1. Exam Overview

What the exam is testing

AI-900 is a fundamentals exam. It tests whether you can recognize:

• the correct AI workload for a business requirement;
• the most suitable Azure service for that workload;
• the difference between closely related AI capabilities;
• the basic logic of machine learning;
• responsible AI principles;
• common generative AI scenarios, risks, and safeguards.

It is not primarily a coding exam. You should be able to read a short business scenario, identify the required output, eliminate unrelated capabilities, and choose the simplest correct option.

The source question bank contains 1,104 original practice questions organized into easy, medium, and hard sections. Its repeated patterns reveal the most valuable exam skills:

1. Match the data type to the correct workload.
2. Match the requested output to the correct capability.
3. Distinguish a managed Azure service from an unrelated service.
4. Recognize when a plausible answer solves a nearby but different problem.
5. Apply responsible AI controls to public-facing or high-impact systems.
6. Choose the correct model type, dataset role, or endpoint in Azure Machine Learning.
7. For generative AI, distinguish generation from extraction and add grounding, evaluation, safety controls, and human review where appropriate.

How to think like the exam

Use this four-step method for almost every scenario:

1. Identify the input type.
   Is the input text, audio, image, video, a document, or tabular data?

2. Identify the required output.
   Does the user need a category, a number, extracted text, a detected object location, generated content, a transcription, a translation, or a risk-control decision?

3. Select the narrowest correct capability.
   Prefer the workload or service that directly produces the requested output. Do not choose a broad service when the question asks for a specific capability.

4. Reject adjacent distractors.
   Many wrong answers are not random. They are nearby concepts that operate on the wrong data type or produce the wrong output.

How to use this course

Study the guide in four passes:

• Pass 1: Learn the workload map and service map.
• Pass 2: Memorize the confusing pairs.
• Pass 3: Practice scenario elimination.
• Pass 4: Use the rapid-review and exam-day checklist.

2. Exam Domains

Official AI-900 domain list

Priority notes

Generative AI has the largest official weighting. However, the other four domains are each large enough that you cannot safely skip any of them.

A practical revision split:

What matters most

The most exam-relevant decision rules are:

• Images or video: think computer vision.
• Written language: think NLP.
• Spoken audio: think speech recognition, speech synthesis, or speech translation.
• Scanned document fields: think document processing and OCR.
• New text, summaries, answers, or code: think generative AI.
• Numeric prediction: think regression.
• Known category prediction: think classification.
• Unlabeled grouping: think clustering.
• Real-time model prediction API: think managed online endpoint.
• Scheduled large-scale scoring: think batch endpoint.
• Trusted-answer generation: think grounding with approved data.
• High-impact generated output: think human review and safeguards.

3. Start-to-Finish Study Path

Foundation

Master these before moving on:

1. AI workload families:

   • computer vision;
   • NLP;
   • document processing;
   • machine learning;
   • generative AI.

2. ML techniques:

   • regression;
   • classification;
   • clustering.

3. Responsible AI principles:

   • fairness;
   • reliability and safety;
   • privacy and security;
   • inclusiveness;
   • transparency;
   • accountability.

4. Core service map:

   • Azure AI Vision;
   • Azure AI Face detection service;
   • Azure AI Language;
   • Azure AI Speech;
   • Azure Machine Learning;
   • Azure OpenAI Service;
   • Azure AI Foundry;
   • Azure AI Foundry model catalog.

Intermediate

Practice choosing between confusing options:

• image classification vs object detection;
• OCR vs document processing;
• speech recognition vs speech synthesis;
• entity recognition vs key phrase extraction;
• classification vs clustering;
• online endpoint vs batch endpoint;
• Azure OpenAI Service vs Azure AI Foundry;
• grounding vs unrestricted generation;
• transparency vs accountability;
• fairness vs reliability and safety.

Advanced

Work through scenario logic:

• Find the exact output requested.
• Check whether the input type matches the service.
• Eliminate answers that return a different output.
• Add governance controls when the workload affects people or uses sensitive information.
• Add evaluation, content safety, grounding, and human review to generative AI where required.
• Avoid overengineering: a fundamentals question usually rewards the simplest directly aligned solution.

Final review

On your last revision day:

1. Read the rapid service-selection table.
2. Recite the six responsible AI principles.
3. Compare the confusing pairs.
4. Review the “If you see X, think Y” rules.
5. Confirm that you can explain why each top distractor is wrong.

4. Core Concepts by Domain

Domain 1 : Describe Artificial Intelligence Workloads and Considerations

4.1 Common AI workloads

Workload selection table

Computer vision workload

Computer vision interprets visual information such as images and video.

Examples:

• identify damaged products in photographs;
• locate vehicles in a camera frame;
• read serial numbers from equipment labels;
• detect whether a face appears in an image.

Exam rule: If the input is pixels and the question is about visible content, start with computer vision.

NLP workload

Natural language processing works with text and language.

Examples:

• determine whether a review is positive or negative;
• extract organization names from reports;
• identify important phrases in feedback;
• translate support messages.

Exam rule: If the input is written human language, start with NLP.

Document processing workload

Document processing extracts structured information from documents such as forms, invoices, or receipts.

Examples:

• vendor name;
• invoice number;
• total amount;
• form fields;
• document-specific structured values.

Trap: OCR reads text. Document processing is broader: it can organize extracted information into meaningful fields.

Generative AI workload

Generative AI creates new content based on instructions or context.

Examples:

• generate a response;
• summarize a conversation;
• answer a question using approved documentation;
• draft product descriptions;
• generate starter code.

Trap: OCR extracts existing content. Generative AI creates new output.

4.2 Responsible AI principles

Fairness

Choose fairness when the main issue is unequal treatment or unjustified differences in outcomes.

Example:

A loan-screening system has lower approval rates for one demographic group even when relevant factors are similar.

Correct focus: fairness.

Do not choose transparency merely because explanations are useful. Transparency does not replace testing for equitable outcomes.

Reliability and safety

Choose reliability and safety when the concern is robust behavior, failure handling, or harm prevention.

Example:

Test how a chatbot responds to ambiguous, unsafe, or adversarial prompts.

Correct focus: reliability and safety.

Do not choose fairness unless the scenario is about unequal outcomes across groups.

Privacy and security

Choose privacy and security when sensitive data, access controls, confidential prompts, or information exposure are involved.

Typical controls:

• data minimization;
• access restriction;
• secure handling of prompts and retrieved content;
• protection of training data;
• protection of customer records.

Inclusiveness

Choose inclusiveness when the requirement is to support diverse users, abilities, or circumstances.

Example:

A voice-enabled application must also work for users who cannot rely on audio.

Correct focus: inclusiveness.

Transparency

Choose transparency when users need to know:

• AI is being used;
• output may require verification;
• a model has limitations;
• how a recommendation was generated at an appropriate level.

Accountability

Choose accountability when the organization must assign responsibility:

• business owner;
• approval authority;
• review process;
• escalation path;
• appeal mechanism.

High-impact decision rule

When an AI system can materially affect people, favor:

• human review;
• escalation;
• auditability;
• appeal routes;
• assigned ownership;
• monitoring.

Avoid:

• fully automated adverse decisions with no appeal;
• removal of human oversight;
• removal of logging;
• assuming AI output is always correct.