Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 49 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 49

Select 3Google Cloud Platform

You are tasked with building a retrieval-augmented generation (RAG) application for a customer support chatbot using Vertex AI. The application must retrieve relevant documents from a knowledge base stored in BigQuery and use a fine-tuned generative AI model to generate responses. Which combination of steps should you take to implement this solution?

  1. A

    Use BigQuery ML to train a retrieval model and deploy it to Vertex AI.

  2. B

    Use Vertex AI Matching Engine to index and retrieve relevant documents from the BigQuery knowledge base.

  3. C

    Build a custom API to connect the generative AI model with the retrieval system.

  4. D

    Use a pre-trained generative AI model in Vertex AI Model Garden and fine-tune it with your domain-specific data.

  5. E

    Combine the document retrieval system with the fine-tuned generative model in a custom Vertex AI pipeline.

Show answer and explanation

Correct answers: B, D, E

Explanation

To build a RAG application in Vertex AI, you should use Vertex AI Matching Engine for efficient document retrieval from the BigQuery knowledge base, fine-tune a pre-trained generative AI model for accurate response generation, and combine these components in a custom Vertex AI pipeline. This approach ensures scalability, efficiency, and ease of integration without the need for custom APIs.

  • A. Incorrect.

    Incorrect. While BigQuery ML can be used to train models, it is not the optimal choice for document retrieval in a RAG workflow. Vertex AI Matching Engine is better suited for this task.

  • B. Correct.

    Correct. Vertex AI Matching Engine is specifically designed for fast and efficient retrieval of relevant documents and can be used to query your BigQuery knowledge base.

  • C. Incorrect.

    Incorrect. There is no need to build a custom API as Vertex AI provides tools to integrate retrieval and generation components efficiently.

  • D. Correct.

    Correct. Fine-tuning a pre-trained generative AI model from Vertex AI Model Garden with domain-specific data ensures the chatbot generates accurate and contextually relevant responses.

  • E. Correct.

    Correct. Combining the retrieval system and the generative model in a Vertex AI pipeline ensures the RAG workflow is automated and operationalized effectively.

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