PMLE Collaborating Within and Across Teams to Manage Data and Models Practice Test 4 — 15 Questions

Question 1

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?

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

Accepted Answer

Enable point-in-time correct retrieval when creating training datasets. Option A is correct because point-in-time correct retrieval ensures training datasets are built from the exact feature values that were available at the time of each event, preventing label leakage and keeping training data consistent with what would have been served historically. Option C is correct because using the same feature view for both training data export and online serving guarantees that the identical feature transformation and source are used in both paths, which is the core defense against training-serving skew. Option E is correct because feature monitoring detects drift in feature distributions between training and serving, surfacing skew or data quality issues so the team can remediate them. Option B is incorrect because deliberately using different feature views for training and serving introduces skew rather than preventing it. Option D is incorrect because exporting training data directly from the online store is not the recommended pattern; training datasets should come from the offline store with point-in-time correctness, while the online store serves low-latency predictions.

Answer

Use different feature views for training and serving to compare performance

Answer

Export training data from the online store directly

Question 2

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. Option C is correct because the Vertex AI SDK provides dedicated experiment logging functions (e.g., aiplatform.log_params() and aiplatform.log_metrics()) that record hyperparameters and metrics directly into a Vertex AI Experiment run, which is exactly what the team needs to capture and compare across runs. Option E is correct because integrating training code with Git and passing the commit hash as a run parameter ties each experiment run to a specific, reproducible code version, satisfying the requirement to capture code versions alongside hyperparameters and metrics. Option A is not appropriate because Cloud Logging captures log output, not structured experiment parameters or metrics for comparison in Vertex AI Experiments. Option B is unnecessary and less precise than using Git commit hashes, since manually linking Cloud Storage artifacts does not automatically associate code versions with runs. Option D is not required because Vertex AI Experiments already provides comparison capabilities natively, so exporting to BigQuery is an extra step not needed for the stated goal.

Answer

Use Cloud Logging to capture all training outputs

Answer

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

Answer

Export experiment data to BigQuery for comparison

Question 3

A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the service designed to track ML experiments, including parameters, metrics, and artifacts, and to compare runs. It integrates with Vertex AI Metadata to log experiment lineage and provides a UI for comparing runs. This directly matches the data scientist's requirement.

Answer

Vertex AI Metadata

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Question 4

A company trains a model using features from Vertex AI Feature Store. They notice training-serving skew because the feature values used at training time differ from those served online. How should they address this?

Accepted Answer

Use point-in-time correct retrieval from the offline store for training data. Training-serving skew occurs when the feature values used during training differ from those served at inference time. Point-in-time correct retrieval from the offline store ensures that, for each training example, the feature values used are exactly those that would have been available at that timestamp — matching what the online store would have served. This eliminates the temporal mismatch that causes skew.

Answer

Use the same online store for both training and serving

Answer

Disable caching in the online store

Answer

Enable feature monitoring to detect drift

Question 5

A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?

Accepted Answer

DVC. DVC (Data Version Control) is purpose-built to integrate with Git and version large datasets and ML models by storing metadata and pointers in Git while keeping the actual data in remote storage. It provides commands like dvc add, dvc push, and dvc pull that mirror Git workflows, making it the right choice for versioning datasets alongside code.

Answer

BigQuery table snapshots

Answer

Delta Lake

Answer

Git LFS

Question 6

A company uses Vertex AI Pipelines to train and deploy models. They want to automatically generate model documentation that includes model details, intended use, and evaluation results. What should they use?

Accepted Answer

Model Cards. Model Cards are a standardized format for model documentation, and Vertex AI supports automated generation of model cards.

Answer

Vertex AI Explanations

Answer

Vertex AI Metadata

Answer

Vertex AI Model Registry with custom metadata

Question 7

A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

Accepted Answer

Use a centralized feature store in a shared project and grant access to other projects via IAM. Vertex AI Feature Store is project-scoped, so to share features across projects, the recommended pattern is to create a centralized feature store in a shared (host) project and grant IAM roles to users/service accounts in other projects. This avoids duplication and ensures a single source of truth for feature definitions and online serving.

Answer

Export features to BigQuery datasets in each project

Answer

Use Vertex AI Feature Store's feature view for cross-project access

Answer

Create separate feature stores in each project and synchronize them with Dataflow

Question 8

A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?

Accepted Answer

Use the lineage query API with the execution ID. The Vertex AI Metadata lineage query API accepts an execution ID and returns all artifacts, contexts, and events connected to that execution, giving the full provenance graph in one call. This is the purpose-built method for tracing which artifacts a pipeline run produced.

