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

Answer: C. C. Exclude all datasets with restrictive licensing and replace them with internally generated content or data with clear commercial use rights.

Excluding restrictively licensed datasets and using content with clear commercial rights eliminates infringement risk. Paraphrasing with an LLM or merely adding disclosures does not cure upstream licensing violations.

Answer: B. B. Store chunked text in a Delta table within a Unity Catalog volume, applying appropriate schema and permissions

Storing chunked text in a Delta table within Unity Catalog directly enables centralized governance, column-level lineage tracking, and fine-grained access control. Databricks-specific exam cues favor Delta and Unity Catalog over unmanaged file systems because they integrate seamlessly with downstream vector search.

Answer: B. B. Create a chain with: text cleaner → sentiment classifier → toxicity detector → summarizer

Building a sequential chain with dedicated text cleaning, sentiment, toxicity, and summarization components provides the necessary modularity and auditability. Exam cues often favor modular pipelines over single prompts because they allow independent logging and transparent safety guardrails.

Answer: C. C. LangChain

LangChain provides the necessary abstractions for building a RAG pipeline, including document loaders, text splitters, and wrappers for external APIs. The other tools focus on distributed training, traditional machine learning, or basic NLP preprocessing rather than end-to-end retrieval chaining.

Answer: C. C. Reconfigure the retriever using cosine similarity and ensure metadata filtering is enabled

Reconfiguring the retriever and applying metadata filtering directly improves search precision without requiring a costly system rebuild. However, modifying chunk size is also a standard Databricks tuning step, making the options slightly ambiguous for practical RAG optimization.

Answer: H. C. Prompt the LLM with multiple documents at once and let it infer jurisdictional compliance from scratch.

Using a sequential chain of discrete tools provides modularity, testability, and reusable components for each pipeline stage. Summarizing before extracting entities destroys intermediate reasoning steps, and single prompts lack traceability.

Answer: G. B. Input: Raw document text; Output: Structured JSON with extracted clauses, missing clause types, and recommended additions

Mapping raw text to a structured JSON object cleanly supports extraction, gap analysis, and recommendations. Plain English summaries lose structured clause data, and starting from metadata skips necessary extraction steps.

Answer: C. B. Chunk based on document headings and sections to preserve semantic boundaries, then apply re-ranking after retrieval

Chunking by headings preserves semantic boundaries, and adding a re-ranking step optimizes relevance. Fixed-length windows add latency, and discarding short sections risks losing critical risk factor data.

Answer: B. B. Add a metaprompt instructing the model: "Only generate code that uses standard Python libraries and has been tested"

Adding explicit metaprompt instructions constrains the model to standard libraries, directly mitigating package hallucinations while preserving overall flexibility. Avoid extreme temperature limits or rigid deterministic rules because they destroy the creative problem-solving utility expected from large language models.

Answer: D. C. Format the prompt to say: "Respond with a JSON object using fields: summary, action, confidence_score. Do not use natural language outside the JSON."

Formatting the prompt to explicitly request a JSON object with specific fields reliably enforces machine-readable outputs. Temperature zero reduces randomness but does not guarantee structure, whereas few-shot examples merely nudge style.

Answer: E. D. Prompt the LLM with raw logs and ask it to identify and escalate the most severe events in one response

Using modular tools provides structured, traceable multi-stage reasoning with clear evaluation hooks. Single prompts lack traceability, and manual escalation fails to meet the need for automated, multi-criteria evaluation.

Answer: B. A. Use pytesseract to extract text, followed by post-processing to clean noise

Using an established OCR library minimizes development time and integrates easily with Python pipelines. HTML parsers cannot read image pixels, and training a custom model introduces unnecessary complexity and cost.

Answer: B,D. A. Apply dense passage retrieval (DPR) with shorter chunk lengths and compare Recall@k || C. Use a test set of known queries and expected chunks to compute NDCG and precision

The correct actions use dense passage retrieval with shorter chunks and validate improvements using labeled test sets. For the exam, pair structural changes like chunk size with objective offline metrics like NDCG rather than subjective checks.

Answer: B. B. Choose a smaller language model that is specifically trained for the cancer research domain.

Choosing a smaller, domain-specific language model optimizes the balance of inference cost and task-specific accuracy. For the exam, remember that restricting document access or user queries harms the application's utility and does not scale appropriately.