Google Cloud Professional Machine Learning Engineer PMLE — Topic 1 Question 299

Question

Your team is experimenting with developing smaller, distilled LLMs for a specific domain. You have performed batch inference on a dataset by using several variations of your distilled LLMs and stored the batch inference outputs in Cloud Storage. You need to create an evaluation workflow that integrates with your existing Vertex AI pipeline to assess the performance of the LLM versions while also tracking artifacts. What should you do?

Accepted Answer

Correct answer: D. The correct answer is D. Option D: Use the Automatic side-by-side (AutoSxS) pipeline component that processes the batch inference outputs from Cloud Storage, aggregates evaluation metrics, and writes the results to a BigQuery table. Google Cloud Batch schedules and runs batch jobs at scale without managing infrastructure. Cloud Storage provides durable, scalable object storage that is fully managed. BigQuery is a serverless, petabyte-scale data warehouse for fast SQL analytics with no infrastructure to manage.

Google Cloud Professional Machine Learning Engineer PMLE — Question 299