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

You recently deployed a model to a Vertex AI endpoint. Your data drifts frequently, so you have enabled request-response logging and created a Vertex AI Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift. What should you do?

• A. Replace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV)
• B. Decrease the sample_rate parameter in the RandomSampleConfig of the monitoring job
• C. Increase the monitor_interval parameter in the ScheduleConfig of the monitoring job
• D. Replace the monitoring job with a custom SQL script to calculate statistics on the features and predictions in BigQuery

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?

• A. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
• B. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
• C. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
• D. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.

You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters.
You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
- For the embedding dimension, you set the type to INTEGER with a
minValue of 16 and maxValue of 64.
- For the learning rate, you set the type to DOUBLE with a minValue of
10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

• A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
• B. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
• C. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
• D. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.