While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

• A.Increase the CPU load
• B.Add caching to the pipeline
• C.Increase the network bandwidth
• D.Add parallel interleave to the pipeline

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You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

• A.Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.
• B.Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.
• C.Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.
• D.Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

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You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time.
User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

• A.Rate of return as measured by additional revenue generated minus the cost of developing a new model
• B.Average time players wait before being assigned to a team
• C.User engagement as measured by the number of battles played daily per user
• D.Precision and recall of assigning players to teams based on their predicted versus actual ability

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You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI
/CD workflow?

• A.Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
• B.Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
• C.Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
• D.Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

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You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

• A.1. Create a new service account and grant it the Notebook Viewer role.
  2 Grant the Service Account User role to each team member on the service account.
  3 Grant the Vertex Al User role to each team member.
  4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
• B.1. Grant the Vertex Al User role to the default Compute Engine service account.
  2. Grant the Service Account User role to each team member on the default Compute Engine service account.
  3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.
• C.1 Create a new service account and grant it the Vertex Al User role.
  2 Grant the Service Account User role to each team member on the service account.
  3. Grant the Notebook Viewer role to each team member.
  4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
• D.1 Grant the Vertex Al User role to the primary team member.
  2. Grant the Notebook Viewer role to the other team members.
  3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

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