PMLE Collaborating within and across teams to manage data and models Practice Test 2 — 15 Questions

Question 1

A team is using Cloud Composer to orchestrate ML workflows. They want to allow multiple data scientists to contribute DAGs without interfering with each other. What is the recommended approach?

Accepted Answer

Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer. Option C is correct because Cloud Composer (based on Apache Airflow) recommends managing DAGs via source control and CI/CD pipelines to ensure version control, code review, and consistent deployment. This prevents conflicts when multiple data scientists contribute, as each change is tracked and tested before being synced to the DAGs folder in Cloud Storage, avoiding overwrites or broken workflows.

Answer

Give each data scientist write access to the DAGs folder in Cloud Storage

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Use a complex naming convention for DAG files to avoid overwriting

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Create a separate Cloud Composer environment for each data scientist

Question 2

A company needs to maintain an audit trail of model changes for compliance. Multiple teams will be updating models. What is the best approach to track who created, modified, or deployed each model version?

Accepted Answer

Use Vertex AI Experiments and Metadata to track model lineage and audit logs. Option A is correct because Vertex AI automatically logs metadata (including user identity) via Cloud Audit Logs and ML Metadata. Option B is wrong because Cloud Storage logs only show object-level access, not model-specific actions. Option C is wrong because manual logging is error-prone. Option D is wrong because Cloud Logging alone does not correlate events to model versions.

Answer

Enable Cloud Storage audit logs and require all model files to be stored in a bucket

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Use Cloud Logging to collect logs from all services and search for model names

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Ask team members to maintain a shared spreadsheet of changes

Question 3

A team is using AI Platform Data Labeling Service to label data for a classification model. They want to allow a labeler from a different team to work on the same dataset. What is the correct way to grant access?

Accepted Answer

Add the labeler as a participant in the labeling task and assign IAM roles on the dataset. Option D is correct because labeling tasks are shared by granting the labeler role on the dataset resource. Option A is wrong because sharing the entire project gives too much access. Option B is wrong because the Data Labeling Service does not use Cloud Storage ACLs for task access. Option C is wrong because exporting and reimporting causes duplication.

Answer

Add the labeler's account as a Project Editor on the project

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Share the Cloud Storage bucket containing the data with the labeler

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Export the dataset and have the labeler create a new dataset

Question 4

To enable collaboration on notebook-based experiments across teams, what is the recommended approach in Google Cloud?

Accepted Answer

Use Colab Enterprise notebooks with shared runtimes and IAM permissions. Colab Enterprise notebooks with shared runtimes and IAM permissions is the recommended approach because it provides a fully managed, collaborative environment where multiple users can work on the same notebook simultaneously, with fine-grained access control via IAM and consistent runtime configurations. This eliminates version conflicts and environment drift, which are common in distributed notebook workflows.

Answer

Share Docker images containing the notebook environment

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Each team member works on their own local Jupyter notebook and shares screenshots

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Store notebooks in a Cloud Storage bucket and open them with Vertex AI Workbench

Question 5

Two teams independently develop two different versions of a model for the same use case. They both deploy to the same Vertex AI endpoint, causing conflicts. What is the best way to manage multiple model versions and avoid conflicts in a collaborative environment?

Accepted Answer

Use Vertex AI Model Registry with staging and production channels, and implement CI/CD to control promotions. Option C is correct because using a model registry with separate staging and production channels helps control which version is promoted. Option A is wrong because deploying to different endpoints increases management overhead. Option B is wrong because versioning metadata does not enforce deployment order. Option D is wrong because separate projects create silos and increase cost.

Answer

Have each team work on a separate Google Cloud project

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Use custom metadata to tag each version and rely on team coordination

Answer

Deploy each team's model to a separate endpoint

Question 6

Which TWO options are recommended practices for managing model versions across teams in Google Cloud?

Accepted Answer

Use Vertex AI Model Registry to track model versions and their deployment history. Vertex AI Model Registry is the recommended service for managing model versions across teams because it provides a centralized repository to track model versions, their associated metadata, and deployment history. It integrates natively with Vertex AI endpoints and pipelines, enabling consistent governance and lineage tracking across the ML lifecycle.

Answer

Store all model files in a GitHub repository

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Maintain a custom database to map model names to artifact locations

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Use AI Platform (Unified) Models as the primary model registry

Question 7

Which TWO actions should be taken to ensure reproducibility of ML experiments when collaborating across teams on Vertex AI?

Accepted Answer

Lock dependency versions in a container image used for training. Locking dependency versions in a container image ensures that the exact same software environment (e.g., Python packages, CUDA libraries, system tools) is used every time a training job runs. This eliminates variability from package updates or OS patches, which is a fundamental requirement for reproducibility across teams. Vertex AI supports custom containers for training, making this a direct and reliable method.

Answer

Share notebooks via Colab Enterprise with real-time editing

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Allow each team to use their own preferred environment

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Always use random seeds for all random operations

Question 8

Which THREE considerations are important when setting up a shared feature store in Vertex AI Feature Store for multiple teams?

Accepted Answer

Enable feature monitoring for data quality and freshness. Option A is correct because Vertex AI Feature Store provides built-in feature monitoring that tracks data quality metrics (e.g., fraction of null values, distribution drift) and freshness (e.g., staleness of feature values). Enabling this monitoring is critical when multiple teams share a feature store to ensure that features remain reliable and up-to-date for downstream models, preventing silent degradation.

