Databricks Certified Generative AI Engineer Associate
Assesses an individual's ability to design and implement LLM-enabled solutions using Databricks. Covers problem decomposition, model and tool selection, and building comprehensive generative AI solutions. Evaluates Databricks-specific tooling including Vector Search for semantic similarity, Model Serving for deployment, MLflow for solution lifecycle management, and Unity Catalog for data governance. Candidates who pass can build and deploy performant RAG applications and LLM chains on Databricks. All machine learning code in this exam is in Python.
Aligned to official Databricks exam objectivesWritten by certified professionalsScenario-based difficulty mirrors the real exam
Design Applications(28)Data Preparation(28)Application Development(60)Assembling and Deploying Apps(44)Governance(16)Evaluation and Monitoring(24)
Question Sets
GAIEA Set 1
50 questions
Free
GAIEA Set 2
50 questions
Pro
GAIEA Set 3
50 questions
Pro
GAIEA Set 4
50 questions
Pro
Sample Questions
Q1.
easy
What is Retrieval Augmented Generation (RAG)?
Q2.
easy
An enterprise needs an LLM to answer questions about its large, frequently updated internal knowledge base. Which approach is most appropriate?
Q3.
medium
Which components are essential in a standard RAG pipeline?
Q4.
medium
An ML team is choosing between fine-tuning a foundation model and building a RAG solution to improve an LLM's responses about specialized medical terminology. Which consideration most strongly favors fine-tuning over RAG?
Q5.
medium
An LLM application must: (1) retrieve relevant documents, (2) summarize each document, and (3) generate a consolidated final answer. Which architecture pattern best decomposes this into manageable, independently testable components?
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