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 is developing a Retrieval-Augmented Generation (RAG) application that retrieves context from so. 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 is developing a Retrieval-Augmented Generation (RAG) application that retrieves cont…
- A Generative AI Engineer is responsible for deploying an application that utilizes a custom MLflow Pyfunc mod…
- A Generative AI Engineer is creating an LLM application that allows users to generate personalized birthday p…
- A Generative AI Engineer is developing a chatbot to help users with insurance-related questions. The chatbot…
- A Generative AI Engineer is developing a RAG (Retrieval-Augmented Generation) application to answer questions…
- A Generative AI Engineer is deploying a RAG application that answers user questions about product warranty po…
Answers and explanations
Tap a question to expand the answer and the exam reasoning. Try to commit to your own pick first.
Q1. A Generative AI Engineer is developing a Retrieval-Augmented Generation (RAG) application that retrieves context from source documents stored as image files (e.g., .jpeg, .png). To minimize coding effort, they want a Python package that ca…
Answer: C. C. pytesseract
The pytesseract library provides a simple Python wrapper for optical character recognition to extract text from image files. The other options handle HTML parsing or web scraping, making them completely ineffective for reading text embedded inside image formats.
Q2. A Generative AI Engineer is responsible for deploying an application that utilizes a custom MLflow Pyfunc model to return interim results. How should they set up the endpoint to securely pass secrets and credentials? A. Use spark.conf.set(…
Answer: C. C. Add credentials through environment variables.
Adding credentials through environment variables is correct because it securely externalizes secrets from the model code during endpoint deployment. As a practical exam cue, remember that hardcoding secrets or using Spark configurations exposes sensitive data, whereas environment variables safely integrate with Databricks secret management.
Q3. A Generative AI Engineer is creating an LLM application that allows users to generate personalized birthday poems based on their names. What technique would be most effective in protecting the application against potentially harmful user i…
Answer: A. A. Implement a safety filter that identifies harmful inputs and instruct the LLM to inform the user that it cannot assist.
Implementing a safety filter is correct because it actively intercepts and blocks harmful or inappropriate user inputs before the model processes them. As an exam cue, remember that allocating more compute or limiting interaction time does not provide content moderation or protect against malicious prompts.
Q4. A Generative AI Engineer is developing a chatbot to help users with insurance-related questions. The chatbot is conversational and powered by a large language model (LLM). However, to ensure the chatbot stays on topic and adheres to compan…
Answer: E. C. Contextual Guardrail
A contextual guardrail is the correct choice because it restricts the large language model to approved business topics, such as insurance, preventing off-topic drift. For the exam, remember that safety guardrails block harmful or toxic content, whereas contextual guardrails enforce business scope and relevance.
Q5. A Generative AI Engineer is developing a RAG (Retrieval-Augmented Generation) application to answer questions related to internal documents for the company SnoPen AI. However, the source documents may contain a considerable amount of irrel…
Answer: C. C. State in the system prompt that the application is not intended to answer questions unrelated to SnoPen AI.
Stating in the system prompt that the application is not intended to answer unrelated topics is correct because it explicitly sets the operational boundaries for the model. As a practical exam cue, remember that combining all documents into a single chunk severely degrades retrieval accuracy and increases token costs.
Q6. A Generative AI Engineer is deploying a RAG application that answers user questions about product warranty policies. The model is deployed using Databricks Model Serving and uses a pyfunc model with pre- and post-processing logic. Users re…
Answer: C. B. The pyfunc model was registered with only the base LLM and not the wrapped chain logic
Registering only the base LLM instead of the wrapped pyfunc object bypasses any custom predict methods. Remember that Databricks Model Serving executes the specific artifact registered, so unserialized preprocessing logic will be completely ignored at runtime.
Q7. A Generative AI Engineer is building a product recommendation assistant using a RAG pattern. The application is expected to retrieve embeddings from a large vector store of product specifications. To ensure scalability and low-latency infe…
Answer: D. C. Apply metadata filtering during query execution to narrow vector search scope before similarity ranking
Applying metadata filters during the query narrows the vector search scope before similarity ranking, reducing latency. Do not rely on the LLM to filter text, as it requires retrieving irrelevant vectors first, which degrades performance and increases token costs.
Q8. A Generative AI Engineer is tasked with enabling enterprise-wide access to a newly trained foundation model via Databricks Model Serving. The organization requires version control and lineage, fine-grained access control for teams (marketi…
Answer: H. C. Register the model to Unity Catalog under a shared catalog and enable model serving with access rules for each team
Registering the model in Unity Catalog provides native version control, lineage tracking, and auditable governance. Serving directly from DBFS or an external VM fails to provide the fine-grained access controls required across enterprise teams.
Q9. A Generative AI Engineer has completed development of a RAG-based assistant to support IT helpdesk agents. The assistant relies on a foundation model from a model hub, a domain-specific embedding model, a retriever connected to Mosaic AI V…
Answer: G. A. Deploy the chain using Databricks Model Serving with MLflow; enable inference logging and configure observability with Databricks metrics
Deploying the chain with Databricks Model Serving and MLflow enables scalable real-time REST endpoints for APIs. Enabling inference tables is your key exam cue for automatically tracking traffic, latency, and full user interactions in a governed Delta table.
Q10. A Generative AI Engineer is configuring a RAG-based chatbot to help employees understand internal security policies. The application uses a Vector Search index populated with policy documents, a foundation model accessed via Databricks Mod…
Answer: G. B. Recreate the vector index regularly with updated chunks from source documents
Recreating or syncing the vector index ensures the latest document chunks are embedded and available for retrieval. Prompting the model to ignore old policies fails because the retriever never surfaces the new chunks for the model to see.
Q11. A Generative AI Engineer is comparing two different RAG configurations for a legal assistant: Config A uses sentence-level chunking and cosine similarity, while Config B uses paragraph-level chunking with semantic re-ranking. The team has…
Answer: E. A. Use NDCG and Recall@k to assess retrieval accuracy across both configs, and complement with human judgment for output quality
Using retrieval metrics like NDCG and Recall provides an objective measure of how well each config surfaces the right context. Relying solely on token usage or chunk quantity ignores accuracy, which is critical for safe legal applications.
Q12. A Generative AI Engineer is building a healthcare-focused chatbot for patients. If a patient's inquiry is not an emergency, the chatbot should gather more information to relay to the doctor's office and recommend relevant pre-approved medi…
Answer: D. B. Please call your local emergency services.
Directing the user to call emergency services is correct because severe headaches and dizziness represent an urgent medical triage scenario requiring immediate human care. For the exam, always prioritize safety and escalation over standard information gathering when a user describes potentially severe medical symptoms.
Q13. What indicator should be taken into account when qualitatively assessing the safety of LLM outputs for a translation use case? A. The capability to produce responses in code. B. The degree of similarity to the original language. C. The res…
Answer: D. D. The accuracy and relevance of the generated responses.. The degree of similarity to the original language.
Assessing the accuracy and relevance of the generated responses is correct because safe translation requires preserving the original meaning without introducing harmful, biased, or misleading context. The distractor focusing on similarity to the original language fails because direct literal translation often misses cultural nuances.
Q14. A Generative AI Engineer is optimizing a customer support chatbot that uses a RAG architecture. Users frequently ask ambiguous questions like "Why was my order late?" or "How do I reset it?" which lack sufficient context. The engineer want…
Answer: A. What is the BEST strategy to implement this enrichment while preserving model accuracy and performance?
Injecting targeted structured metadata into the prompt supplies the grounding context needed to disambiguate vague queries. Option B fails because appending the entire user history inflates token costs and risks exposing irrelevant data.
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