AI-102 Azure AI Engineer Associate Certification Course

1. Exam Overview

What the exam is testing

AI-102 tests whether you can design, build, deploy, secure, monitor, and integrate Azure AI solutions. The exam is not only about knowing service names. Most questions ask you to choose the best service, architecture, API, deployment model, or security approach for a business scenario.

You should be ready to reason about:

• Selecting the right Microsoft Foundry / Azure AI service for a requirement.
• Building generative AI solutions with models, prompts, retrieval, evaluation, safety, and deployment.
• Creating agentic solutions that use tools, grounding data, workflows, and guardrails.
• Implementing computer vision, OCR, image analysis, video/image classification, and face-related scenarios.
• Implementing language, speech, translation, summarization, sentiment, PII detection, and conversational workflows.
• Building search, enrichment, document extraction, vector retrieval, and knowledge mining pipelines.
• Applying Responsible AI, content safety, identity, network security, monitoring, cost control, and CI/CD.

How to think like the exam

The exam usually hides the answer in the requirement wording. Look for these clues:

How to use this course

1. Read the domain overview first.
2. Study each domain by service-selection logic, not by memorizing isolated facts.
3. Use the tables to eliminate wrong answers quickly.
4. Review the traps section before practicing questions.
5. Use the final checklist as the last-day exam review.

2. Exam Domains

The official AI-102 domains are organized as follows. The CSV question bank was generated to match these priorities and then consolidated into this course.

Priority notes

High-yield areas from the source bank:

• Security and deployment appear repeatedly across domains, especially managed identity, RBAC, Key Vault, private endpoints, and logging.
• Azure AI Search is one of the most repeated services because it appears in knowledge mining, RAG, generative AI, and agentic grounding.
• Document Intelligence is frequently contrasted with OCR, Azure AI Search, and generative AI.
• Language, Speech, and Translator are commonly confused in scenario questions.
• Foundry, generative AI, RAG, prompt evaluation, and content safety are emphasized in the latest blueprint.

3. Start-to-Finish Study Path

Foundation phase: know the service map

Study the purpose of each core service:

• Microsoft Foundry / Azure AI Foundry: build, deploy, evaluate, and manage AI apps, models, prompt flows, and generative AI solutions.
• Azure OpenAI / model deployment: chat, completions, embeddings, generative AI, summarization, classification, code/text generation.
• Azure AI Search: lexical, semantic, hybrid, vector search, indexing, enrichment, retrieval for RAG.
• Azure AI Document Intelligence: structured extraction from documents such as invoices, receipts, IDs, tax forms, and custom forms.
• Azure AI Content Understanding: multimodal extraction and understanding across content types when the scenario goes beyond simple form extraction.
• Azure AI Language: sentiment, key phrase extraction, named entity recognition, PII detection, summarization, classification, conversational language understanding.
• Azure AI Speech: speech-to-text, text-to-speech, speech translation, pronunciation assessment, speaker-related capabilities where supported.
• Azure AI Translator: text translation and document translation.
• Azure AI Vision: OCR, image analysis, captions, object/tag detection, spatial/image insights where supported.
• Azure AI Content Safety: detect harmful user or model-generated text/images.

Intermediate phase: learn decision rules

For each scenario, identify:

1. Input type: text, speech, image, video, document, mixed content.
2. Output type: extracted fields, search results, generated text, classification, translation, speech, summary.
3. Customization level: prebuilt model, custom model, prompt-based, fine-tuned/model deployment, custom extractor.
4. Security requirements: keyless auth, private network, data isolation, audit logging, compliance.
5. Integration style: SDK, REST API, container, managed endpoint, CI/CD pipeline, event-driven ingestion.
6. Operational needs: monitoring, evaluation, cost, latency, failover, versioning.

Advanced phase: master architecture tradeoffs

Practice these tradeoffs:

• Prebuilt model vs custom model.
• OCR vs Document Intelligence.
• Azure AI Search indexing vs direct database query.
• RAG vs fine-tuning vs prompt engineering.
• Semantic search vs vector search vs hybrid search.
• Single-service resource vs multi-service resource.
• API key vs managed identity.
• Public endpoint vs private endpoint.
• Batch ingestion vs real-time inference.
• Model evaluation vs application monitoring.

