PMLE Collaborating Within and Across Teams to Manage Data and Models Practice Test 2 — 15 Questions

Question 1

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?

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

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

Answer

Cloud Storage

Answer

BigQuery

Question 2

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 3

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

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Use random sampling of feature values

Question 4

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

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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 5

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

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Model evaluation metrics

Question 6

A data scientist is using Vertex AI Experiments to track training runs. They want to automatically log all hyperparameters, metrics, and model artifacts without modifying their training code. Which approach should they use?

Accepted Answer

Enable autologging with MLflow and use Vertex AI Experiments as the tracking server. Vertex AI Experiments supports autologging with MLflow integration. By using the MLflow tracking API or Vertex AI's autologging, parameters and metrics are captured without code changes.

Answer

Use Cloud Logging to capture training logs and parse them into experiments

Answer

Manually log parameters and metrics using the Vertex AI SDK in the training script

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Run training in Vertex AI Workbench and manually save metrics to a BigQuery table

Question 7

A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata tracks artifacts, executions, and their lineage relationships. It supports lineage queries to find upstream and downstream dependencies.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Model Registry

Question 8

A team of data scientists is collaborating on notebooks in Vertex AI Workbench. They need to use Git for version control and share notebooks with real-time editing. Which type of Workbench instance should they choose?

Accepted Answer

Managed notebooks. Managed notebooks in Vertex AI Workbench support real-time collaborative editing (similar to Google Docs) and can integrate with Git for version control via Cloud Source Repositories. User-managed notebooks are single-user and lack real-time collaboration, so they do not meet both requirements.

Answer

User-managed notebooks

Answer

Vertex AI Pipelines

Answer

Dataproc Jupyter notebooks

Question 9

You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

Accepted Answer

dvc add data/processed. DVC tracks data versions via 'dvc add' which creates a .dvc file that points to the data in Cloud Storage. 'dvc run' is for pipelines, 'dvc push' uploads cached data to remote storage, and 'dvc commit' saves changes to DVC-tracked files after a pipeline run.

Answer

dvc push

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dvc run -n preprocess

Answer

dvc commit

Question 10

An organisation uses Delta Lake on Dataproc to manage a data lake for ML training. They need ACID transactions for concurrent reads and writes. Which file format does Delta Lake use as the underlying storage?

Accepted Answer

Apache Parquet. Delta Lake uses Parquet as the base file format, adding a transaction log for ACID properties. Avro and ORC are not used by Delta Lake, and CSV does not support ACID.

Answer

Apache ORC

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CSV

Answer

Apache Avro

Question 11

You need to create a reproducible snapshot of a BigQuery table as of a specific timestamp for ML model training. The snapshot should be queryable without copying the entire dataset. Which BigQuery feature should you use?

Accepted Answer

BigQuery table snapshots. BigQuery table snapshots create a read-only copy of a table at a specific point in time, queryable without additional storage costs for the data. Time travel queries access historical data but are limited to 7 days, and copying to a new table duplicates storage.

Answer

BigQuery export to Cloud Storage as Parquet

Answer

BigQuery time travel (FOR SYSTEM_TIME AS OF)

Answer

CREATE TABLE AS SELECT with WHERE clause

Question 12

A team wants to enforce governance and compliance for all ML models across the organisation. They need a centralised repository that tracks model versions, deployment history, and evaluation metrics. Which service should they use?

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry is the centralised repository for model versioning, lineage, metrics, and deployment management, providing governance and compliance.

Answer

Cloud Storage

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Question 13

A company uses Vertex AI Pipelines for ML workflows. They want to standardize pipeline templates across teams to ensure consistency. Which TWO approaches should they use?

Accepted Answer

Create reusable pipeline components using the Vertex AI SDK and store them in a shared repository. Using the Vertex AI SDK to create reusable pipeline components and storing them in a shared repository ensures consistency. Publishing components to the Google Cloud Marketplace is not a standard approach.

Answer

Use only pre-built components from Google's public repository

Answer

Share notebooks with pipeline code via Google Drive

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Publish pipeline components as custom containers in Google Cloud Marketplace

Question 14

A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

Accepted Answer

Enable point-in-time correct retrieval when creating training datasets. Using the same feature view for training and serving ensures consistency. Point-in-time correct retrieval prevents leakage. Feature monitoring detects drift that could indicate skew.

Answer

Use different feature views for training and serving to compare performance

Answer

Export training data from the online store directly

Question 15

A data science team uses Vertex AI Experiments to compare multiple model training runs. They want to capture and compare hyperparameters, metrics, and code versions for each run. Which TWO steps should they take?

Accepted Answer

Log hyperparameters and metrics using the Vertex AI SDK's experiment logging functions. Using the Vertex AI SDK to log parameters and metrics enables comparison. Integrating with a version control system like Git ensures code version tracking.

Answer

Use Cloud Logging to capture all training outputs

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Store code versions in Cloud Storage and link them to experiments manually

Answer

Export experiment data to BigQuery for comparison