Databricks Generative AI Engineer Associate Guided Course

This guided course is for learners who want a structured path through Databricks Certified Generative AI Engineer Associate Practice Test. Start with ordered lessons, answer exam-style questions, review explanations, and use Practice Mode afterward to test retention.

• Follow an ordered Databricks Generative AI Engineer Associate course path instead of starting with random questions.
• Use source-backed scenarios to connect concepts to practical administration decisions.
• Review why the correct answer fits and why the distractors do not.
• Track course progress on this device and return to the next lesson later.
• Move from guided learning into Practice Mode when you are ready to check retention and speed.

The full course continues in order. These early lessons are crawlable here so you can see the shape of the path before the interactive course loads.

1. Lesson 1: Select an embedding model context length based on source documents, queries, and optimization strategy
   Lesson 1 uses Select an embedding model context length based on source documents, queries, and optimization strategy to connect Application Development (30%) with Select an embedding model context length based on source documents, queries, and optimization strategy.
2. Lesson 2: Register the model to Unity Catalog using MLflow
   Lesson 2 uses Register the model to Unity Catalog using MLflow to connect Assembling and Deploying Applications (22%) with Register the model to Unity Catalog using MLflow.
3. Lesson 3: Select key metrics to monitor for a specific LLM deployment scenario
   Lesson 3 uses Select key metrics to monitor for a specific LLM deployment scenario to connect Evaluation and Monitoring (12%) with Select key metrics to monitor for a specific LLM deployment scenario.
4. Lesson 4: Code a simple chain according to requirements
   Lesson 4 uses Code a simple chain according to requirements to connect Assembling and Deploying Applications (22%) with Code a simple chain according to requirements.
5. Lesson 6: Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application
   Lesson 6 uses Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application to connect Governance (8%) with Recommend an alternative for problematic text mitigation in a data source feeding a GenAI application.
6. Lesson 7: Determine how and when to use Agent Bricks to solve problems
   Lesson 7 uses Determine how and when to use Agent Bricks to solve problems to connect Assembling and Deploying Applications (22%) with Explain key concepts and components of Databricks Vector Search / AI Search.
7. Lesson 8: Apply a chunking strategy for a given document structure and model constraints
   Lesson 8 uses Apply a chunking strategy for a given document structure and model constraints to connect Design Applications (14%) with Translate business use case goals into desired AI pipeline inputs and outputs.
8. Lesson 9: Configure a persistent datastore for intermediate memory or structured information
   Lesson 9 uses Configure a persistent datastore for intermediate memory or structured information to connect Assembling and Deploying Applications (22%) with Configure a persistent datastore for intermediate memory or structured information.
9. Lesson 10: Select the best LLM based on application attributes
   Lesson 10 uses Select the best LLM based on application attributes to connect Application Development (30%) with Select the best LLM based on application attributes.
10. Lesson 11: Identify how to serve an LLM application that leverages Foundation Model APIs
    Lesson 11 uses Identify how to serve an LLM application that leverages Foundation Model APIs to connect Assembling and Deploying Applications (22%) with Identify how to serve an LLM application that leverages Foundation Model APIs.