PMLE Collaborating within and across teams to manage data and models — Timed 20 Questions

Question 1

A data science team uses a shared Cloud Storage bucket to store training datasets. They notice that some team members accidentally overwrite existing datasets, causing issues with reproducibility. Which approach best prevents accidental overwrites while maintaining collaboration?

Accepted Answer

Enable object versioning on the bucket and use lifecycle rules to manage versions.. Option D is correct because enabling object versioning on a Cloud Storage bucket preserves all versions of an object, so even if a team member overwrites a dataset, the previous version remains accessible. This maintains collaboration (anyone can upload) while preventing permanent data loss. Lifecycle rules can then be used to manage storage costs by automatically deleting old versions after a specified period.

Answer

Use a single shared service account with strict IAM roles that allow only append operations.

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Require team members to manually rename files before uploading.

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Set bucket permissions to read-only for all team members except the data owner.

Question 2

A machine learning engineer needs to share a trained model with the product team for integration. The model is stored in Cloud Storage, and the product team’s service account needs read access. The engineer wants to follow the principle of least privilege. Which IAM configuration should be used?

Accepted Answer

Grant the product team's service account the roles/storage.objectViewer role at the bucket level.. Option B is correct because granting the product team's service account the roles/storage.objectViewer role at the bucket level provides read-only access to objects in that specific bucket, adhering to the principle of least privilege. This role allows the service account to list and read objects without granting broader permissions, such as modifying or deleting them, and scoping it to the bucket prevents unnecessary access to other buckets in the project.

Answer

Generate a signed URL with read access and share it with the product team.

Answer

Grant the product team's service account the roles/storage.objectAdmin role at the bucket level.

Answer

Grant the product team's service account the roles/storage.objectViewer role at the project level.

Question 3

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.

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Vertex AI Feature Store

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Vertex AI Model Registry

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Vertex AI Endpoints

Question 4

A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?

Accepted Answer

Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.. Option D is correct because using a git repository (e.g., Cloud Source Repositories) provides version control, branching, and a full history of changes, which is essential for collaborative development. This approach avoids conflicts by allowing team members to work on separate branches and merge changes systematically, unlike shared storage or manual methods that lack conflict resolution and audit trails.

Answer

Email notebook files to each other and manually merge changes.

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Store notebooks in a shared Cloud Storage bucket and access them simultaneously.

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Use Vertex AI Experiments to share notebook outputs.

Question 5

A machine learning team is deploying a model for real-time predictions using Vertex AI. They need to ensure that the deployment follows best practices for collaboration and governance. Which TWO actions should they take?

Accepted Answer

Use a continuous integration/continuous deployment (CI/CD) pipeline to deploy model versions.. Option A is correct because using a CI/CD pipeline for deploying model versions ensures automated, repeatable, and auditable deployments, which is a best practice for collaboration and governance. This approach enforces version control, testing, and approval gates, reducing the risk of errors and enabling rollback if needed.

Answer

Store all model artifacts in a local file system to reduce latency.

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Manually configure autoscaling parameters for the endpoint.

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Allow any team member to deploy directly to production without review.

Question 6

A financial services company uses Vertex AI Pipelines to train and deploy models for fraud detection. The ML team consists of data scientists who develop models and ML engineers who deploy them. They use a CI/CD pipeline with Cloud Build to build and push Docker images to Artifact Registry, then trigger Vertex AI Pipelines. Recently, the team noticed that a model deployed to production was trained on a dataset that had not been approved by the data governance team. Upon investigation, they found that a data scientist accidentally used an unapproved version of the training data by specifying a Cloud Storage path that was not the latest approved dataset. The company needs to enforce that only approved datasets are used in training jobs. Which approach should they take?

Accepted Answer

Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job.. Option C is correct because it enforces governance at the source by using IAM conditions to restrict access to only approved datasets, preventing unauthorized data from being used in training. This approach integrates with the CI/CD pipeline to automatically pass only approved dataset references, eliminating the risk of human error in specifying Cloud Storage paths.

Answer

Implement a manual approval process where data scientists request dataset paths from the data governance team before each training run.

Answer

After training, run a validation step that checks if the dataset used matches the latest approved version, and roll back if not.

Answer

Restrict all Cloud Storage buckets to be read-only for the data scientists, and have ML engineers copy approved datasets to a separate bucket.

Question 7

A data science team is using a shared Cloud Storage bucket to store training data. Multiple team members are simultaneously uploading new data files, and occasionally the wrong version of a file is used in training, leading to inconsistent results. Which best practice should the team implement to ensure data version consistency?

Accepted Answer

Enable object versioning on the Cloud Storage bucket and use the version ID when referencing data files.. Option C is correct because enabling object versioning on a Cloud Storage bucket preserves each object's history, allowing the team to reference a specific version ID when reading data files. This ensures that every training run uses the exact same version of a file, eliminating inconsistency from concurrent uploads. The version ID acts as an immutable pointer, decoupling the training process from the bucket's live state.

Answer

Use Cloud Composer to schedule a daily snapshot of the Cloud Storage bucket.

