PMLE Collaborating Within and Across Teams to Manage Data and Models Practice Test 3 — 15 Questions

Question 1

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.. Vertex AI Pipelines lets teams define ML workflows as DAGs of containerized components, and organizing those components in a component registry (e.g., Vertex AI's component registry or Artifact Registry) enables reuse across teams. This approach enforces governance through versioning, access control, and standardized interfaces, so teams share vetted components rather than duplicating pipeline logic.

Answer

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

Answer

Use Cloud Composer to orchestrate ad-hoc scripts.

Answer

Have each team build their own pipelines independently.

Question 2

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 correctness requires retrieving feature values as they existed at the timestamp of each training example, which is exactly what the offline store's point-in-time join does when a feature_view is configured with an event/timestamp column. Vertex AI Feature Store uses this timestamp column to perform an as-of join, preventing future data from leaking into the training row. The online store and timestamp-less get_features calls return only the latest values, which is the classic source of label leakage.

Answer

Use the `get_features` API without specifying a timestamp.

Answer

Use BigQuery to manually join features with a sliding window.

Answer

Use the online store to retrieve the latest feature values.

Question 3

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 is a feature specifically designed to generate structured documentation for models, including purpose, training data, evaluation results, and intended use. It provides a standardized format for model transparency and governance. The other tools are for development, experimentation, or notebooks, not documentation.

Answer

Vertex AI Workbench

Answer

Vertex AI Experiment

Answer

Cloud Datalab

Question 4

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 5

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 (A) is correct because it provides the managed ML metadata store that automatically tracks artifacts, executions, and contexts, enabling model lineage tracking across teams. Vertex AI Model Registry (B) is correct because it is the central repository for managing model versions, including versioning, aliasing, and deployment tracking across teams. Vertex AI Experiments (D) is correct because it records and compares experiment runs, storing evaluation metrics and parameters so teams can track model performance over time. Vertex AI Workbench (C) is not correct because it is an interactive notebook development environment, not a governance or lineage tracking service. Vertex AI Feature Store (E) is not correct because it manages feature storage and serving for training and prediction, not model lineage, metrics, or version management.

Answer

Vertex AI Workbench

Answer

Vertex AI Feature Store

Question 6

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. Option A, Git integration in Vertex AI Workbench, is correct because user-managed notebooks include built-in Git support, allowing data scientists to clone repositories, commit, push, and pull notebook code directly from the JupyterLab interface for version control and collaboration. Option E, Cloud Source Repositories, is correct because it is a fully managed private Git repository service on Google Cloud that can host the team's notebook code and integrate with Workbench's Git tooling for sharing and versioning. Option B, Cloud Functions, is incorrect because it is a serverless compute service for event-driven functions, not a version control or code-sharing tool. Option C, Vertex AI Model Registry, is incorrect because it manages trained model versions and their metadata, not notebook source code. Option D, Vertex AI Experiments, is incorrect because it tracks experiment runs, parameters, and metrics, not Git-based notebook version control.

Answer

Cloud Functions

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 7

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 to store, share, and serve machine learning features with low-latency online serving and consistent offline serving for training. It lets a data science team centralize engineered features so multiple projects reuse them, while providing an online serving endpoint that returns feature values in milliseconds for real-time predictions.

Answer

Vertex AI Model Registry

Answer

Cloud Storage

Answer

BigQuery

Question 8

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 with timestamp matching ensures that for each training row, the feature values used are the ones that were actually available at the time of the label event, preventing future information from leaking into the training set. This is critical for time-varying features like user activity counts, where using the latest value would introduce look-ahead bias. By joining on entity ID and event timestamp, the feature store returns the feature value as of that timestamp, mimicking the production inference environment.

Answer

Retrieve the latest feature values for each entity

Answer

Aggregate features over all historical data

Answer

Use random sampling of feature values

Question 9

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. Vertex AI Feature Store supports native feature monitoring configured at the feature view level, where you specify the drift detection method (including Jensen-Shannon divergence) and a threshold. Setting the threshold to 0.1 with a daily schedule directly satisfies the requirement without building custom infrastructure. This is the first-party, managed approach for detecting feature drift.

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 10

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 aliases in Vertex AI Model Registry let you assign a mutable, named reference (e.g., 'champion', 'challenger') to a specific model version, so deployment endpoints can point to the alias rather than a fixed version ID. This enables seamless promotion or rollback by reassigning the alias, and supports A/B testing by directing traffic to different aliased versions. It is the intended feature for designating champion/challenger roles.

Answer

Model version labels

Answer

Model lineage

Answer

Model evaluation metrics

Question 11

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 is the service that records and stores ML metadata — artifacts, executions, and contexts — and their relationships, forming the lineage graph. It lets you query upstream and downstream dependencies of any artifact, such as finding all models and metrics derived from a dataset. This is exactly the lineage-query capability the team needs.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Model Registry

Question 12

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. The `dvc add` command registers a file or directory with DVC, creating a .dvc metafile that captures the hash and metadata of the data, and adds the actual data to the DVC cache. After preprocessing produces a new dataset in data/processed, running `dvc add data/processed` creates the versioned pointer that DVC tracks in Git, which is exactly what 'registering the changes' means.

Answer

dvc push

Answer

dvc run -n preprocess

Answer

dvc commit

Question 13

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 stores its data files in Apache Parquet format and layers a transaction log (the _delta_log directory) on top to provide ACID guarantees, schema enforcement, and time travel. Parquet's columnar layout is ideal for ML workloads because it enables efficient column pruning and predicate pushdown during feature engineering. The Delta transaction log, not the file format itself, is what delivers the ACID properties.

Answer

Apache ORC

Answer

CSV

Answer

Apache Avro

Question 14

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 lightweight, queryable copy of a table at a point in time using copy-on-write storage — only changed data consumes additional bytes, and the snapshot is a first-class table you can query directly. This satisfies the requirement for a reproducible, timestamped, queryable snapshot without duplicating the full dataset. Time travel, by contrast, is a query-time window (default 7 days) and does not persist as a durable object.

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 15

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 a centralized repository that tracks model versions, lineage, deployment history, and evaluation metrics across the organization, making it the correct choice for governance and compliance. It integrates with Vertex AI Pipelines and Model Monitoring so that every model artifact has an auditable lifecycle. This directly addresses the requirement for a single source of truth for ML models.

Answer

Cloud Storage

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments