Microsoft Azure AI Engineer Associate Study Guide (2026)

Microsoft Azure AI Engineer Associate Study Guide 2026 – Pass on Your First Attempt

This Microsoft Azure AI Engineer Associate study guide covers all exam domains, key concepts, and real exam-style scenarios to help you pass on your first attempt. Learn what topics matter most, avoid common mistakes, and follow a structured plan based on the official exam blueprint.

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What should you study for the Microsoft Azure AI Engineer Associate exam?

To pass the Microsoft Azure AI Engineer Associate certification exam, you should focus on:

  • Plan and Manage Azure AI Solutions: Covers selecting the right Azure AI services, managing authentication, responsible AI principles, and monitoring.
  • Implement Content Moderation Solutions: Covers Azure AI Content Safety for detecting and filtering harmful content in text and images.
  • Implement Computer Vision Solutions: Covers Azure AI Vision, image analysis, optical character recognition, and custom vision.
  • Implement Natural Language Processing Solutions: Covers Azure AI Language service including entity recognition, sentiment analysis, summarisation, CLU, and question answering.
  • Implement Generative AI Solutions with Azure OpenAI: Covers Azure OpenAI Service, prompt engineering, RAG patterns, assistants API, and deploying generative AI solutions.
  • Implement Knowledge Mining and Document Intelligence Solutions: Covers Azure AI Search with AI enrichment pipelines and Azure AI Document Intelligence.

The exam tests your ability to apply concepts in real scenarios, not just memorize definitions.

Microsoft Azure AI Engineer Associate Exam Syllabus and Topics

The Microsoft Azure AI Engineer Associate exam is divided into 6 domains. Each domain tests specific skills and contributes to your overall score.

Plan and Manage Azure AI Solutions

Covers selecting the right Azure AI services, managing authentication, responsible AI principles, and monitoring.

15%
Weight
8
Questions
150
Marks

Azure AI Services Overview

  • Azure AI Services multi-service vs single-service resources
  • Choosing between Azure OpenAI Service, Azure AI Services, and Azure ML
  • Authentication: API keys, managed identity, and Microsoft Entra ID
  • Azure Key Vault for managing AI service secrets and keys
  • Monitoring AI services: Azure Monitor, Log Analytics, diagnostics logs

Responsible AI

  • Microsoft Responsible AI principles: Fairness, Reliability, Privacy, Inclusiveness, Transparency, Accountability
  • Content Safety service: detecting harmful content categories
  • Azure AI model evaluation and bias detection
  • Transparency notes and RAI impact assessments

Implement Content Moderation Solutions

Covers Azure AI Content Safety for detecting and filtering harmful content in text and images.

10%
Weight
5
Questions
100
Marks

Content Safety Implementation

  • Content Safety categories: hate, violence, sexual, self-harm
  • Severity levels (0–6) and configuring thresholds
  • Text moderation API and image moderation API
  • Blocklists and custom categories
  • Integrating Content Safety with Azure OpenAI deployments
  • Grounding and prompt shields for jailbreak detection

Implement Computer Vision Solutions

Covers Azure AI Vision, image analysis, optical character recognition, and custom vision.

15%
Weight
8
Questions
150
Marks

Image Analysis

  • Azure AI Vision Image Analysis 4.0: dense captioning, object detection, tagging
  • OCR with Azure AI Vision: Read API for printed and handwritten text
  • Spatial analysis: detecting people and objects in video streams
  • Face API: face detection, attribute analysis, and verification
  • Custom Vision: training image classifiers and object detectors
  • ONNX model export from Custom Vision for edge deployment

Video and Document Intelligence

  • Azure Video Indexer: transcription, people detection, scene segmentation
  • Azure AI Document Intelligence: pre-built vs custom models
  • Pre-built models: invoice, receipt, business card, ID document, tax form
  • Custom model training: labelled data requirements and confidence thresholds
  • Composed models: combining multiple custom models

Implement Natural Language Processing Solutions

Covers Azure AI Language service including entity recognition, sentiment analysis, summarisation, CLU, and question answering.

20%
Weight
11
Questions
200
Marks

Pre-built Language Features

  • Sentiment analysis and opinion mining
  • Key phrase extraction and entity recognition (NER)
  • Personally Identifiable Information (PII) detection and redaction
  • Language detection
  • Text summarisation: extractive vs abstractive
  • Named entity recognition categories and custom NER

Conversational Language Understanding (CLU)

  • Intents, entities, and utterances in CLU
  • Training and evaluating a CLU model
  • Direct line speech integration for voice-enabled CLU
  • Orchestration workflows: combining CLU with QnA and LUIS

Question Answering

  • Custom question answering: knowledge base creation from documents and URLs
  • Chitchat and multi-turn conversations
  • Active learning and review suggestions
  • Integrating question answering with Azure AI Bot Service

Translator and Speech Services

  • Azure AI Translator: text translation, document translation, and custom translator
  • Azure AI Speech: speech-to-text, text-to-speech, and speech translation
  • Custom Speech models: adapting acoustic and language models
  • Custom Neural Voice: creating branded voices
  • Speaker recognition: identification and verification

Implement Generative AI Solutions with Azure OpenAI

Covers Azure OpenAI Service, prompt engineering, RAG patterns, assistants API, and deploying generative AI solutions.

25%
Weight
14
Questions
250
Marks

Azure OpenAI Fundamentals

  • Azure OpenAI models: GPT-4o, GPT-4, GPT-3.5-Turbo, text-embedding models, DALL-E, Whisper
  • Provisioned throughput vs token-based consumption
  • Model deployment types: Standard, Provisioned Managed, Global Standard
  • Azure OpenAI Studio: playground, deployments, and fine-tuning
  • API endpoints: completions, chat completions, embeddings, image generation

Prompt Engineering

  • System message design and persona setting
  • Few-shot prompting: providing examples in context
  • Chain-of-thought prompting for complex reasoning
  • Temperature and top-p: controlling response randomness and diversity
  • Max tokens, stop sequences, and frequency/presence penalties
  • Prompt injection risks and mitigation strategies

Retrieval-Augmented Generation (RAG)

  • RAG architecture: retrieval, augmentation, and generation pipeline
  • Azure AI Search as a vector store: indexing, embedding, and hybrid search
  • Semantic ranking and vector similarity search in Azure AI Search
  • Chunking strategies for document indexing
  • Azure OpenAI on your data: connecting OpenAI models to Azure AI Search
  • Evaluating RAG quality: faithfulness, groundedness, and relevance metrics

Azure AI Foundry and Agents

  • Azure AI Foundry (formerly Azure AI Studio): project management and model catalogue
  • Azure OpenAI Assistants API: threads, runs, and tool use
  • Function calling: connecting models to external APIs
  • Code interpreter and file search built-in tools
  • Semantic Kernel and LangChain integration patterns
  • Evaluation flows in Azure AI Foundry

Implement Knowledge Mining and Document Intelligence Solutions

Covers Azure AI Search with AI enrichment pipelines and Azure AI Document Intelligence.

15%
Weight
9
Questions
150
Marks

AI Enrichment Pipelines

  • Azure AI Search architecture: indexers, data sources, indexes, and skillsets
  • Built-in cognitive skills: OCR, language detection, key phrase extraction, entity recognition
  • Custom skills: using Azure Functions or Azure ML endpoints in a skillset
  • Knowledge store: projections to Blob Storage and Table Storage
  • Debug sessions for troubleshooting skillsets
  • Semantic search and semantic ranker configuration
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Learning Progress

Plan and Manage Azure AI Solutions 85%
Implement Content Moderation Solutions 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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