You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?