Practice for the Databricks Certified Generative AI Engineer Associate exam with 14 exam-style practice questions, instant answer reveals, and concise explanations of every correct answer. Topics include: A Generative AI Engineer has successfully trained a custom LLM fine-tuned for customer support scenarios. They now need . Follow @CertPunch and visit certpunch.com for more certification practice exams and study content.
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What you will practice
- A Generative AI Engineer has successfully trained a custom LLM fine-tuned for customer support scenarios. The…
- A Generative AI Engineer is building a chain to take user input, format it to match a legacy system, pass it…
- A Generative AI Engineer is working with a government contractor to build a RAG-based assistant that referenc…
- A Generative AI Engineer has deployed a RAG application to help internal sales teams generate product recomme…
- A Generative AI Engineer is comparing two LLMs (Model A and Model B) to power a chatbot that assists in regul…
- Which library is the most appropriate for creating a multi-step workflow involving large language models (LLM…
Answers and explanations
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Q1. A Generative AI Engineer has successfully trained a custom LLM fine-tuned for customer support scenarios. They now need to deploy this model in Databricks with secure access, full lineage tracking, and versioning support. Which steps must…
Answer: C. C. Register the model to Unity Catalog using MLflow → Apply access controls → Deploy to Model Serving
Registering models in Unity Catalog using MLflow provides the necessary governance, versioning, and lineage tracking before deployment. Saving artifacts directly to DBFS circumvents these centralized security controls and lacks robust audibility.
Q2. A Generative AI Engineer is building a chain to take user input, format it to match a legacy system, pass it to an LLM, and then post-process the result before returning it to the application front end. The engineer wants to package this l…
Answer: A. A. Write a custom pyfunc model with pre- and post-processing logic and deploy via Model Serving
A custom pyfunc model packages the entire workflow, including pre-processing and post-processing, into a single, deployable artifact. Splitting logic into multiple scripts hosted externally removes the built-in dependency capture, versioning, and governance essential for production.
Q3. A Generative AI Engineer is working with a government contractor to build a RAG-based assistant that references classified policy documents. The system must prevent sensitive fields like citizen_id and medical_history from being exposed in…
Answer: B. B. Replace sensitive fields with consistent pseudo-identifiers before chunking to maintain referential meaning.
Replacing sensitive fields with consistent pseudo-identifiers preserves referential integrity while hiding raw values. Pure deletion breaks context and forces hallucinations, whereas metadata filters cannot redact exposed PII within retrieved text.
Q4. A Generative AI Engineer has deployed a RAG application to help internal sales teams generate product recommendation summaries for clients. Over time, users report that response quality has declined, and some outputs contain irrelevant pro…
Answer: B. B. Use MLflow to compare prompt variants and log evaluation scores; monitor inference tables for drift and retrieval accuracy.
Using MLflow to compare prompt variants and monitoring inference tables effectively tracks evaluation metrics and data drift. Token counts lack relevance context, and blind fine-tuning without logging hides pipeline failures.
Q5. A Generative AI Engineer is comparing two LLMs (Model A and Model B) to power a chatbot that assists in regulatory compliance Q&A. The team has created a dataset of prompts and expected responses with ground truth labels. The final model m…
Answer: C. C. Use quantitative evaluation metrics such as BLEU, ROUGE, and exact match against ground truth; combine this with latency and cost metrics.
Combining quantitative metrics against ground truth with latency and cost provides the necessary accuracy and budget balance. Subjective ratings or grammar checks ignore regulatory correctness, while choosing the cheapest model sacrifices quality.
Q6. Which library is the most appropriate for creating a multi-step workflow involving large language models (LLMs)?
Answer: D. D. LangChain
LangChain is specifically designed to orchestrate multi-step workflows and chain interactions with large language models. Pandas, TensorFlow, and PySpark handle data processing or model training rather than runtime LLM application orchestration.
Q7. When developing an LLM application, it is essential to ensure that the data used for training adheres to licensing rules to prevent legal issues. Which action is NOT a proper approach for avoiding legal risks?
Answer: D. D. Contact the data curators directly after you've already started using the trained model to inform them.
Informing data curators after already deploying a model violates proactive compliance and intellectual property guidelines. The strongest distractor, contacting them beforehand, fails because it correctly describes a necessary preventative measure.
Q8. A Generative AI Engineer is creating a chatbot for a gaming company, with the goal of enhancing user engagement on its platform while users play online video games. Which metric would be most beneficial in increasing user engagement and re…
Answer: B. B. Diversity of responses
Response diversity ensures chatbot interactions remain dynamic and engaging, which is critical for retaining users in gaming environments. Randomness lacks contextual control, while repetition creates a stale and predictable conversational experience.
Q9. A team intends to deploy a code generation model to assist their software developers, ensuring support for multiple programming languages. The primary focus is on maintaining high quality in the generated code. Which of the Databricks Foun…
Answer: D. D. CodeLlama-34B
CodeLlama is explicitly fine-tuned for code generation and understanding across multiple programming languages. General models like Llama or MPT lack this specialized training. Expect to choose domain-specific foundation models when the use case demands high accuracy for targeted tasks like coding.
Q10. A Generative AI Engineer is responsible for developing an application that utilizes an open-source large language model (LLM). They require a foundational LLM that offers a large context window. Which model would best meet this requirement?
Answer: D. D. DBRX
DBRX was designed with a massive context window to handle extensive prompts and long-form retrieval inputs. Older open-source models like Llama 2 have strictly limited context windows. Memorize the context limits of core Databricks models like DBRX to answer these requirements correctly.
Q11. A Generative AI Engineer is tasked with creating a solution where the user uploads resumes and receives job-fit summaries. The engineer wants the pipeline to extract job-relevant experience, match it with the job description, and then summ…
Answer: B. B. Resume Embedder → Retriever → LLM Summarizer
The standard Retrieval-Augmented Generation pattern requires embedding documents, retrieving relevant context via vector search, and summarizing with an LLM. Skipping the embedding step prevents accurate semantic matching. Memorize the core embedding, retrieval, and generation sequence for exam scenarios.
Q12. A Generative AI Engineer is developing a medical assistant chatbot to help doctors understand drug interactions. The model occasionally provides speculative or non-FDA-approved guidance in its responses. The engineer wants to minimize this…
Answer: D. D. Add post-processing logic to exclude any response containing unsupported phrases
Post-processing logic acts as an enforceable guardrail by inspecting the generated draft before it reaches the user. While metaprompts help guide behavior, they are advisory and lack the strict, auditable control needed to filter non-compliant medical advice.
Q13. A Generative AI Engineer is working on a news summarization RAG pipeline. The source documents are long, and users tend to ask high-detail queries. However, inference costs are a major concern. What should the engineer prioritize when sele…
Answer: B. B. Choose a model with a balance between longer context window and smaller embedding dimension
Balancing an adequate context window with a smaller embedding dimension controls both storage and inference costs in a RAG pipeline. Always picking the largest available context wastes money, as accuracy can actually drop past certain sequence lengths.
Q14. A Generative AI Engineer is tasked with building a RAG-based chatbot to help field agents query policy manuals. The application must be reproducible, support experimentation, and integrate well with Databricks-native infrastructure. The te…
Answer: B. B. Model flavor, embedding model, retriever, dependencies, model signature, and input examples
Packaging elements like the retriever and model signature inside an MLflow model ensures reproducibility and tracks lineage across Databricks environments. Basic pipeline components alone do not provide the governance and experiment tracking required for enterprise deployment.
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