Answer

Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.

Question 14

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Accepted Answer

Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test. Export the model, upload to GCS, register as a model, deploy to endpoint, then test.

Answer

Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test

Answer

Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test

Answer

Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test

Question 15

Match each regularization technique to its effect.

Accepted Answer

L1 Regularization: Encourages sparsity by setting some coefficients to zero. This question matches regularization techniques to their effects. L1 regularization (option A) encourages sparsity by setting some coefficients to zero, which is correct. Option E incorrectly states L1 shrinks coefficients uniformly; that is L2 regularization. L2 regularization (option B) shrinks coefficients to prevent overfitting, which is correct. Dropout (option C) randomly drops neurons during training to prevent co-adaptation, which is correct. Option F incorrectly states dropout adds penalty to the loss function; that describes regularization like L1/L2. Early stopping (option D) halts training when validation performance stops improving, which is correct.

Answer

L1 Regularization: Shrinks coefficients uniformly

Answer

Dropout: Adds penalty to the loss function

Question 16

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.

Answer

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 17

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.

Answer

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 18

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.. The 'roles/aiplatform.viewer' IAM role grants read-only access to Vertex AI resources, including models in the Model Registry. Option A is incorrect because 'roles/aiplatform.admin' grants full administrative access, which is too broad for read-only needs. Option B is wrong because exporting the model and sharing via a shared drive bypasses version control and security best practices. Option D is incorrect because Cloud Storage bucket ACLs control access to the underlying bucket, not to the Vertex AI Model Registry; the model is managed through Vertex AI IAM.

Answer

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

Answer

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

Answer

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

Question 19

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.. Configuring default values for missing features in the feature store allows the model to fall back on them, ensuring predictions can still be made during intermittent ingestion pipeline failures. Option B is incorrect because monitoring alerts notify of failures but do not prevent them. Option C is incorrect because ignoring missing features may lead to incorrect or failed predictions. Option D is incorrect because manually re-ingesting is not a sustainable solution and does not address the intermittent nature of the failure.

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 20

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.

Answer

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

Answer

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