You work for a food product company. Your company's historical sales data is stored in BigQuery.You need to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

You built a legal document assistant with Gemini. Each user session repeatedly asks questions about the same 300-page contract, and the contract text is resent with every request. Costs and time-to-first-token are both higher than acceptable. The contract does not change during a session. You need to reduce cost and latency. What should you do?

Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and
1,000 images with credit cards. You now have to train a model with the following label map:
[`drivers_license', `passport', `credit_card'].
Which loss function should you use?

Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?