Answer

Use separate BigQuery tables for each team's features

Answer

Allow each team to build independent ingestion pipelines

Question 9

Refer to the exhibit. A team runs this command to upload a model to Vertex AI. They want to create this model as a new version under an existing model named 'my_model'. What is missing from the command?

Accepted Answer

--parent-model=my_model. Option E is correct because the `--parent-model` flag is required when uploading a new model version to an existing model in Vertex AI. Without specifying the parent model name, the command would attempt to create a brand-new model rather than adding a version to the existing 'my_model'. The `gcloud ai models upload` command uses this flag to associate the new version with the specified parent model.

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--description='Second version'

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--version=v2

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--labels=team=ml

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--service-account=sa@project.iam.gserviceaccount.com

Question 10

Refer to the exhibit. A team leader applies this IAM policy on a Vertex AI model resource. What does the condition accomplish?

Accepted Answer

Limits access to models whose resource name starts with 'dev-'. The condition in the IAM policy uses the `resource.name.startsWith('dev-')` condition expression, which restricts access to Vertex AI model resources whose resource name begins with the prefix 'dev-'. This is a common pattern for environment-based access control, allowing the data scientist to only interact with models designated for development.

Answer

Allows the data scientist to access model evaluations only

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Limits access to models owned by the data scientist

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Limits access to models only in the us-central1 region

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Limits access to models created after a certain date

Question 11

Refer to the exhibit. A team uses this Cloud Build configuration to deploy a model to a Vertex AI endpoint. The build succeeds up to the 'upload' step, but the 'deploy-model' step fails with an error that the model 'my-model' does not exist. What is the most likely cause?

Accepted Answer

The model was not uploaded because the artifact URI is a directory, not a valid SavedModel. The 'deploy-model' step fails because the model was not successfully uploaded. Cloud Build's 'upload' step expects a valid SavedModel artifact (a directory containing a saved_model.pb file and variables subdirectory). If the artifact URI points to a directory that is not a valid SavedModel, the upload may appear to succeed but does not register a usable model resource, causing the subsequent deploy step to fail with 'model does not exist'.

Answer

The deploy step uses the display name instead of the model resource ID

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The Vertex AI API was not enabled for the project

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The region in the deploy step does not match the model's region

Question 12

A data science team is deploying a large NLP model to Vertex AI for real-time inference. They notice high latency per request. Which action should they take first to reduce latency?

Accepted Answer

Use Vertex AI Model Optimization to quantize the model and deploy on a smaller machine.. Option C is correct because it directly addresses the root cause of high latency in real-time inference: model size and compute requirements. Vertex AI Model Optimization applies quantization or pruning to reduce the model's memory footprint and computational cost, allowing it to run on a smaller, faster machine (e.g., fewer vCPUs or less GPU memory) while maintaining acceptable accuracy. This is the first step recommended by Google Cloud best practices for latency-sensitive deployments, as it reduces per-request processing time without requiring architectural changes.

Answer

Use Cloud Functions for inference.

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Use model optimization techniques like quantization or pruning.

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Enable autoscaling and set min replicas to 5.

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Implement batch prediction instead of online prediction.

Question 13

A team wants to ensure that only approved models are deployed to production. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Model Registry with versioning and alias.. Vertex AI Model Registry with versioning and alias (Option E) is the correct feature because it allows teams to manage model lifecycle, track approved versions, and assign aliases (e.g., 'champion' or 'production') to designate which model is approved for deployment. This ensures only vetted models are promoted to production, aligning with governance and compliance requirements.

Answer

Vertex AI Experiments.

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Cloud DLP.

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Vertex AI Pipelines.

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Vertex AI Feature Store.

Question 14

A team wants to share a trained model with another team who will deploy it to a different Google Cloud project. Which is the recommended way to transfer the model?

Accepted Answer

Use Vertex AI Model Registry with cross-project IAM permissions to allow the second project to access the model.. Option E is correct because Vertex AI Model Registry supports cross-project access via IAM permissions, allowing the second project to directly deploy the model without copying artifacts. This approach maintains a single source of truth, avoids data duplication, and leverages Vertex AI's built-in versioning and lineage tracking. It is the recommended pattern for sharing models across projects in Google Cloud.

Answer

Copy the model artifact from one project's Cloud Storage to another using gsutil.

Answer

Export the model as a SavedModel, store in a shared Cloud Storage bucket, and import into the second project.

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Package the model in a Docker container and push to a cross-project Container Registry.

Answer

Use Cloud Marketplace to publish the model.

Question 15

A data engineer is setting up a data pipeline for ML training. The raw data is in Cloud Storage, and they need to transform it into features stored in Vertex AI Feature Store. The pipeline should run daily. Which service should they use?

Accepted Answer

Dataflow with Apache Beam pipeline.. Dataflow with Apache Beam is the correct choice because it provides a fully managed, serverless service for both batch and streaming data processing, which is ideal for transforming raw data from Cloud Storage into features for Vertex AI Feature Store on a daily schedule. Dataflow handles auto-scaling, exactly-once processing, and integrates natively with Google Cloud services, making it efficient for ETL pipelines that need to run reliably at scale.

Answer

Cloud Composer with Airflow DAG.

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Cloud Dataproc with Spark.

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Vertex AI Pipelines with custom components.

Answer

Cloud Functions on a schedule.