Databricks Generative AI Engineer Associate Question 35
Select 3You are developing a multi-stage reasoning system using Databricks and need to define and order tools that gather knowledge or take actions in a structured reasoning pipeline. Which of the following tools are appropriate for gathering knowledge and executing actions in a multi-stage reasoning system?
- A
A vector database for retrieving relevant context for a query
- B
A large language model (LLM) for generating answers or reasoning over retrieved data
- C
An orchestration framework like LangChain to link tools and manage reasoning stages
- D
A data visualization tool like Tableau for presenting results to stakeholders
- E
A GPU-enabled cluster for training a new deep learning model
Show answer and explanation
Correct answers: A, B, C
Explanation
In a multi-stage reasoning system, tools like vector databases for knowledge retrieval, LLMs for reasoning, and orchestration frameworks like LangChain for managing tool interactions are essential. These tools work together to gather and process knowledge or execute actions. Visualization tools and model training infrastructure, while important in other contexts, are not directly involved in the multi-stage reasoning process.
- A. Correct.
A vector database is crucial for multi-stage reasoning as it enables retrieval of relevant context or embeddings needed for knowledge gathering.
- B. Correct.
A large language model (LLM) is a key tool for reasoning and generating answers based on retrieved knowledge or inputs.
- C. Correct.
An orchestration framework like LangChain is critical for structuring the reasoning pipeline and linking tools in a logical sequence.
- D. Incorrect.
While useful for presenting results, a data visualization tool like Tableau is not directly involved in knowledge gathering or reasoning in a multi-stage pipeline.
- E. Incorrect.
A GPU-enabled cluster is more relevant for training new models, not for the reasoning or action-taking stages of a multi-stage system.