Databricks Generative AI Engineer Associate Practice Exam Questions and Answers – Part 5/6

Answer: A. A. Context length 512: smallest model size is 0.13 GB with embedding dimension 384

Choosing the 512 context length model with the smallest footprint keeps latency and compute costs minimized for chunked retrieval. The other options waste memory and compute by providing unnecessary context limits and larger embedding dimensions for the given chunk size.

Answer: D. D. Create a tool that finds available team members for the given project dates. Embed team member profiles in a vector store and use the project description to search and filter for the best-matched team members who are available.

Option D is correct because embedding employee profiles into a vector store and filtering by availability combines structured metadata filtering with unstructured semantic search. This approach efficiently scales compared to keyword matching or brute-force similarity scoring. Remember that Vector Search natively handles hybrid queries.

Answer: D. D. Carefully curate the upstream data, including performing manual reviews before incorporating it into the RAG system.

Option D is correct because carefully curating upstream data prevents offensive or inflammatory content from ever entering the retrieval pipeline. Simply refreshing data or notifying users does not actively reduce harmful outputs at the source. Remember that strong guardrails rely heavily on data quality and curation.

Answer: D. D. Context length of 512; the smallest model size is 0.13GB, with an embedding dimension of 384.

Option D is correct because choosing the smallest model size and matching context length directly minimizes compute cost and latency. Since the application prioritizes speed over quality, larger models and context windows introduce unnecessary computational overhead. Always align resource selection with strict application constraints.

Answer: A. A. Evaluate the quality of the context being retrieved.

Option A is correct because evaluating retrieved context isolates the root cause of irrelevant responses before modifying the language model. If the system retrieves the wrong documents, even the best model will fail to answer correctly. Always debug the retrieval step first when facing relevance issues.

Answer: D. D. Create an MLflow PyFunc model that includes a separate function for processing the prompts.

Creating an MLflow PyFunc model is the correct choice because it modularizes and wraps your custom preprocessing logic seamlessly for model serving. For the exam, avoid options suggesting internal model architecture changes, as large language models are treated as fixed inference endpoints.

Answer: B. B. Enhance the computing resources to boost the processing speed of questions, allowing for better relevance analysis.

Enhancing computing resources is the correct answer because increasing processing speed or throughput does not inherently improve the topical relevance of the generated outputs. Relevance is achieved through domain guardrails and manual reviews, whereas compute simply makes existing pipelines run faster.

Answer: B. B. Flatten the dataframe so that each chunk is in its own row, create a unique identifier for each row, and save it to a Delta table.

Flattening the dataframe so each chunk is an independent row is correct because vector search indexes require granular text segments to perform accurate similarity searches. Avoid storing data as independent JSON files, as Delta tables provide the necessary querying, indexing, and scalability foundation.

Answer: B. B. Use a universal multilingual embedding model and apply metadata-based filtering during retrieval to select language-matched chunks

A universal multilingual embedding model maps different languages into a shared space, while metadata filters restrict retrieval to the correct language. For the exam, remember that engine-level metadata filtering in Vector Search prevents irrelevant chunks from polluting the context window.

Answer: B. B. Use Databricks ai_query() within a PySpark job to apply the LLM across rows in parallel and log inference results

Using the ai_query function within a PySpark job applies the LLM across rows in parallel, scaling effortlessly for batch workloads. Synchronous REST calls via Model Serving are meant for real-time requests and lack inherent distributed batch parallelism.

Answer: B. B. Use masking techniques during chunking to redact PII and enforce license-aware filtering during retrieval

Masking PII during chunking and enforcing license-aware filtering during retrieval guarantees sensitive data never reaches the model. Relying on prompt instructions is a soft guardrail, whereas engine-level metadata filters provide a hard, reliable boundary.

Answer: B. B. Apply content filtering during retrieval to exclude toxic language before passing chunks to the model

Applying content filtering during retrieval excludes toxic language before the model receives the context, preserving safe, useful data. Removing all chat logs sacrifices valuable coverage, while relying on the foundation model risks quoting toxic text verbatim.

Answer: A. A. Track latency by language using inference tables and set hard limits on total token count per request

Tracking latency by language using inference tables and setting hard limits on total token count per request addresses both issues. For the exam, remember that Databricks inference tables provide granular telemetry for performance debugging, while max_tokens limits directly control generative costs.

Answer: A,B. A. Increase the document chunk size || B. Reduce the overlap between chunks

Increasing the document chunk size and reducing the overlap between chunks both directly reduce the total number of generated vectors. Remember that switching to a smaller embedding model only reduces vector dimensionality and memory footprint, not the actual embedding count.