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Reference: https://www.databricks.com/learn/certification/genai-engineer-associate
Databricks Certified Generative AI Engineer Associate Sample Questions:
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
- A. Use the largest LLM possible because that gives the best performance for any general queries
- B. Limit the number of relevant documents available for the RAG application to retrieve from
- C. Pick a smaller LLM that is domain-specific
- D. Limit the number of queries a customer can send per day
Correct Answer: C 🗳️
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A Generative Al Engineer is developing a RAG system for their company to perform internal document Q & A for structured HR policies, but the answers returned are frequently incomplete and unstructured It seems that the retriever is not returning all relevant context The Generative Al Engineer has experimented with different embedding and response generating LLMs but that did not improve results.
Which TWO options could be used to improve the response quality?
Choose 2 answers
- A. Fine tune the response generation model
- B. Split the document by sentence
- C. Increase the document chunk size
- D. Add the section header as a prefix to chunks
- E. Use a larger embedding model
Correct Answer: C,D 🗳️
Explanation: Only visible for Real4dumps members. You can sign-up / login (it's free).
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer ' s system perform?
- A. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
- B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
- C. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
- D. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
Correct Answer: C 🗳️
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A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal cases. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text.
The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?
- A. Implement sentence level embeddings with each chunk tagged with the time to enable metadata filtering.
- B. Implement windowed summarization with overlapping chunks.
- C. Implement paragraph level embeddings with each chunk.
- D. Implement a hierarchical tree structure, like RAPTOR, to group similar legal concepts.
Correct Answer: B 🗳️
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A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM ' s on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?
- A. ROUGE metric
- B. NDCG metric
- C. BLEU metric
- D. RECALL metric
Correct Answer: C 🗳️
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