Use this quick start guide to collect all the information about Databricks Generative AI Engineer Associate Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the Databricks Certified Generative AI Engineer Associate exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Databricks Certified Generative AI Engineer Associate certification exam.
The Databricks Generative AI Engineer Associate certification is mainly targeted to those candidates who want to build their career in Generative AI Engineer domain. The Databricks Certified Generative AI Engineer Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Databricks Generative AI Engineer Associate.
Databricks Generative AI Engineer Associate Exam Summary:
| Exam Name | Databricks Certified Generative AI Engineer Associate |
| Exam Code | Generative AI Engineer Associate |
| Exam Price | $200 (USD) |
| Duration | 90 mins |
| Number of Questions | 45 |
| Passing Score | 70% |
| Books / Training | Generative AI Engineering With Databricks |
| Schedule Exam | Databricks Webassesor |
| Sample Questions | Databricks Generative AI Engineer Associate Sample Questions |
| Practice Exam | Databricks Generative AI Engineer Associate Certification Practice Exam |
Databricks Generative AI Engineer Associate Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Design Applications |
- Design a prompt that elicits a specifically formatted response - Select model tasks to accomplish a given business requirement - Select chain components for a desired model input and output - Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline - Define and order tools that gather knowledge or take actions for multi-stage reasoning - Determine how and when to use Agent Bricks (Knowledge Assistant, Multiagent Supervisor, Information Extraction) to solve problems |
14% |
| Data Preparation |
- Apply a chunking strategy for a given document structure and model constraints - Filter extraneous content in source documents that degrades quality of a RAG application - Choose the appropriate Python package to extract document content from provided source data and format. - Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog - Identify needed source documents that provide necessary knowledge and quality for a given RAG application - Use tools and metrics to evaluate retrieval performance - Design retrieval systems using advanced chunking strategies - Explain the role of re-ranking in the information retrieval process |
14% |
| Application Development |
- Select Langchain/similar tools for use in a Generative AI application. - Qualitatively assess responses to identify common issues such as quality and safety - Select chunking strategy based on model & retrieval evaluation - Augment a prompt with additional context from a user's input based on key fields, terms, and intents - Create a prompt that adjusts an LLM's response from a baseline to a desired output - Implement LLM guardrails to prevent negative outcomes - Select the best LLM based on the attributes of the application to be developed - Select an embedding model context length based on source documents, expected queries, and optimization strategy - Select a model from a model hub or marketplace for a task based on model metadata/model cards - Select the best model for a given task based on common metrics generated in experiments - Utilize MLflow and Agent Framework for developing agentic systems - Compare the evaluation and monitoring phases of the Gen AI application life cycle - Enable multi-agent systems to leverage Genie Spaces or conversational API to retrieve data |
30% |
| Assembling and Deploying Applications |
- Code a chain using a pyfunc model with pre- and post-processing - Control access to resources from model serving endpoints - Code a simple chain according to requirements - Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature - Register the model to Unity Catalog using MLflow - Create and query a Vector Search index - Identify how to serve an LLM application that leverages Foundation Model APIs - Explain the key concepts and components of Mosaic AI Vector Search - Identify batch inference workloads and apply ai_query() appropriately - Configure vector search for a particular solution based on number of embeddings, update frequency, latency, and cost requirements. - Configure a persistent datastore to store and retrieve intermediate memory or structured information. - Apply CI/CD best practices such as updating a Vector Search index, promoting prompts across environments, and testing individual components of an agent. - Integrate managed, external, and custom MCP servers based on a given application requirements - Apply prompt version control and manage prompt lifecycle - Develop an appropriate interactive user facing interface for an agent usage scenario (Apps, Slack, Teams, etc.) |
22% |
| Governance |
- Use masking techniques as guard rails to meet a performance objective - Select guardrail techniques to protect against malicious user inputs to a Gen AI application - Use legal/licensing requirements for data sources to avoid legal risk - Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application |
8% |
| Evaluation and Monitoring |
- Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics - Select key metrics to monitor for a specific LLM deployment scenario - Evaluate agent performance using MLflow scoring and tracing - Use inference logging to assess deployed RAG application performance - Use Databricks features to control LLM costs - Use inference tables and Agent Monitoring to track a live LLM endpoint - Identify evaluation judges that require ground truth - Use AI Gateway (Inference Tables, Usage Tables, and rate limiting) to track an LLM or agent deployed via Agent Framework. - Use Databricks custom Scorers for evaluating agents and LLMs - Incorporate SME feedback to improve agent performance |
12% |
To ensure success in Databricks Generative AI Engineer Associate certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Databricks Certified Generative AI Engineer Associate exam.
