Databricks Databricks-Generative-AI-Engineer-Associate Practice Exam Questions

Databricks Databricks-Generative-AI-Engineer-Associate Sample Question Answers

Question # 1

A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this? 

A. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent 
B. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools 
C. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent t
D. 1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use COT reasoning to determine the appropriate agen

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Question # 2

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services. Given the following user input: “I have been experiencing severe headaches and dizziness for the past two days.” Which response is most appropriate for the chatbot to generate? 

A. Here are a few relevant articles for your browsing. Let me know if you have questions after reading them. 
B. Please call your local emergency services. 
C. Headaches can be tough. Hope you feel better soon! 
D. Please provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness. 

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Question # 3

A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window. Which model fits this need?

A. DistilBERT 
B. MPT-30B 
C. Llama2-70B 
D. DBRX 

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Question # 4

A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort. How can they set up their Vector Search index to support this use case?

A. Split articles by 10 day blocks and return the block closest to the query. 
B. Include metadata columns for article date and topic to support metadata filtering. 
C. pass the query directly to the vector search index and return the best articles. 
D. Create separate indexes by topic and add a classifier model to appropriately pick the best index. 

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Question # 5

A Generative AI Engineer is building an interactive catalog for a company’s inventory system that allows users to search for any item using a plain-text description. There are currently about 17,000 items, and new items are not frequently added. They need a solution that will be the most cost-effective and easy for the company to maintain. Which solution should the engineer choose?

A. Storage-optimized vector search with a Direct Vector Access index, triggered sync. 
B. Standard vector search with Databricks-managed embeddings and a Delta Sync index, continuous sync. 
C. Standard vector search with self-managed embeddings and a Delta Sync index, continuous sync. 
D. Standard vector search with Databricks-managed embeddings and a Delta Sync index, triggered sync.

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Question # 6

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. BLEU metric
 C. NDCG metric 
D. RECALL metric 

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Question # 7

A Generative AI Engineer is managing prompt templates using MLflow v3.x for a document summarization pipeline. A regulatory audit requires the team to demonstrate exactly which prompt version was used to generate outputs on a specific date three months ago, including the exact prompt text and any variables used at that time. Which combination of MLflow v3.x capabilities allows the engineer to satisfy this audit requirement?

A. MLflow Model Registry webhooks and a downstream audit log stored in an external database. 
B. MLflow autologging and Delta Lake time travel on the inference table. 
C. MLflow experiment tags and an automatically scripted changelog stored in a Databricks notebook. 
D. MLflow Prompt Registry version history and logged runs that reference the prompt name and version used during inference. 

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Question # 8

A Generative Al Engineer at an automotive company would like to build a questionanswering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information. Which of the following components will NOT be useful in building such a chatbot? 

A. Response-generating LLM 
B. Invite users to submit long, rather than concise, questions 
C. Vector database 
D. Embedding model 

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Question # 9

A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI. The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies. Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

A. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them. 
B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed. 
C. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
 D. Consolidate all SnoPen AI related documents into a single chunk in the vector database. 

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Question # 10

A Generative AI Engineer has been reviewing issues with their company's LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team? 

A. It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses. 
B. It allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated. 
C. It allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases. 
D. It reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens. 

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