PMLE Collaborating Within and Across Teams to Manage Data and Models — Timed 25 Questions

Question 1

A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?

Accepted Answer

Use MLflow autologging by calling `mlflow.autolog()` before the training code and wrap the training script with `mlflow.start_run()`.. Vertex AI Experiments supports autologging via the MLflow library. By wrapping the training code with mlflow.start_run() and enabling autolog, all parameters, metrics, and artifacts are captured automatically.

Answer

Manually log each parameter and metric using `aiplatform.log_metrics()` after each training step.

Answer

Enable Vertex AI Experiments autologging by setting `autolog=True` in the experiment run context.

Answer

Use TensorBoard with tf.keras.callbacks.TensorBoard to log metrics.

Question 2

A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store centralizes feature storage, ensuring the same features are used for training and serving, reducing training-serving skew.

Answer

Vertex AI Workbench

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 3

An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata stores ML metadata and supports lineage queries to track the provenance of artifacts across pipeline executions.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Model Registry

Question 4

A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?

Accepted Answer

Bigtable online store. Bigtable online store is optimized for high write throughput and low-latency serving, suitable for frequently updated features. Optimized online store is better for read-heavy, static features.

Answer

Optimized online store

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Firestore online store

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Cloud SQL online store

Question 5

A machine learning team wants to implement champion/challenger model deployment. They have two model versions: v1 (champion) and v2 (challenger). They deploy both to the same endpoint with traffic splitting. How should they manage model versions in Vertex AI Model Registry to reflect this?

Accepted Answer

Upload v1 with alias 'champion' and v2 with alias 'challenger'. Then deploy both to the endpoint with traffic split.. Aliases in Model Registry allow labeling models as 'champion' and 'challenger' for easy identification and traffic routing.

Answer

Upload both models without aliases. Use endpoint traffic splitting by model version ID.

Answer

Use Vertex AI Experiments to designate champion/challenger.

Answer

Create two separate endpoints: one for champion and one for challenger.

Question 6

A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Accepted Answer

Delta Lake on Dataproc. Delta Lake on Dataproc provides ACID transactions on cloud storage data lakes, enabling concurrent reads/writes with consistency.

Answer

BigQuery table snapshots

Answer

DVC (Data Version Control)

Answer

Vertex AI Feature Store

Question 7

A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

Accepted Answer

User-managed notebooks. User-managed notebooks are persistent and retain customizations even when stopped, while managed notebooks are not persistent and lose customizations after stop.

Answer

Managed notebooks

Answer

Colab Enterprise notebooks

Answer

Vertex AI Pipelines

Question 8

A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?

Accepted Answer

Use Vertex AI Pipelines with pre-built and custom components organized in a component registry.. Standardized pipeline templates (components) in Vertex AI Pipelines allow reuse and governance across teams.

Answer

Store pipeline definitions in a shared Cloud Storage bucket and copy them manually.

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Use Cloud Composer to orchestrate ad-hoc scripts.

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Have each team build their own pipelines independently.

Question 9

A company uses Vertex AI Feature Store for feature engineering. They need to ensure point-in-time correctness to avoid data leakage during training. Which feature retrieval method should they use?

Accepted Answer

Use the offline store with point-in-time join using the `feature_view` with a timestamp column.. Point-in-time retrieval in Vertex AI Feature Store allows fetching feature values as they existed at a specific timestamp, preventing leakage.

Answer

Use the `get_features` API without specifying a timestamp.

Answer

Use BigQuery to manually join features with a sliding window.

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Use the online store to retrieve the latest feature values.

Question 10

A machine learning engineer needs to deploy a model to an endpoint for real-time predictions. The model is registered in Vertex AI Model Registry. Which command should they use to create an endpoint and deploy the model with the alias 'champion'?

Accepted Answer

gcloud ai endpoints deploy-model --endpoint=my-endpoint --model=my-model --alias=champion. The correct command uses `gcloud ai endpoints deploy-model` with the model's resource name and alias.

