Collaborating within and across teams to manage data and models

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?

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?

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?

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?

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?

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?

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?

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?

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?

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

Match each ML pipeline component to its description.

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?

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?

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?

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?

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?

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?

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?

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

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?

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

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

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

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?

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

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?

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?

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

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?

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?

A data science team is using Vertex AI Feature Store for online serving. They notice that the online serving latency is high. What is the most likely cause?

A company uses Vertex AI Pipelines for model training and deployment. The pipeline includes a model evaluation step that produces metrics. If the metrics are below a threshold, the pipeline should fail and not deploy. Which component should they use?

A company has multiple business units using the same Vertex AI environment. They need to enforce that models deployed to production have passed a validation pipeline, and only the ML Engineering team can deploy to production. Which IAM configuration should they use?

A company uses Vertex AI Experiments to track ML training runs. They want to enforce that all training runs use only approved libraries from a central Artifact Registry to ensure compliance. Which approach should they take?

A team uses Vertex AI Experiments to track ML training runs. They want to automatically trigger a retraining pipeline when new labeled data arrives in BigQuery, and ensure the pipeline uses only approved libraries from a central artifact registry. Which combination of services should they use?

Which TWO of the following are best practices for versioning ML models and datasets?

Which TWO of the following are recommended methods to ensure data privacy when collaborating with external partners on ML projects?

Which THREE of the following are valid ways to share a Vertex AI model across two different Google Cloud projects?

Refer to the exhibit. A team runs the command above and sees only two models. They know there is a model 'model-v3' created three days ago. What is the most likely reason it is not listed?

Refer to the exhibit. A team runs this Vertex AI Pipeline definition but the deploy component never executes, even though the evaluate step outputs a metric of 0.9. What is the most likely cause?

A large e-commerce company uses Vertex AI to train a recommendation model daily. The training pipeline is built with Vertex AI Pipelines and involves three steps: data preprocessing, training, and model evaluation. The pipeline is triggered by a Cloud Scheduler job every morning at 8 AM. Recently, the pipeline has been failing intermittently during the data preprocessing step, with an error message indicating 'ResourceExhausted: Quota limits exceeded for read api requests.' The team has checked and confirmed that the quota for BigQuery read requests is not exceeded at the project level. The preprocessing step reads data from a BigQuery table with billions of rows. The team has also noticed that the pipeline runs on a custom machine type (n1-standard-4) with a persistent disk. What is the most likely cause of this error?

A data science team is collaborating on a project to build a churn prediction model. They use Vertex AI Workbench instances for development. Each data scientist has their own instance with a persistent disk. They share code via a GitHub repository. They want to ensure that the model training is reproducible across different team members' environments. Currently, they manually install Python packages in their instances, and they have noticed that the model metrics differ slightly between runs on different instances. Which of the following is the best action to ensure reproducibility?

A team of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?

A Vertex AI pipeline is triggered from Cloud Build using the configuration above. The pipeline fails with an error: 'Unable to submit build: The source code is not available.' What is the most likely cause?

Your team manages multiple ML models in Vertex AI Model Registry. Each model has several versions deployed to different endpoints for testing and production. You need to implement a process where a model version can be promoted from a staging environment to production only after it has passed automated validation tests and been approved by a designated reviewer. The team uses CI/CD pipelines (Cloud Build) for training and deployment. Currently, model versions are deployed to endpoints using Vertex AI Endpoints with a single traffic split configuration. You want to track promotion requests and enforce approval gates. What should you do?