Applied AI cert-prep add-on

Databricks Generative AI Engineer Associate Exam Prep Add-On

Convert AI Engineering Mastery into a Databricks GenAI Engineer ramp aligned to Mosaic AI tooling.

$147 add-on50h additional studyTarget exam: Databricks Certified Generative AI Engineer AssociateExam fee: $200

What the add-on ships

++Domain reviews aligned to the official Databricks GenAI Engineer Associate exam guide
++Three full-length mock exams matching Databricks's question format and difficulty
++Mosaic AI lab walkthroughs covering vector search, model serving, and AI agents on Databricks
++Lakehouse-specific RAG design patterns and Unity Catalog governance for AI workloads
++Direct link to the official Databricks certification page as the canonical reference

Built on the parent course

++AI Engineering Mastery 18-module curriculum covering RAG, agents, evals, observability
++Vector database, retrieval, and serving patterns from the parent course that map directly to Databricks scenarios

Parent course: ai engineering mastery

Best for

++AI Engineers building production GenAI features on the Databricks Lakehouse
++Data Engineers extending an existing Databricks practice into generative AI
++ML Engineers cross-training onto the Databricks platform

Practice surface

++Three full-length mock exams in Databricks's question format
++Mosaic AI lab walkthroughs runnable in a Databricks Community Edition workspace
++Targeted weak-area question drilling per Databricks exam objective

Buy the add-on

$147 on top of the ai engineering mastery parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

Databricks Certified Generative AI Engineer Associate covers building and deploying generative-AI solutions on Databricks. Four sections: design, building / evaluating, deployment / operations, and governance. 45 questions in 90 minutes.

Domain reviews

Generative AI Application Design

Architectural patterns: RAG, prompt engineering, agentic workflows on Databricks.

++RAG design on Databricks: Vector Search, embedding models, chunking
++Prompt engineering patterns: zero-shot, few-shot, chain-of-thought, structured output
++Agent design with Mosaic AI Agent Framework
++Choosing models: foundation-model APIs vs custom fine-tuned models, Mosaic AI Model Serving
++Data design: Unity Catalog volumes, Delta tables, vector indexes

Primary sources:

Data Preparation and Vector Search

Preparing and indexing data for GenAI use.

++Document loading and chunking strategies
++Embedding model selection (BGE, Instructor, OpenAI, etc.)
++Databricks Vector Search: managed and self-hosted endpoints
++Sync from Delta tables to vector indexes
++Hybrid search: dense vector plus keyword

Primary sources:

Databricks Vector Search docs

Model Selection, Fine-Tuning, and Evaluation

Choosing and customizing models on Databricks.

++Mosaic AI Model Training for fine-tuning
++Foundation-Model APIs (Llama, DBRX, Mixtral, etc.) for pay-per-token inference
++Mosaic AI Model Serving: serverless, provisioned throughput, multi-model
++Evaluation: Mosaic AI Agent Evaluation, custom eval datasets, MLflow LLM evaluation
++Cost considerations: token cost, latency, throughput tradeoffs

Primary sources:

Mosaic AI Model Training

Deployment, Monitoring, and Governance

Operating GenAI on Databricks in production.

++MLflow + Databricks: model registration, version control, alias management
++Mosaic AI Gateway: rate limiting, PII detection, AI guardrails, audit logging
++Unity Catalog governance for models, prompts, datasets
++Monitoring: Lakehouse Monitoring for inference tables, drift detection
++Responsible AI: AI Guardrails, content moderation, PII redaction

Primary sources:

Practice scenarios (6)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 6 · Generative AI Application Designfree preview

A team building a RAG application on Databricks needs to keep a vector index synchronized with an updating Delta table of policy documents. Which Databricks feature is the canonical fit?

A. Spark Streaming with a custom pipeline to write to a third-party vector database
B. Databricks Vector Search with Delta Sync: define the source Delta table and the embedding model, and Databricks manages the sync
C. Daily batch export of the table to JSON, manual upload to an external vector store
D. Manually re-index after every document change

Answer: B

Databricks Vector Search Delta Sync is the managed integration that keeps a vector index synchronized with the source Delta table. Spark Streaming with custom pipelines is feasible but defeats the managed-platform value. Manual processes are anti-patterns.

