Databricks Generative AI Engineer Associate Practice Exam Questions and Answers – Part 7/9

Answer: B. Register the model directly in Unity Catalog using MLflow, then grant the development workspace READ permissions on that model.

Registering the model in Unity Catalog enables secure, cross-workspace sharing with fine-grained access controls without duplicating data. This approach avoids the storage and compute costs of manual exports or redundant training pipelines, aligning with standard Databricks governance practices.

Answer: A. Massive Multi-task Language Understanding (MMLU) benchmark score

Massive Multi-task Language Understanding, or MMLU, is an academic benchmark meant for testing broad foundation model capabilities rather than measuring live application success. For the exam, remember that production systems require operational metrics like accuracy, latency, and throughput, not generic research scores.

Answer: C. Retain all articles—including non-technical ones—because the system needs exposure to unrelated content in order to discourage off-topic answers

Retaining irrelevant documents pollutes the vector store and defeats the core purpose of retrieval-augmented generation by increasing noise and hallucination risk. A key exam cue is that curating and filtering the corpus before indexing is a fundamental best practice for domain-specific systems.

Answer: C. Build a dedicated evaluation dataset that independently tests retrieval effectiveness and generation quality, and use MLflow's evaluation features to assess each component separately.

Building a dedicated evaluation dataset and using MLflow allows engineers to assess retrieval and generation independently, isolating bottlenecks accurately. Using ROUGE or an LLM-as-a-judge only grades final text, hiding whether failures stem from poor context fetching or weak generation.

Answer: A. It enables complex tasks to be divided into a sequence of smaller, dependent steps, allowing the assistant to produce more thorough and accurate answers.

Prompt chaining divides complex tasks into smaller sequential steps, passing outputs from one stage as inputs to the next for improved accuracy. Chaining actually increases latency and token costs, making options claiming speed or cost reduction clearly incorrect.

Answer: C. Inference Tables

Inference Tables automatically log requests and responses for provisioned-throughput endpoints, providing the exact observability needed. Be careful not to confuse this with Vector Search, which handles embedding retrieval for RAG rather than tracking endpoint payloads.

Answer: B. Explode the chunk array so each row contains a single chunk, generate a unique ID for every row, and enable change data feed on the resulting Delta table.

Exploding the array ensures each chunk is an independent row, generating unique IDs creates a required primary key, and enabling change data feed allows efficient synchronization. Writing unchanged data fails because Vector Search requires a unique primary key for every indexable record.

Answer: C. Use a framework such as ReAct, enabling the LLM to create reasoning traces and invoke external tools through structured actions whenever required.

The ReAct framework enables an LLM to interleave reasoning traces with autonomous external tool actions, supporting dynamic planning and revision. Standard Chain-of-Thought prompting improves reasoning but requires manual intervention to supply tool outputs, preventing true autonomous execution.

Answer: A,B. Authenticate using an OAuth flow designed for machine-to-machine communication || Use an access token issued to a service principal

Using an access token issued to a service principal provides the non-interactive, machine-to-machine authentication required for secure production deployments. While machine-to-machine OAuth is conceptually similar, explicitly using a service principal ensures independent lifecycle management and strict least privilege.

Answer: A,D. Add the section header as a prefix to chunks || Increase the document chunk size

Adding section headers and increasing chunk size directly resolve incomplete retrieval by preserving semantic context within structured documents. Sentence splitting destroys context, and upgrading models cannot fix missing information reaching the response generator.

Answer: B. Deploy Llama Guard through the Foundation Model API and use it to screen outputs for safety issues

Deploying Llama Guard through the Foundation Model API provides a purpose-built moderation layer with minimal engineering overhead. Regex rules are too brittle for semantic safety, while custom LLM calls introduce unnecessary cost, latency, and complexity into the application flow.

Answer: A. BLEU

BLEU is the industry standard metric for evaluating machine translation quality by measuring n-gram overlap against high-quality reference translations. ROUGE is used for summarization, while NDCG and Recall apply to ranking and retrieval tasks rather than translation fidelity.

Answer: C. Use an embedding model that was trained on data similar to the application's domain

Selecting an embedding model trained on similar domain data ensures it accurately captures the specific semantics and vocabulary of your corpus. General leaderboard rankings or multilingual capabilities do not guarantee strong performance on your highly specialized internal documents.

Answer: D. beautifulsoup

BeautifulSoup is a Python library built specifically for parsing HTML and XML, extracting text with minimal code. Exam tip: pytesseract is for OCR on images, PyPDF2 is for PDF files, and NumPy handles numerical computing.