100% OFF Certification Databricks Generative AI Engineer Associate Coupon Code

Declaration: Databricks® is a registered trademark of Databricks, Inc. This course material is not affiliated with or endorsed by Databricks, Inc.

Additional Material (Exclusive Bonus Content)

• Study Guide (PDF – 200 Pages): Get a comprehensive, exam-aligned companion book to guide you through every topic. Available for download in the Resources section under Practice Paper 1, Question 1.

Prepare with confidence for the Databricks Certified Generative AI Engineer Associate exam using a focused bank of scenario-based MCQs and in-depth explanations mapped to the official blueprint. These questions help you practice problem decomposition, model/tool selection, and end-to-end GenAI solution design on Databricks—covering Vector Search, Model Serving, MLflow, and Unity Catalog for governance.

Why this practice set

• Realistic items aligned to the live exam version.

• Explanations that reinforce concepts, pitfalls, and best-practices

• Domain-wise organization so you can target weak areas efficiently

• Built to mirror how Databricks expects you to design, build, deploy, govern, evaluate, and monitor GenAI apps

About the real exam (for your planning)

• Format: 45 scored items (MCQ/MCSA; unscored items may appear)

• Time: 90 minutes | Fee: $200 | Delivery: Online proctored

• Aides: None allowed | Prerequisite: None (6 months hands-on Databricks recommended)

• Validity: 2 years | Recertification: Retake the current live exam after 2 years

Who should take these practice tests

• Engineers building RAG applications and LLM chains on Databricks

• Practitioners selecting models, embeddings, and retrieval strategies

• Teams adopting Unity Catalog governance and MLflow lifecycle management

• Python developers using LangChain, Hugging Face, and model/embedding hubs

Recommended preparation (reflected in the questions)

• Databricks Academy ILT & self-paced: Generative AI Engineering with Databricks

  • Generative AI Solution Development (RAG)

  • Generative AI Application Development (Agents)

  • Generative AI Application Evaluation & Governance

  • Generative AI Application Deployment & Monitoring

• Working knowledge of: Python, LLM APIs, prompt engineering/evaluation, and popular GenAI toolchains

What you’ll practice (exam outline mapping)

1) Design Applications

• Designing prompts for specific output formats

• Choosing model tasks and chain components for business goals

• Translating use-case goals into pipeline inputs/outputs

• Defining & ordering tools for multi-stage reasoning

2) Data Preparation

• Chunking strategies by document structure & model constraints

• Filtering extraneous content that hurts RAG quality

• Selecting Python extractors by source type/format

• Writing chunked text to Delta tables in Unity Catalog

• Picking high-quality source documents and prompt/response pairs

• Evaluating retrieval with tools/metrics, using advanced chunking and re-ranking

3) Application Development

• Creating tools for data retrieval; selecting LangChain/similar libraries

• How prompt formats impact outputs; qualitative safety/quality checks

• Context augmentation from user input (keys/terms/intents)

• Guardrails: preventing negative outcomes, metaprompts to reduce hallucinations/leakage

• Agent prompts exposing functions; utilizing Agent Frameworks

• Selecting LLMs/embedding models by context length, metadata, and experiment metrics

4) Assembling & Deploying Applications

• Coding chains (including pyfunc with pre/post-processing) and LangChain recipes

• Access control for Model Serving endpoints

• Choosing RAG elements: model flavor, embeddings, retriever, deps, signature, input examples

• MLflow registration to Unity Catalog; deploy endpoints for basic RAG

• Creating/querying Vector Search indexes (incl. Mosaic AI concepts)

• Identifying batch inference and using ai_query() appropriately; serving with Foundation Model APIs

5) Governance

• Masking techniques as guardrails to meet performance goals

• Selecting defenses against malicious inputs

• Legal/licensing considerations for data sources feeding RAG

• Alternatives for problematic text mitigation

6) Evaluation & Monitoring

• Choosing LLM size/architecture via quantitative metrics

• Key runtime metrics to monitor; inference logging and inference tables

• Evaluating RAG with MLflow; Agent Monitoring for live endpoints

• Cost controls for LLM/RAG on Databricks

• When evaluation judges require ground truth; compare evaluation vs. monitoring phases

What you’ll gain

• Sharper judgment in model & tool selection across the GenAI stack

• Hands-on familiarity with Vector Search, Model Serving, MLflow, Unity Catalog

• Confidence to implement production-grade RAG pipelines with proper governance

• Exam-day readiness through domain-focused, explanation-rich practice

Build real exam confidence: practice across the full lifecycle—design → data prep → development → deploy → govern → evaluate/monitor—exactly how Databricks frames the role.