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Databricks Certified Generative AI Engineer Associate Syllabus

Design Applications

Translating requirements into prompts, chains, tools and agent designs.

14%
Weight
6
Questions
14
Marks

Prompt and Chain Design

  • Prompts that enforce a response format
  • Selecting model tasks for business needs
  • Choosing chain components for inputs and outputs
  • Mapping business goals to pipeline inputs and outputs

Agentic Design

  • Defining and ordering tools for multi-stage reasoning
  • When to use Agent Bricks (Knowledge Assistant, Multi-Agent Supervisor)
  • Agent Bricks Information Extraction
  • Translating requirements into agent tool calls

Data Preparation

Preparing, chunking and storing source content for retrieval.

14%
Weight
6
Questions
14
Marks

Chunking and Extraction

  • Chunking by document structure and model limits
  • Filtering extraneous content
  • Python packages for document extraction
  • Advanced chunking strategies

Retrieval Readiness

  • Writing chunks to Unity Catalog Delta tables
  • Selecting source documents for quality
  • Retrieval evaluation metrics
  • Role of re-ranking

Application Development

Building, guarding and selecting models and components for LLM apps.

30%
Weight
14
Questions
30
Marks

Prompting and Guardrails

  • Augmenting prompts with retrieved context
  • Shaping output with prompt design
  • Implementing LLM guardrails
  • Qualitative assessment of response quality and safety

Model Selection

  • Selecting an LLM for application attributes
  • Embedding model context length
  • Using model cards and hubs
  • Choosing models from experiment metrics

Agent Frameworks

  • LangChain and similar frameworks
  • MLflow and Mosaic AI Agent Framework
  • Multi-agent systems using Genie spaces
  • Evaluation vs. monitoring phases

Assembling and Deploying Applications

Packaging, registering, serving and integrating LLM applications.

22%
Weight
10
Questions
22
Marks

Packaging and Registration

  • pyfunc chains with pre- and post-processing
  • Model signatures and input examples
  • Registering models to Unity Catalog with MLflow
  • Prompt version control

Serving and Retrieval

  • Model Serving endpoints and resource access
  • Foundation Model APIs
  • Creating and querying Mosaic AI Vector Search indexes
  • Batch inference with ai_query()

Integration and CI/CD

  • Managed, external and custom MCP servers
  • Persistent memory datastores
  • CI/CD for indexes, prompts and components
  • User interfaces via Databricks Apps, Slack or Teams

Governance

Applying guardrails, masking and legal checks to GenAI data and apps.

8%
Weight
4
Questions
8
Marks

Guardrails

  • Masking as a guardrail
  • Guardrails against malicious user inputs
  • Prompt injection risks
  • Filtering unsafe outputs

Data and Legal Governance

  • Licensing requirements of data sources
  • Legal requirements for source data
  • Alternatives for problematic source text
  • Unity Catalog governance of GenAI assets

Evaluation and Monitoring

Measuring quality, cost and behaviour of deployed LLM apps.

12%
Weight
5
Questions
12
Marks

Evaluation

  • MLflow evaluation with scorers and tracing
  • Custom Scorers for agents and LLMs
  • Judges that require ground truth
  • Incorporating SME feedback

Monitoring

  • Inference tables and Agent Monitoring
  • AI Gateway usage tracking and rate limits
  • Key metrics for deployments
  • Controlling LLM costs

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Learning Progress

Design Applications 85%
Data Preparation 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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