Databricks Generative AI Engineer Associate Practice Exam Questions and Answers – Part 8/9

Answer: D. Collect the policy documents → Create embeddings and store them in Vector Search → User sends a query to the LLM → LLM retrieves relevant documents → LLM generates an answer → Evaluate performance → Deploy using Model Serving

The correct workflow begins with collecting documents, creating embeddings, and storing them in Vector Search before users submit queries. Evaluation must occur before deployment to ensure the pipeline retrieves context accurately and generates grounded responses in production.

Answer: D. Apply rate-limiting controls directly on the Foundation Model endpoints.

Applying rate-limiting controls directly to the endpoint enforces hard infrastructure quotas, effectively preventing accidental traffic spikes. Distractors relying on prompt instructions or manual code reviews fail because they lack real-time, automated enforcement at the serving layer.

Answer: B. Add metadata fields for publication date and topic so the system can apply metadata filters during retrieval.

Adding structured metadata fields like publication date and topic allows the system to apply hard filters during vector retrieval. This is the most efficient approach because relying on semantic similarity alone cannot guarantee precise constraints like specific date ranges.

Answer: D. Deploy the endpoint with provisioned throughput to obtain predictable, guaranteed performance.

Provisioned throughput reserves dedicated compute capacity for a foundation model endpoint, guaranteeing predictable performance and avoiding noisy neighbor issues. For the exam, associate provisioned throughput with reliable production scaling rather than simply shrinking the model size.

Answer: A. Break the manuals into smaller text chunks and generate embeddings stored in a vector database. For each customer query, retrieve the most relevant chunks and have the LLM craft an answer using the retrieved content.

Breaking manuals into chunks and retrieving the most relevant sections forms the standard retrieval-augmented generation pipeline, ensuring accurate and efficient answers. Avoid options involving collaborative filtering or massive context windows, which are inefficient or lossy.

Answer: D. Add a safety-filtering layer that flags harmful text and instructs the model to decline those requests.

A dedicated safety-filtering layer intercepts and blocks harmful prompts before they reach the language model, preventing malicious inputs from generating unsafe outputs. Relying on the model to warn users leaves the system vulnerable, whereas compute scaling or time limits never address input toxicity.

Answer: B. Allow the system to ingest content only from vetted, trustworthy social media and news accounts so unexpected toxic material is excluded.

Curating data sources to ingest content only from trusted accounts prevents toxic material from entering the retrieval corpus, which keeps harmful context from influencing model outputs. Limiting retrieval count fails because any selected toxic chunk still poisons the generation phase.

Answer: A,B. The Big Code Models Leaderboard || The model's parameter count

The Big Code Models Leaderboard evaluates task-specific coding capabilities, making it essential for choosing a code generation base model. Parameter count dictates GPU memory requirements and directly impacts hosting costs, whereas MTEB only ranks embedding models.

Answer: A. Turn on MLflow tracing to obtain detailed visibility into the agent's execution flow and the behavior of each step.

MLflow tracing provides granular observability into agent execution flows, automatically capturing inputs, outputs, and tool calls for multi-step workflows. Structured logging lacks this deep automated introspection, while MLflow evaluate is meant for scoring model quality.

Answer: A. Introduce a reranker to reorder the retrieved documents based on their true relevance scores.

Introducing a reranker resolves this by using a cross-encoder to reorder the retrieved documents based on true semantic relevance. Relying on the LLM to reorder context is unreliable, whereas a dedicated reranking stage is the standard approach to fix imperfect embedding similarity.

Answer: D. whisper-large-v3 (1.6B)

Whisper-large-v3 is an open-source model specifically designed for fast and accurate automatic speech recognition. For the exam, remember that audio transcription requires specialized acoustic models like Whisper, whereas text-based models like DBRX cannot process audio natively.

Answer: A. Use only datasets that are clearly marked with an open license and carefully adhere to all associated licensing terms.

Using datasets with clear open licenses and strictly adhering to their terms creates a reliable chain of rights, reducing legal exposure. Treating public data as unrestricted is a major trap, as public accessibility does not eliminate copyright or usage constraints.

Answer: B. You will receive a customer call transcript involving a question about product availability. Output "Success" if the product is available and "Fail" if the product is unavailable.

Option B provides direct, deterministic instructions that align perfectly with the strict product availability classification task. Avoid options introducing unnecessary reasoning steps or complex formatting, as these distract the model and degrade classification consistency.

Answer: A,B. Vector Stores || Conversation Buffer Memory

Vector stores are essential for retrieving relevant knowledge, while conversation buffer memory maintains context across multiple chat turns. External tools and UI components might enhance an application, but they are not foundational requirements for a standard conversational RAG pipeline.