Databricks Certified Generative AI Engineer Associate Practice Exam Questions and – Part 1/3

Answer: D. D. Provide few-shot examples to the model or adjust the user prompt to guide the system toward the desired output format.

Providing few-shot examples or adjusting the user prompt directly shapes the output format and removes unwanted meta-text. Modifying chunk sizes or ingestion pipelines serves retrieval relevance, which fails to address the generation behavior itself.

Answer: B. B. Fine-tune the model on your data, hoping it will automatically learn to avoid inappropriate outputs.

Simply fine-tuning a model on internal data does not guarantee it will prevent hallucinations or automatically enforce data access restrictions. Guardrails, strict system prompts, and explicit permission-based data retrieval are required for security.

Answer: D. D. You will be given a transcript of a customer call where the customer asks about product availability. Respond with "In Stock" if the product is available or "Out of Stock" if it's unavailable.

The correct prompt clearly defines the decision rule based on availability and constrains the output to the required labels. The strongest distractor adds unnecessary JSON formatting and fields, which introduces complexity and potential hallucination errors.

Answer: A. A. The system should include a prompt, a retriever, and an LLM. The retriever's output is inserted into the prompt, which is then passed to the LLM to generate answers.

A Retrieval-Augmented Generation pipeline dynamically accesses newly published documents, avoiding the high costs of continuous model retraining. Relying solely on prompt engineering introduces stale knowledge, while utilizing agents introduces unnecessary architectural complexity.

Answer: B. B. Create a system prompt for the agent listing the available tools, and implement an agent system that runs different calls to handle the queries.

Defining available tools within the system prompt enables the agent to dynamically interact with external APIs and structured tables. Static architectures like basic Retrieval-Augmented Generation lack the operational flexibility required to trigger real-time external lookups.

Answer: C. C. Unstructured

The Unstructured library is purpose-built to extract and clean text from complex file formats like PDFs using minimal code. Standard libraries like BeautifulSoup only parse HTML or XML, making them unsuitable for raw document extraction. Rely on Unstructured for efficient data preparation.

Answer: C. C. The chatbot should be designed as a multi-step LLM workflow. First, it should identify the type of question being asked, then route the query to the appropriate model. For questions about upcoming events, the query should be directed to a text-to-SQL model, while ticket purchasing inquiries should redirect the user to a payment platform.

Building a multi-step workflow with an initial intent classification step allows the system to route queries to specialized tools or models. Handling everything in one prompt or splitting into multiple chatbots creates unnecessary complexity. Look for options that describe query routing or agentic workflows.

Answer: C. C. "Given the complaint, respond in the following JSON format: { "issue_summary": …, "suggested_action": …, "urgency_level": … }."

Providing an explicit JSON schema directly in the prompt forces the model to adhere to the required structure and field names. Simple conversational prompts fail to enforce machine-readable formats reliably. Always use clear formatting instructions when structured outputs are needed.

Answer: C. C. Input: production logs; Output: failure reason and recommended fix

Defining inputs as production logs and outputs as generated diagnoses directly aligns with the stated application requirements. Using historical tickets or database tables reverses the data flow. For exam questions mapping pipelines, strictly match the data sources and desired business outcomes.

Answer: C. C. Pre-process documents to remove disclaimers, footers, and legal sections before chunking.

Pre-processing documents to remove irrelevant noise before chunking ensures the vector database contains only meaningful context. Increasing chunk overlap or expanding context windows simply adds more noise to the prompt. Cleaning raw data is always the first step for reliable retrieval.

Answer: C. C. Use Spark DataFrame API to write as Delta → Save to managed Unity Catalog volume → Register table in appropriate schema

Using Spark to write chunked data into a managed Unity Catalog Delta table provides ACID compliance and centralized security. Saving files as CSV or Parquet bypasses governance features and lacks time travel. Rely on managed Delta tables for secure RAG infrastructure.

Answer: A,E. A. Use evaluation metrics like Mean Reciprocal Rank (MRR) or NDCG to assess ranking quality || E. Build a test set of query-answer pairs and compute precision/recall across retrieval strategies

Building a labeled test set and calculating standard metrics like precision provides an objective baseline for comparing retrieval strategies. Rank-aware metrics like Mean Reciprocal Rank measure how well the system orders relevant results. Avoid subjective or isolated component metrics for holistic evaluation.

Answer: B. B. Inject the metadata fields into the prompt as system context before the user's input

Injecting metadata directly into the system context grounds the model in the specific user profile before it generates a response. Appending metadata as a footnote happens after generation, meaning it cannot influence the highly personalized output you need.

Answer: B. B. Modify the prompt to instruct: "Respond in one sentence. Be direct and actionable."

Explicit prompt instructions directly steer the style and length of the output without requiring any changes to the underlying model. Raising temperature increases randomness and often makes responses longer, while hard token limits risk truncating important information mid-sentence.

Answer: B. B. A summarization-capable model with support for structured output

A summarization-capable model with structured output support reliably extracts fields like issue type directly from text transcripts. A speech-to-text model is the wrong choice because the audio transcription step is already completed.