Reference: Databricks Vector Search Delta Sync

Unlock the rest

5 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify whether the question is about design, building, deployment, or governance. Each section emphasizes different Databricks products.
++Most questions have one Databricks-native managed-service answer; eliminate options that require custom code where a managed service exists.
++Vector Search, Mosaic AI Model Serving, Mosaic AI Gateway, Mosaic AI Agent Framework, and Mosaic AI Model Training are the five product names most frequently tested. Internalize what each does.
++Watch for distractors that confuse training (Mosaic AI Model Training) with serving (Mosaic AI Model Serving) or with evaluation (Mosaic AI Agent Evaluation).
++Pace at 2 minutes per question. 90 minutes for 45 questions is generous.

Additional resources

Sources

Official Databricks Certified Generative AI Engineer Associate exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Domain reviews aligned to the official Databricks GenAI Engineer Associate exam guide

++Three full-length mock exams matching Databricks's question format and difficulty

++Mosaic AI lab walkthroughs covering vector search, model serving, and AI agents on Databricks

++Lakehouse-specific RAG design patterns and Unity Catalog governance for AI workloads

++Direct link to the official Databricks certification page as the canonical reference

Domain reviews

Generative AI Application Design

Architectural patterns: RAG, prompt engineering, agentic workflows on Databricks.

++RAG design on Databricks: Vector Search, embedding models, chunking
++Prompt engineering patterns: zero-shot, few-shot, chain-of-thought, structured output
++Agent design with Mosaic AI Agent Framework
++Choosing models: foundation-model APIs vs custom fine-tuned models, Mosaic AI Model Serving
++Data design: Unity Catalog volumes, Delta tables, vector indexes

Primary sources:

Data Preparation and Vector Search

Preparing and indexing data for GenAI use.

++Document loading and chunking strategies
++Embedding model selection (BGE, Instructor, OpenAI, etc.)
++Databricks Vector Search: managed and self-hosted endpoints
++Sync from Delta tables to vector indexes
++Hybrid search: dense vector plus keyword

Primary sources:

Databricks Vector Search docs

Model Selection, Fine-Tuning, and Evaluation

Choosing and customizing models on Databricks.

++Mosaic AI Model Training for fine-tuning
++Foundation-Model APIs (Llama, DBRX, Mixtral, etc.) for pay-per-token inference
++Mosaic AI Model Serving: serverless, provisioned throughput, multi-model
++Evaluation: Mosaic AI Agent Evaluation, custom eval datasets, MLflow LLM evaluation
++Cost considerations: token cost, latency, throughput tradeoffs

Primary sources:

Mosaic AI Model Training

Deployment, Monitoring, and Governance

Operating GenAI on Databricks in production.

++MLflow + Databricks: model registration, version control, alias management
++Mosaic AI Gateway: rate limiting, PII detection, AI guardrails, audit logging
++Unity Catalog governance for models, prompts, datasets
++Monitoring: Lakehouse Monitoring for inference tables, drift detection
++Responsible AI: AI Guardrails, content moderation, PII redaction

Primary sources:

Practice scenarios (6)

Preview scenario 1 of 6 · Generative AI Application Designfree preview

A team building a RAG application on Databricks needs to keep a vector index synchronized with an updating Delta table of policy documents. Which Databricks feature is the canonical fit?

A. Spark Streaming with a custom pipeline to write to a third-party vector database
B. Databricks Vector Search with Delta Sync: define the source Delta table and the embedding model, and Databricks manages the sync
C. Daily batch export of the table to JSON, manual upload to an external vector store
D. Manually re-index after every document change

Answer: B

Reference: Databricks Vector Search Delta Sync

Unlock the rest

5 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify whether the question is about design, building, deployment, or governance. Each section emphasizes different Databricks products.

++Most questions have one Databricks-native managed-service answer; eliminate options that require custom code where a managed service exists.

++Vector Search, Mosaic AI Model Serving, Mosaic AI Gateway, Mosaic AI Agent Framework, and Mosaic AI Model Training are the five product names most frequently tested. Internalize what each does.

++Watch for distractors that confuse training (Mosaic AI Model Training) with serving (Mosaic AI Model Serving) or with evaluation (Mosaic AI Agent Evaluation).

++Pace at 2 minutes per question. 90 minutes for 45 questions is generous.