The Databricks Certified Generative AI Engineer Associate exam validates your ability to design, build, and deploy generative AI applications on the Databricks platform. This credential is intended for engineers who work with large language models, prompt engineering, and AI workflows in production environments. This page outlines the exam structure, core topics, and effective study strategies to help you prepare confidently. Whether you're new to generative AI or deepening your Databricks expertise, understanding the syllabus and question formats is essential for success.
Use this topic map to guide your study for the Databricks Certified Generative AI Engineer Associate certification within the Generative AI Engineer Associate path.
The exam uses multiple question types to measure both conceptual knowledge and practical decision-making in real-world generative AI scenarios.
Questions increase in complexity and require you to connect concepts across design, development, and operations phases of real projects.
Effective preparation requires mapping the six core topics to a structured study schedule and practicing with realistic scenarios. Dedicate time to each domain while identifying connections between data preparation, application development, and production monitoring. A balanced approach combining theory review, hands-on labs, and practice questions will build confidence and competency.
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Application Development and Assembling and Deploying Applications typically demand the most practical experience. Spend time building small generative AI projects on Databricks, experimenting with prompt engineering, and deploying models to serving endpoints. Hands-on labs will reinforce concepts and improve your confidence in real-world scenarios.
Data quality directly affects model performance and production monitoring outcomes. Questions often test your understanding of how preparation choices (tokenization, embedding strategies) influence model outputs, and how monitoring metrics reveal data quality issues. Study the feedback loop between these domains to answer integrated scenario questions effectively.
Candidates often confuse when to use different deployment patterns, overlook governance requirements in application design, and underestimate the importance of monitoring in production. Another frequent error is not reading scenario details carefully, leading to incorrect decisions about infrastructure or data pipeline choices. Review practice test explanations closely to avoid these pitfalls.
Read the scenario carefully to identify the business requirement, constraints, and current state. Eliminate obviously incorrect options first, then evaluate remaining choices against best practices for design, performance, and governance. Consider trade-offs (cost vs. latency, flexibility vs. compliance) and select the option that best balances the stated priorities.
Focus on weak topic areas identified in practice tests rather than re-reading all material. Review scenario-based questions from each domain, paying attention to decision points and trade-offs. Take one final timed practice test under exam conditions, then spend your remaining time reviewing explanations for questions you missed or found difficult.
A Generative Al Engineer is responsible for developing a chatbot to enable their company's internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:
call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives' call resolution from fields call_duration and call start_time.
transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.
call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.
call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.
maintenance_schedule -- a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.
They need sources that could add context to best identify ticket root cause and resolution.
Which TWO sources do that? (Choose two.)
In the context of developing a chatbot for a company's internal HelpDesk Call Center, the key is to select data sources that provide the most contextual and detailed information about the issues being addressed. This includes identifying the root cause and suggesting resolutions. The two most appropriate sources from the list are:
Call Detail (Option D):
Contents: This Delta table includes a snapshot of all call details updated hourly, featuring essential fields like root_cause and resolution.
Relevance: The inclusion of root_cause and resolution fields makes this source particularly valuable, as it directly contains the information necessary to understand and resolve the issues discussed in the calls. Even if some records are incomplete, the data provided is crucial for a chatbot aimed at speeding up resolution identification.
Transcript Volume (Option E):
Contents: This Unity Catalog Volume contains recordings in .wav format and text transcripts in .txt files.
Relevance: The text transcripts of call recordings can provide in-depth context that the chatbot can analyze to understand the nuances of each issue. The chatbot can use natural language processing techniques to extract themes, identify problems, and suggest resolutions based on previous similar interactions documented in the transcripts.
Why Other Options Are Less Suitable:
A (Call Cust History): While it provides insights into customer interactions with the HelpDesk, it focuses more on the usage metrics rather than the content of the calls or the issues discussed.
