You have developed a BigQuery ML model that predicts customer chum, and deployed the model to Vertex AI Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

• A. 1 Enable request-response logging on Vertex AI Endpoints
  2. Schedule a TensorFlow Data Validation job to monitor prediction drift
  3. Execute model retraining if there is significant distance between the distributions
• B. 1. Create a Vertex AI Model Monitoring job configured to monitor prediction drift
  2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected
  3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery
• C. 1. Create a Vertex AI Model Monitoring job configured to monitor training/serving skew
  2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected
  3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery
• D. 1. Enable request-response logging on Vertex AI Endpoints
  2. Schedule a TensorFlow Data Validation job to monitor training/serving skew
  3. Execute model retraining if there is significant distance between the distributions

You are collaborating on a model prototype with your team. You need to create a Vertex AI 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 AI User role to each team member
  4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account
• B. 1. Grant the Vertex AI 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 AI 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 AI 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 AI Workbench user-managed notebook instance that uses the new service account
• D. 1. Grant the Vertex AI User role to the primary team member
  2. Grant the Notebook Viewer role to the other team members
  3. Provision a Vertex AI Workbench user-managed notebook instance that uses the primary user's account

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table. How should you perform the inference?

• A. Export the historical data to Cloud Storage in CSV format. Configure a Vertex AI batch prediction job to generate predictions for the exported data
• B. Import the TensorFlow model by using the CREATE MODEL statement in BigQuery ML. Apply the historical data to the TensorFlow model
• C. Configure a Vertex AI batch prediction job to apply the model to the historical data in BigQuery
• D. Export the historical data to Cloud Storage in Avro format. Configure a Vertex AI batch prediction job to generate predictions for the exported data

You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user- managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

• A. There is training-serving skew in your production environment.
• B. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
• C. There is not a sufficient amount of training data.
• D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex AI. You need to configure the worker pools of the distributed training job. What should you do?

• A. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
• B. Configure the machines of the first two pools to have TPUs, and to use a container image where your training code runs. Configure the third pool to have TPUs, and use the reductionserver container image.
• C. Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs. Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
• D. Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to have GPUs, and use the reductionserver container image.