Databricks Generative AI Engineer Associate — Study Notes

Study notes and reference material for the Databricks Generative AI Engineer Associate certification.

What's in this repo

Content was generated using Exa and Context7 against the Databricks partner academy learning plan above.

File	Description
1_fundamentals.md	Generative AI fundamentals
2_retrieval_agents.md	Retrieval and agents
3_single_agent_applications.md	Single-agent application patterns
4_eval_govern.md	Evaluation and governance
5_deploy_monitor.md	Deployment and monitoring
exam_sections.md	Exam domain breakdown with weightings
study_cheat_sheet.md	Quick-reference cheat sheet
study_flashcards.md	Flashcards for key concepts
study_practice_questions.md	Practice exam questions
study_topic_summaries.md	Topic summaries

Exam domains

Domain	Weight
App Development	30%
Assembling and Deploying Applications	22%
Design Applications	14%
Data Preparation	14%
(remaining domains)	20%

Updating this content

If the learning plan changes, regenerate notes by re-running Exa + Context7 against the partner academy URL:

https://partner-academy.databricks.com/learn/learning-plans/315/generative-ai-engineering-pathway

Step 1 — Pull fresh content with Exa

Install the SDK and run the script below to fetch up-to-date content from the learning plan page.

pip install exa-py
export EXA_API_KEY="your_key_here"

from exa_py import Exa

exa = Exa(api_key=os.environ["EXA_API_KEY"])

# Fetch the current module content directly from the learning plan URL
results = exa.get_contents(
    ["https://partner-academy.databricks.com/learn/learning-plans/315/generative-ai-engineering-pathway"],
    text={"max_characters": 20000},
    # maxAgeHours=0 forces a live crawl so you always get the latest version
    max_age_hours=0,
)

print(results.results[0].text)

If you want to also search for supplementary Databricks GenAI content across the web:

results = exa.search(
    "Databricks Generative AI Engineer Associate exam topics 2025",
    type="deep",
    num_results=10,
    contents={"highlights": True},
)

for r in results.results:
    print(r.title, r.url)
    print(r.highlights)

Step 2 — Supplement with Context7

In Claude Code, use the Context7 MCP tool to pull documentation for specific Databricks/MLflow APIs referenced in the notes (e.g. mlflow, databricks-sdk). This fills in implementation details that Exa's web crawl may not surface.

Step 3 — Refresh the markdown files

Paste the Exa/Context7 output into Claude Code and ask it to update the relevant section files. The files map to the learning plan modules:

File	Module
1_fundamentals.md	Generative AI Fundamentals
2_retrieval_agents.md	Retrieval & Agents
3_single_agent_applications.md	Single-Agent Applications
4_eval_govern.md	Evaluation & Governance
5_deploy_monitor.md	Deployment & Monitoring

Update the section files first, then regenerate the derived study files last:

• study_cheat_sheet.md
• study_flashcards.md
• study_practice_questions.md
• study_topic_summaries.md