Final review phase

Before exam day, focus on:

• Service selection tables.
• Domain traps.
• RAG architecture steps.
• Azure AI Search indexing pipeline.
• Document extraction pipeline.
• Security and Responsible AI controls.
• Prompt evaluation and content safety.
• Difference between Language, Speech, Translator, Vision, Search, and Document Intelligence.

4. Core Concepts by Domain

Domain 1: Plan and manage an Azure AI solution

Concepts

This domain tests whether you can design and operate Azure AI solutions safely and correctly. It is not limited to provisioning resources. It includes choosing services, configuring deployment options, securing access, applying Responsible AI principles, monitoring, and integrating AI services into DevOps workflows.

Key concepts:

• Selecting Microsoft Foundry Services based on task type.
• Creating Azure AI resources and choosing single-service vs multi-service resources.
• Choosing model deployment options and default endpoints.
• Installing and using SDKs and REST APIs.
• Securing secrets with Key Vault.
• Using managed identities instead of hardcoded keys.
• Applying RBAC and least privilege.
• Configuring private endpoints and network restrictions.
• Monitoring usage, latency, errors, quotas, and content safety events.
• Building repeatable CI/CD deployment for AI apps.
• Applying Responsible AI: fairness, reliability, safety, privacy, transparency, accountability.

Services

Patterns

Pattern: secure AI app integration

Recommended architecture:

1. Application uses managed identity.
2. Managed identity is granted minimum required RBAC role.
3. Secrets are stored in Key Vault only when keys are unavoidable.
4. AI service access is restricted with private endpoint if required.
5. Diagnostics are sent to Log Analytics.
6. Content Safety is applied where user-generated or model-generated content is involved.

Why wrong answers fail:

• API keys in code are fast but insecure.
• Storage account alone does not protect AI service calls.
• Public endpoint with unrestricted keys fails private/compliance requirements.

Pattern: containerized AI service

Use containers when:

• You need edge processing or low latency near data.
• Connectivity is intermittent but billing/licensing requirements can still be met.
• The selected service supports containers.

Trap: containers do not eliminate billing, licensing, connectivity, or service-specific limitations.

Traps

• Choosing Azure Machine Learning when the scenario only needs a prebuilt AI API.
• Choosing a multi-service resource when strict isolation or unsupported service features are required.
• Choosing API keys when the requirement says “no secrets” or “least privilege.”
• Assuming private endpoint automatically handles authorization; it only handles network path.
• Confusing content moderation with security authorization.
• Ignoring quotas, region availability, and model deployment constraints.

Domain 2: Implement generative AI solutions

Concepts

This domain tests your ability to build generative AI applications using Microsoft Foundry tooling, deployed models, prompt design, RAG, evaluation, and safety controls.

You must know:

• Model selection and deployment.
• Prompt engineering and system messages.
• Grounding with enterprise data.
• Retrieval-augmented generation.
• Embeddings and vector search.
• Prompt flow / orchestration patterns.
• Evaluation of generated outputs.
• Content safety and responsible AI guardrails.
• Monitoring, cost, latency, and token usage.

Services

Patterns

Pattern: RAG application

Use RAG when the question says:

• “Answers must use company documents.”
• “Information changes frequently.”
• “Citations are required.”
• “Do not retrain the model.”
• “Use private knowledge base.”

Typical RAG flow:

1. Ingest documents into storage.
2. Chunk documents into meaningful sections.
3. Generate embeddings for chunks.
4. Store chunks, metadata, and vectors in Azure AI Search.
5. At query time, embed the user query.
6. Retrieve relevant chunks with vector, semantic, or hybrid search.
7. Pass retrieved context to the model.
8. Generate grounded answer with citations.
9. Apply content safety and logging.
10. Evaluate answer quality and retrieval quality.

Pattern: prompt safety and evaluation

Use:

• System message to define role, boundaries, format, and refusal behavior.
• Grounding data to reduce hallucination.
• Content Safety to detect harmful inputs/outputs.
• Evaluation datasets to compare prompt versions.
• Logging and monitoring to detect failures in production.

Traps

• Fine-tuning when RAG is better for changing knowledge.
• Prompt engineering alone when source grounding is required.
• RAG without chunking and metadata, causing poor retrieval.
• Vector search alone when keyword matching is also important; hybrid search may be better.
• Storing secrets in prompt flow or code instead of Key Vault.
• Ignoring content safety for user-generated inputs.
• Assuming the model can access private documents without retrieval integration.