Online Access Free Databricks-Generative-AI-Engineer-Associate Exam Questions

When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

• A.Reach out to the data curators directly before you have started using the trained model to let them know.
• B.Reach out to the data curators directly after you have started using the trained model to let them know.
• C.Only use data explicitly labeled with an open license and ensure the license terms are followed.
• D.Use any available data you personally created which is completely original and you can decide what license to use.

A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?

• A.Compare the Levenshtein distances of returned results against a representative sample of test inputs
• B.Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
• C.Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
• D.Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?

• A.Chatbot Arena Leaderboard
• B.Number of model parameters
• C.Number of model downloads last month
• D.Big Code Models Leaderboard
• E.MTEB Leaderboard

A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

• A.vsc.create_delta_sync_index()
• B.vsc.similarity_search()
• C.vsc.get_index()
• D.vsc.create_direct_access_index()

A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
Which change could the Generative Al Engineer perform to mitigate this issue?

• A.Split the LLM output by newline characters to truncate away the summarization explanation.
• B.Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.
• C.Tune the chunk size of news articles or experiment with different embedding models.
• D.Provide few shot examples of desired output format to the system and/or user prompt.