B (Maintenance Schedule): This data is useful for understanding when services may not be available but does not contribute directly to resolving user issues or identifying root causes.
C (Call Rep History): Though it offers data on call durations and start times, which could help in assessing performance, it lacks direct information on the issues being resolved.
Therefore, Call Detail and Transcript Volume are the most relevant data sources for a chatbot designed to assist with identifying and resolving issues in a HelpDesk Call Center setting, as they provide direct and contextual information related to customer issues.
A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?
The task involves improving a Retrieval-Augmented Generation (RAG) application's performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let's evaluate the options based on Databricks Generative AI Engineer best practices.
Option A: Pick an embedding model trained on related domain knowledge
Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application's context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.
Databricks Reference: 'For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data' ('Building LLM Applications with Databricks,' 2023).
Option B: Pick the most recent and most performant open LLM released at the time
LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn't address the embedding step in RAG. This option misunderstands the component being selected.
Databricks Reference: Embedding models and LLMs are distinct in RAG workflows: 'Embedding models convert text to vectors, while LLMs generate responses' ('Generative AI Cookbook').
Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace
The MTEB leaderboard ranks models across general tasks, but high overall performance doesn't guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer's unique data.
Databricks Reference: General performance is less critical than domain fit: 'Benchmark rankings provide a starting point, but domain-specific evaluation is recommended' ('Databricks Generative AI Engineer Guide').
Option D: Pick an embedding model with multilingual support to support potential multilingual user questions
Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.
Databricks Reference: 'Choose features like multilingual support based on application requirements' ('Building LLM-Powered Applications').
Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system---aligning with Databricks' emphasis on tailoring models to specific use cases.
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy. Here's why B is the best choice:
Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.
Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.
Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low. Large, general-purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.
This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.
A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user's query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)
To optimize a chunking strategy for a Retrieval-Augmented Generation (RAG) application, the Generative AI Engineer needs a structured approach to evaluating the chunking strategy, ensuring that the chosen configuration retrieves the most relevant information and leads to accurate and coherent LLM responses. Here's why C and E are the correct strategies:
Strategy C: Evaluation Metrics (Recall, NDCG)
Define an evaluation metric: Common evaluation metrics such as recall, precision, or NDCG (Normalized Discounted Cumulative Gain) measure how well the retrieved chunks match the user's query and the expected response.
Recall measures the proportion of relevant information retrieved.
NDCG is often used when you want to account for both the relevance of retrieved chunks and the ranking or order in which they are retrieved.
Experiment with chunking strategies: Adjusting chunking strategies based on text structure (e.g., splitting by paragraph, chapter, or a fixed number of tokens) allows the engineer to experiment with various ways of slicing the text. Some chunks may better align with the user's query than others.
Evaluate performance: By using recall or NDCG, the engineer can methodically test various chunking strategies to identify which one yields the highest performance. This ensures that the chunking method provides the most relevant information when embedding and retrieving data from the vector store.
Strategy E: LLM-as-a-Judge Metric
Use the LLM as an evaluator: After retrieving chunks, the LLM can be used to evaluate the quality of answers based on the chunks provided. This could be framed as a 'judge' function, where the LLM compares how well a given chunk answers previous user queries.
Optimize based on the LLM's judgment: By having the LLM assess previous answers and rate their relevance and accuracy, the engineer can collect feedback on how well different chunking configurations perform in real-world scenarios.
This metric could be a qualitative judgment on how closely the retrieved information matches the user's intent.
Tune chunking parameters: Based on the LLM's judgment, the engineer can adjust the chunk size or structure to better align with the LLM's responses, optimizing retrieval for future queries.
By combining these two approaches, the engineer ensures that the chunking strategy is systematically evaluated using both quantitative (recall/NDCG) and qualitative (LLM judgment) methods. This balanced optimization process results in improved retrieval relevance and, consequently, better response generation by the LLM.
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?
Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
Explanation of Options:
Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
Option B is ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.