Answer

gcloud ai models upload --model=my-model --alias=champion --endpoint=my-endpoint

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gcloud ai endpoints predict --model=my-model --alias=champion

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gcloud ai endpoints create --model=my-model --alias=champion

Question 11

A data scientist wants to automatically generate model documentation that includes model purpose, training data, evaluation results, and intended use. Which tool should they use?

Accepted Answer

Model Cards in Vertex AI. Model Cards in Vertex AI provide a standardized framework for documenting models, automatically populated with metadata from Model Registry.

Answer

Vertex AI Workbench

Answer

Vertex AI Experiment

Answer

Cloud Datalab

Question 12

A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

Accepted Answer

Enable feature monitoring on the feature group with drift threshold and notification channel.. Feature monitoring in Vertex AI Feature Store allows defining drift thresholds and alerting via Cloud Monitoring.

Answer

Use Cloud Monitoring custom metrics and log-based alerts manually.

Answer

Use Vertex AI Experiments to compare distributions.

Answer

Export features to BigQuery and set up scheduled queries with alerts.

Question 13

A company wants to implement a central model governance strategy using Vertex AI. They need to track model lineage, store evaluation metrics, and manage model versions across teams. Which THREE Vertex AI services should they use? (Choose 3)

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata tracks lineage, Model Registry stores models and versions with evaluation metrics, and Experiments captures run parameters and metrics.

Answer

Vertex AI Workbench

Answer

Vertex AI Feature Store

Question 14

A team uses Vertex AI Feature Store with an online store for real-time predictions. They notice that the online store queries are taking longer than expected. Which TWO actions could improve online store performance? (Choose 2)

Accepted Answer

Reduce the number of features served by the online store.. For Bigtable online store, increasing nodes improves throughput; for optimized online store, decreasing number of features reduces load. Caching is not a built-in feature.

Answer

Use the offline store for serving predictions.

Answer

Switch from Bigtable to Firestore online store.

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Enable caching on the online store.

Question 15

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Accepted Answer

Git integration in Vertex AI Workbench. Git integration in Workbench allows version control, and sharing via Cloud Source Repositories or GitHub provides collaboration.

Answer

Cloud Functions

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 16

A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store is purpose-built for managing and sharing ML features, with an online store for low-latency serving. BigQuery is for analytics, Cloud Storage for objects, and Vertex AI Model Registry for models.

Answer

Vertex AI Model Registry

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Cloud Storage

Answer

BigQuery

Question 17

You are configuring a Vertex AI Feature Store online store for a real-time recommendation system that requires single-digit millisecond latency and high throughput. The feature values are updated frequently. Which online store type should you use?

Accepted Answer

Optimized online store. The optimized online store (backed by Cloud Bigtable) is designed for high throughput, low latency, and frequent updates, making it suitable for real-time systems. Bigtable online store is the legacy option with similar performance but higher cost and manual scaling.

Answer

Spanner online store

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Bigtable online store

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Firestore online store

Question 18

A team is training a model using historical data and wants to avoid data leakage when joining feature values from a feature store. The features include time-varying data like user activity counts. Which retrieval method should they use when creating a training dataset?

Accepted Answer

Use point-in-time correct retrieval with timestamp matching. Point-in-time correct retrieval joins features at the exact timestamp of each training row, ensuring no future data is used. This prevents data leakage. Other methods without timestamp handling introduce leakage.

Answer

Retrieve the latest feature values for each entity

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Aggregate features over all historical data

Answer

Use random sampling of feature values

Question 19

You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?

Accepted Answer

Configure feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence. Feature monitoring in Vertex AI Feature Store uses monitoring_config with drift detection via statistical tests like Jensen-Shannon divergence. The correct approach is to set the drift threshold in the feature view's monitoring configuration.

Answer

Use BigQuery scheduled queries to compare distributions and send alerts

Answer

Set up a Cloud Composer DAG to compute drift and publish to Cloud Monitoring

Answer

Enable model monitoring on the Vertex AI endpoint to detect drift

Question 20

A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Accepted Answer

Model aliases. Model Registry aliases allow tagging model versions with labels like 'champion' or 'challenger', enabling easy routing and comparison.

Answer

Model version labels

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Model lineage

Answer

Model evaluation metrics