Online Access Free Databricks-Generative-AI-Engineer-Associate Practice Test

A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output "In Stock" if the product is available or only the term "Out of Stock" if not.
Which prompt will work to allow the engineer to respond to call classification labels correctly?

• A.You will be given a customer call transcript where the customer asks about product availability. The outputs are either "In Stock" or "Out of Stock". Format the output in JSON, for example: {"call_id": "123", "label": "In Stock"}.
• B.You will be given a customer call transcript where the customer inquires about product availability. Respond with "In Stock" if the product is available or "Out of Stock" if not.
• C.Respond with "Out of Stock" if the customer asks for a product.
• D.Respond with "In Stock" if the customer asks for a product.

A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

• A.L!ama-2-70b-chat-hf
• B.whisper-large-v3 (1.6B)
• C.MPT-30B-lnstruct
• D.DBRX

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 paragraph level embeddings with each chunk.
• B.Implement a hierarchical tree structure, like RAPTOR, to group similar legal concepts.
• C.Implement sentence level embeddings with each chunk tagged with the time to enable metadata filtering.
• D.Implement windowed summarization with overlapping chunks.

A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?

• A.pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace
• B.Pick an embedding model trained on related domain knowledge
• C.Pick the most recent and most performant open LLM released at the time
• D.Pick an embedding model with multilingual support to support potential multilingual user questions

A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?

• A.Repetition of responses
• B.Lack of relevance
• C.Randomness
• D.Diversity of responses