You are training a ResNet-50 computer vision model on Gemini Enterprise Agent Platform using a custom PyTorch container. The training job is configured with a generic machine type and one NVIDIA A100 GPU. During the training, you monitor the resource utilization charts. You observe that the GPU utilization is volatile, frequently dropping to 0% for short intervals, while the CPU utilization remains consistently above 90%. You want to maximize GPU utilization to reduce training time. What should you do?

You have deployed a deep learning model to an Agent Platform endpoint using a machine type with NVIDIA GPUs. You initially configured the endpoint to autoscale based on a target CPU utilization of 60%. During a load test, you observe that prediction latency increases significantly as traffic rises, but the number of replicas remains constant. Cloud Monitoring indicates that CPU utilization stays below 40%, while the GPU utilization consistently exceeds 90%. You need to ensure that the endpoint scales efficiently to handle the increased load. What should you do?

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API You want to use the most efficient approach. What should you do?

You are in the exploratory phase of development of a demand forecasting model. Several terabytes of historical sales and inventory records are stored in a BigQuery table. You need to create a new, clean table to perform preliminary analyses on metrics, such as moving averages of sales figures and weekly aggregates, without additional data movement. You want to use the simplest approach and minimize overhead. What should you do?