Databricks Generative AI Engineer Associate Practice Exam Questions and Answers – Part 9/9

Answer: A. CodeLlama-34B-Instruct-hf

CodeLlama is explicitly pretrained and instruction-tuned for code generation, making it the optimal specialized model. General-purpose models like Llama-2 or Mixtral can write code, but they lack the targeted architecture needed for high-quality out-of-the-box programming tasks.

Answer: A. Encouraging customers to ask lengthy, detailed questions instead of short ones

Encouraging long questions does not improve a RAG chatbot, which relies on semantic embeddings to understand brief queries naturally. The LLM, embedding model, and vector database are foundational components, making the query length a irrelevant design factor.

Answer: C. Craft prompts that explicitly instruct the LLM to produce output in the required tone and style

Crafting explicit prompts is the most efficient initial method to steer model behavior because it adjusts output without modifying weights or requiring new pipelines. For the exam, always choose prompt engineering over fine-tuning when the goal is a low-cost, immediate adjustment to tone or style.

Answer: D. Build a multi-step chain where one LLM first identifies sentiment and, based on that classification, dynamically adjusts the system prompt used by the response-generation LLM.

Building a multi-step chain allows a dedicated model to classify sentiment and dynamically adapt the system prompt for the final response. This orchestration pattern is preferred over retrieval or basic regression because LLMs handle nuanced emotional cues and tone shifts much more effectively.

Answer: A. Provide few-shot examples that clearly demonstrate the exact output format expected

Providing few-shot examples reliably forces the model to mimic a strict output format without generating unwanted explanations. Zero-shot instructions are often ignored for formatting, and chain-of-thought prompting would actually increase the reasoning text you want to eliminate.

Answer: A. Input: Customer service chat histories; Output: Organize chats by user, summarize each user's prior interactions, and then craft a response

Organizing and summarizing chat histories by user enables faster responses while maintaining a highly personalized experience based on prior context. Retrieval pipelines lack personalization, and sentiment analysis or clustering reviews does not directly generate a reply to customer inquiries.

Answer: C. Build the chatbot as a multi-stage LLM pipeline: first classify the user's query type, then route it accordingly. Direct failure-rate questions to a text-to-SQL model that queries the failure table, and send troubleshooting queries to another model that summarizes the relevant manual and produces an answer.

A multi-stage pipeline classifies user intent and routes the request to the appropriate downstream handler, such as text-to-SQL for structured data or retrieval for manuals. Combining all data into a single format degrades accuracy by stripping away the native structure of relational tables.

Answer: A. Use windowed chunking with overlapping segments to maintain continuity across adjacent text.

Windowed chunking with overlapping segments maintains continuity across adjacent text boundaries, ensuring legal reasoning remains intact. Paragraph chunking lacks overlap causing context gaps, while sentence chunking is too granular, and hierarchical clustering disrupts chronological flow.

Answer: B. 1. Define or import the tools 2. Specify the agent configuration 3. Initialize the agent with ReAct logic, the LLM, and the tools

In LangGraph, the correct sequence is to define the tools, specify the agent configuration, and then initialize the agent with ReAct logic. Unlike classic LangChain, LangGraph requires defining the graph state and transitions before instantiating the actual agent.

Answer: A. The OpenAI client library

The OpenAI client library requires its own explicit API key and does not inherit Databricks notebook session credentials. For the exam, remember that native tools like the Databricks Python SDK and MLflow Deployments automatically authenticate using the active workspace session.

Answer: A. Measure the cosine similarity between the embeddings of retrieved items and those of a representative set of test queries

Cosine similarity measures the semantic closeness between query and retrieved item embeddings, directly evaluating retrieval accuracy. Avoid text generation metrics like BLEU or ROUGE, which measure n-gram overlap for translation or summarization tasks instead of vector distance.

Answer: B. Use Databricks access control lists (ACLs) to tightly manage which identities can interact with the model.

Applying access control lists directly restricts which identities can query the model endpoint, mitigating inversion risks. While guardrails are useful, they filter unsafe content rather than stopping adversarial probing, making strict endpoint access controls the strongest defense.

Answer: D. Review the model card and training data details to verify whether the model was trained on medical or biomedical corporathe model with the highest download count, assuming that popularity reflects broad effectiveness and model quality

Reviewing the model card and training data details confirms whether the model was exposed to biomedical corpora, which is critical for domain-specific accuracy. For the exam, always rely on documented training data over proxy metrics like popularity or recency when selecting models.

Answer: F. Using extremely large chunk sizes that stretch to the limits of the model's context window in hopes of maximizing text coverage

Using extremely large chunks degrades retrieval precision by packing unrelated information together, which increases noise and hallucinations. Metadata filtering, few-shot examples, and custom embedding fine-tuning are all effective strategies that meaningfully improve chatbot accuracy.