Online Access Free Professional-Machine-Learning-Engineer Exam Questions

You are using generative AI to build a conversational agent to support bank customers' inquiries about their product base and transaction history. You must ensure that the agent does not include personally identifiable information (PII) in its responses. You want to implement this requirement with minimal effort. What should you do?

• A.Configure the safety_settings thresholds within the Agent Platform model parameters to Block_Most.
• B.Implement Model Armor with Sensitive Data Protection to detect and redact PII from the agent's responses.
• C.Develop a post-processing wrapper using Python's re module with patterns r'\b(?:\d{4}[- ]?){3}\d{4}\b' to identify and redact credit card numbers.
• D.Use sampled Shapley (SHAP) values to understand the model's predictions and ensure that it is not biased toward revealing PII.

You have recently developed a custom model for image classification by using a neural network.
You need to automatically identify the values for learning rate, number of layers, and kernel size.
To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management.
What should you do?

• A.Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
• B.Create a Vertex AI pipeline that runs different model training jobs in parallel.
• C.Create a Vertex AI hyperparameter tuning job.
• D.Train an AutoML image classification model.

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

• A.Use AI Platform Training to execute the experiments.
  Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
• B.Use Kubeflow Pipelines to execute the experiments.
  Export the metrics file, and query the results using the Kubeflow Pipelines API.
• C.Use AI Platform Training to execute the experiments.
  Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D.Use AI Platform Notebooks to execute the experiments.
  Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

You are responsible for managing the recommendation engine on an ecommerce website. The current model is deployed on a public Agent Platform endpoint and is actively serving production traffic. You have developed an updated version of the model and registered the model in Model Registry. You need to perform an A/B test to compare the new model's performance to the current model.
You plan to implement a traffic split, routing 10% of all incoming production requests to the new model and sending the remaining 90% to the current model. You want this transition to be transparent, and require no modifications to the client application that calls the endpoint. What should you do?

• A.Deploy the new model to the Agent Platform endpoint where the current model is deployed.
  Configure the endpoint's traffic split to allocate 10% of user requests to the new model.
• B.Deploy the new model to a serverless Cloud Run service. Implement logic in the client application that routes 10% of user requests to the Cloud Run service and the rest to the original endpoint.
• C.Deploy the new model to a new Agent Platform endpoint. Configure Cloud Load Balancing to send 10% of user requests to the new endpoint and the rest to the original endpoint.
• D.Create two separate Agent Platform Pipelines, one for each model version. Use Agent Platform Experiments to run both pipelines simultaneously. Configure the experiment to send 10% of user requests to the new model's pipeline and the rest to the production model's pipeline.

You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic. You plan to direct all user traffic to the new model. You need to deploy the model with minimal disruption to your application. What should you do?

• A.1. Create a new model. Set the parentModel parameter to the model ID of the currently deployed model. Upload the model to Vertex AI Model Registry.
  2. Deploy the new model to the existing endpoint, and set the new model to 100% of the traffic
• B.1. Create a new endpoint
  2. Create a new model. Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version. Upload the model to Vertex AI Model Registry
  3. Deploy the new model to the new endpoint, and set the new model to 100% of the traffic.
• C.1. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry
  2. Deploy the new model to the existing endpoint
• D.1. Create a new endpoint
  2. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry
  3. Deploy the new model to the new endpoint
  4. Update Cloud DNS to point to the new endpoint