Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate Free Exam Questions

A. Change embedding models and compare performance.

B. Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

C. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.
Choose the strategy that gives the best performance metric.

D. Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

E. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

A. Reach out to the data curators directly after you have started using the trained model to let them know.

B. Reach out to the data curators directly before you have started using the trained model to let them know.

C. Use any available data you personally created which is completely original and you can decide what license to use.

D. Only use data explicitly labeled with an open license and ensure the license terms are followed.

A. Use the largest LLM possible because that gives the best performance for any general queries

B. Limit the number of queries a customer can send per day

C. Limit the number of relevant documents available for the RAG application to retrieve from

D. Pick a smaller LLM that is domain-specific

A. Repetition of responses

B. Lack of relevance

C. Randomness

D. Diversity of responses

A. Foundation Model APIs

B. AutoML

C. DatabrickslQ

D. Feature Serving

A. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.

B. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

C. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

D. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

A. Increase the amount of compute that powers the LLM to process input faster

B. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user

C. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

D. Reduce the time that the users can interact with the LLM

A. Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond

B. Input: Customer reviews: Output Classify review sentiment

C. Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

D. Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user's interactions, then respond