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Migrate all training data to BigQuery and use time-travel queries to access historical versions.

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Restrict write access to the bucket to only one team member using IAM roles.

Question 8

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

Accepted Answer

Store the training code in a Cloud Source Repository and tag commits with the experiment ID.. Option A is correct because storing training code in a Cloud Source Repository with tags linked to experiment IDs ensures that every team member can retrieve the exact code version used for a given experiment. This is a core reproducibility practice in Vertex AI Experiments, where the code snapshot is a key component of the experiment lineage.

Answer

Share a service account key with all team members so they can access the same resources.

Answer

Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.

Question 9

A team has set up the IAM policy above on a Vertex AI project. Alice, a data scientist, reports that she cannot create a Vertex AI Training custom job using a pre-built container. Other data scientists in the group 'data-scientists@example.com' have the same issue. What is the most likely cause?

Accepted Answer

The 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.. The 'roles/aiplatform.user' role includes the 'aiplatform.customJobs.create' permission, which is required to create custom training jobs. However, the issue is that Alice and her group cannot create a custom job using a pre-built container. The most likely cause is that the 'roles/aiplatform.user' role does not grant the permission to create custom training jobs with pre-built containers; it only allows using managed models or notebooks. To create custom training jobs, the 'roles/aiplatform.customJobUser' role is needed, which includes the necessary permissions for custom job creation.

Answer

The Vertex AI Custom Code Service Agent service account is missing the 'roles/aiplatform.user' role.

Answer

Alice is not included in the 'data-scientists@example.com' group.

Answer

The service account 'vertex-ai@project.iam.gserviceaccount.com' does not have permission to access the training data.

Question 10

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

Question 11

Match each ML pipeline component to its description.

Question 12

A company has multiple teams that need to access and manage ML models in Vertex AI. Different teams require different permission levels: the data science team should be able to create and update models, while the MLOps team should have full control. What is the recommended approach to manage access?

Accepted Answer

Use folders in Google Cloud Resource Manager and assign IAM roles at the folder level. Option B is correct because Google Cloud Resource Manager folders allow hierarchical IAM policy inheritance, enabling you to assign roles like 'roles/aiplatform.user' (for data science) and 'roles/aiplatform.admin' (for MLOps) at the folder level. This approach scales across multiple projects within the folder, ensuring consistent permissions without per-project duplication. It aligns with the principle of least privilege and centralized access management for Vertex AI resources.

Answer

Grant the 'aiplatform.user' role to a Google Group containing all users

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Use labels and tags on models to control access

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Create a separate Google Cloud project for each team

Question 13

A data scientist needs to share a BigQuery dataset with a colleague in a different team so they can run queries. What is the simplest and most secure way to grant access?

Accepted Answer

Add the colleague's account as a BigQuery Data Viewer on the dataset. Option A is correct because BigQuery dataset ACLs (via IAM) allow fine-grained access to specific datasets. Option B is wrong because sharing the entire project gives too much access. Option C is wrong because exporting to Cloud Storage adds unnecessary complexity and stale data. Option D is wrong because sharing the service account key is a security risk.

Answer

Export the dataset to Cloud Storage and share the bucket

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Share the service account key of a BigQuery job user with the colleague

Answer

Add the colleague's account as a Project Viewer on the entire project

Question 14

A team is building a CI/CD pipeline for ML using Cloud Build. The pipeline trains a model and deploys it to Vertex AI. Recently, a change in the data processing step caused the model to be trained with a different data version, leading to a failed deployment because the model was invalid. How should the team prevent this in the future?

Accepted Answer

Use a data versioning tool (e.g., DVC) to track datasets and ensure the pipeline always uses the correct version. Option C is correct because the root cause is a data version mismatch, not a code or environment issue. A data versioning tool like DVC (Data Version Control) tracks dataset versions via hash-based pointers in Git, ensuring the pipeline retrieves the exact dataset version used during training. This prevents silent failures when data processing steps change the data schema or content, which library pinning or manual reviews cannot guarantee.

Answer

Add a manual review step before training

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Pin all library versions in the Docker image

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Schedule a cron job to check for data changes

Question 15

Two teams are collaborating on a project and want to use a shared Feature Store in Vertex AI. They need to ensure that features are discoverable and that access is controlled. What is the best practice?

Accepted Answer

Use Vertex AI Feature Store and grant appropriate IAM roles to each team. Option B is correct because Vertex AI Feature Store supports sharing features with access controls (IAM) and enables discovery through the UI and API. Option A is wrong because BigQuery alone lacks feature store metadata and online serving. Option C is wrong because Cloud Storage is not a feature store. Option D is wrong because a ad-hoc pipeline is not a managed solution.

Answer

Export features to CSV files in Cloud Storage and share the bucket

Answer

Build a custom feature pipeline using Dataflow and store in Cloud SQL

Answer

Each team stores features in their own BigQuery table and shares the table

Question 16

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 17

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

Answer

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 18

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 19

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

Answer

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

Answer

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

Question 20

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