Answer

List executions and then list artifacts separately

Answer

Create a context and query executions

Answer

Query artifacts by filter on execution ID

Question 9

An organization needs to implement MLOps with standardized pipeline templates across multiple teams. Which Vertex AI feature should they use to create reusable pipeline components?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the managed orchestration service that lets teams define, run, and monitor ML workflows as directed acyclic graphs (DAGs) using Kubeflow Pipelines or TFX. It supports reusable pipeline components and templates, enabling standardization across teams by packaging preprocessing, training, evaluation, and deployment steps into versioned, shareable artifacts.

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Answer

Vertex AI Workbench

Question 10

A company wants to monitor features in Vertex AI Feature Store for drift over time. Which two services should they use? (Choose two.)

Accepted Answer

Vertex AI Feature Store monitoring. Option A, Vertex AI Feature Store monitoring, is correct because it is the native capability that computes feature-level drift and skew statistics for features served from a Vertex AI Feature Store, generating metrics and alerts when distributions shift over time. Option E, Cloud Monitoring, is correct because the drift metrics produced by Feature Store monitoring are exported to Cloud Monitoring, where they can be visualized on dashboards and used to create alerting policies. Option B, Cloud Logging, is not the right choice because it captures log entries rather than computing or charting feature drift metrics. Option C, Vertex AI Model Monitoring, targets model prediction drift/skew on deployed endpoints, not feature drift within a Feature Store. Option D, Vertex AI Experiments, is for tracking training runs, parameters, and metrics, not for ongoing drift monitoring of stored features.

Answer

Cloud Logging

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Experiments

Question 11

A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)

Accepted Answer

Artifacts. Vertex AI Metadata is the core lineage service that stores and connects metadata about ML resources, so option C is correct because it provides the underlying metadata store for tracking lineage. Within that metadata store, Artifacts (option A) represent the inputs and outputs of pipeline steps—such as datasets, models, and metrics—and are the nodes whose lineage is tracked. Executions (option E) represent a single run of a pipeline step or component and record the events that consume and produce artifacts, which is exactly what links artifacts together into a lineage graph. Vertex AI Experiments (option B) is for tracking and comparing experiment runs and metrics, not for artifact/execution lineage, and Model Registry (option D) is for managing model versions and deployment, not for general lineage tracking.

Answer

Vertex AI Experiments

Answer

Model Registry

Question 12

A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry (B) is the centralized repository for managing the lifecycle of ML models, providing a single source of truth for governance across an organization, which directly matches the requirement for a centralized model registry. Model versioning and aliases (D) are core capabilities of the Model Registry that let teams track multiple model versions, assign meaningful aliases (e.g., 'production', 'staging'), and control which version is deployed, which is essential for governance and reproducibility. Together, B and D provide the registry plus the version/alias controls needed for centralized model governance. Vertex AI Feature Store (A) manages feature storage and serving, not model registration, so it does not fulfill the model registry requirement. Vertex AI Experiments (C) tracks experiment runs, parameters, and metrics for training, not model governance or registration. Vertex AI Metadata (E) stores metadata about artifacts and executions in a lineage graph, but it is not the centralized model registry itself.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Question 13

An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store is purpose-built to centrally define, store, and serve ML features for both training (batch) and online inference (low-latency) with a consistent feature definition, which directly eliminates training-serving skew. It provides a single source of truth so teams across projects can share features without manual coordination.

Answer

Cloud Data Catalog

Answer

Vertex AI Model Registry

Answer

Cloud Storage with versioning

Question 14

An ML engineer has a model trained in Vertex AI and wants to deploy it to an endpoint with autoscaling and traffic splitting for canary testing. They have the model artifact stored in Vertex AI Model Registry with alias 'champion'. What is the correct sequence of steps?

Accepted Answer

Upload model to registry, create endpoint, then deploy model to endpoint with traffic split.. The correct sequence is: upload the model to Vertex AI Model Registry (creating a model resource with the 'champion' alias), create an endpoint, then deploy the model to the endpoint with traffic split for canary testing. The model must exist in the registry before it can be deployed, and the endpoint must exist before deployment.

Answer

Create endpoint, upload model to registry, then deploy model to endpoint with traffic split.

Answer

Create endpoint, deploy model directly from Cloud Storage, then add traffic split.

Answer

Upload model to registry, then create endpoint and deploy in one command using gcloud ai endpoints deploy-model.

Question 15

A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata (part of Vertex ML Metadata) is the service designed to record and query ML metadata, including artifacts (datasets, models), executions (pipeline runs), and events, forming a lineage graph. It captures which datasets, parameters, and models were used in each pipeline execution, enabling reproducibility and auditability. This directly matches the requirement to track lineage of ML pipeline runs.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments