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

Question 1

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 designed for tracking and comparing ML experiment runs, capturing parameters, metrics, and artifacts. It integrates with MLflow for autologging.

Answer

Vertex AI Metadata

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Question 2

A team uses Vertex AI Feature Store with an online store. They need low-latency serving for millions of features with high write throughput. Which online store type should they choose?

Accepted Answer

Bigtable online store. Bigtable online store is optimized for high throughput and low latency, suitable for large-scale online serving.

Answer

Cloud SQL online store

Answer

Optimized online store

Answer

Firestore online store

Question 3

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. Point-in-time correct retrieval ensures that the feature values used in training correspond to the exact same point in time as the label, preventing data leakage and 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 4

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 designed to version large datasets and models alongside Git, storing data in remote storage and metadata in Git.

Answer

BigQuery table snapshots

Answer

Delta Lake

Answer

Git LFS

Question 5

An ML engineer needs to deploy a model from Vertex AI Model Registry to an endpoint. The model has multiple versions. They want to designate one version as the 'champion' for production traffic. How should they do this?

Accepted Answer

Use the 'champion' alias. Vertex AI Model Registry supports aliases; assigning the 'champion' alias to a model version allows routing production traffic to that version.

Answer

Set the version as default in the registry

Answer

Use the 'production' alias

Answer

Deploy the model directly to an endpoint without alias

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 supports cross-project sharing by registering the feature store at the organization level, allowing features to be accessed from different projects.

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 data scientist is using Vertex AI Workbench notebooks and wants to collaborate with team members in real-time on the same notebook. Which notebook type supports real-time collaboration?

Accepted Answer

Managed notebooks. Managed notebooks in Vertex AI Workbench support real-time collaboration similar to Google Docs, while user-managed instances do not.

Answer

JupyterLab on Compute Engine

Answer

Deep Learning VMs

Answer

User-managed notebooks

Question 9

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 lineage query API can retrieve upstream and downstream artifacts for a given execution, allowing tracing of artifacts from a specific pipeline run.

Answer

List executions and then list artifacts separately

Answer

Create a context and query executions

Answer

Query artifacts by filter on execution ID

Question 10

A company uses Delta Lake on Dataproc for their data lake. They need to ensure ACID transactions and schema enforcement for data ingested from streaming sources. Which Delta Lake feature should they enable?

Accepted Answer

Schema enforcement. Delta Lake provides ACID transactions, schema enforcement, and time travel. Schema enforcement is key for streaming data to prevent data quality issues.

Answer

Delta Lake time travel

Answer

Delta Lake change data feed

Answer

Optimized write

Question 11

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 allows creation of reusable pipeline components and templates that can be shared across teams, enabling standardization.

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Answer

Vertex AI Workbench

Question 12

A data engineer wants to create a BigQuery table snapshot for point-in-time recovery of a critical dataset. The snapshot should be created daily and retained for 30 days. What should they use?

Accepted Answer

BigQuery scheduled queries with CREATE SNAPSHOT. BigQuery table snapshots are created using the CREATE SNAPSHOT statement and can be scheduled with a retention period.

Answer

BigQuery copy job

Answer

BigQuery time travel

Answer

BigQuery export to Cloud Storage

Question 13

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

Answer

Cloud Logging

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Experiments

Question 14

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

Answer

Vertex AI Experiments

Answer

Model Registry

Question 15

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

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata