Applied AI cert-prep add-on

Microsoft Azure AI Fundamentals (AI-900) Exam Prep Add-On

Convert AI Engineering Mastery's foundation modules into an AI-900 entry-credential ramp.

$97 add-on25h additional studyTarget exam: Microsoft Certified: Azure AI FundamentalsExam fee: $99

What the add-on ships

++Five domain reviews mapped to the official Microsoft AI-900 exam study guide
++Three full-length 40 to 60 question mock exams in Microsoft's foundation format
++AI workload taxonomy: regression, classification, clustering, NLP, computer vision, generative
++Responsible AI principles and the Azure AI service catalog overview
++Direct link to the official Microsoft Learn AI-900 exam page as the canonical reference

Built on the parent course

++AI Engineering Mastery foundation modules covering ML fundamentals, generative AI taxonomy, and AI lifecycle
++Glossary and vocabulary practice from the broader curriculum that compounds on the AI-900 surface

Parent course: ai engineering mastery

Best for

++Career changers entering Applied AI from adjacent IT roles looking for a low-friction credential
++Project managers and product owners adding AI literacy without an engineering background
++Sales engineers selling Azure AI services who need foundational technical credibility

Practice surface

++Three Microsoft-format mock exams in the AI-900 style
++AI workload identification worked examples
++Targeted weak-area question drilling per AI-900 exam objective

Buy the add-on

$97 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

Microsoft Azure AI Fundamentals (AI-900) is Microsoft's entry-level AI credential. Covers AI workload taxonomy, ML fundamentals, computer vision, NLP, generative AI, and the Azure AI service catalog at a foundation level. 40 to 60 questions in 45 minutes.

Domain reviews

AI Workload Identification

Recognizing the AI workload type from a scenario.

++Regression, classification, clustering
++NLP tasks: classification, NER, summarization, translation
++Computer vision tasks: classification, object detection, segmentation
++Generative AI: text, image, code generation
++Anomaly detection, knowledge mining

Primary sources:

AI-900 study guide

ML Fundamentals

Foundational ML concepts at AI-900 depth.

++Training vs validation vs test splits
++Common metrics: accuracy, precision, recall, F1, RMSE
++Overfitting and underfitting
++Feature engineering basics

Primary sources:

Microsoft ML fundamentals on Learn

Azure AI Service Catalog

Identifying the right Azure AI service for a scenario.

++Azure OpenAI, AI Search, AI Foundry
++AI Vision, AI Document Intelligence
++AI Language, AI Translator, AI Speech
++Azure Machine Learning

Primary sources:

Azure AI services overview

Responsible AI

Microsoft Responsible AI Standard at foundation depth.

++Six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability
++Azure AI Content Safety
++Recognized AI risks (bias, hallucination, privacy)

Primary sources:

Microsoft Responsible AI Standard v2

Practice scenarios (5)

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 5 · AI Workload Identificationfree preview

A retail company wants to predict whether a customer will respond to a promotional email (yes / no). Which ML task is this?

A. Regression
B. Binary classification
C. Clustering
D. Anomaly detection

Answer: B

A yes/no prediction is a binary classification task. Regression predicts continuous values. Clustering groups unlabeled data. Anomaly detection identifies outliers.

Reference: AI-900 study guide

Unlock the rest

4 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. $97 one-time, lifetime access.

Exam-day strategy

++Read each question for the workload type (classification, regression, clustering, NER, OCR, generation). The right answer almost always maps to one specific workload.
++Match scenarios to Azure service names. The exam tests whether you can name the right service for a use case.
++Memorize the six Microsoft Responsible AI principles. Distractors swap them.
++Pace at roughly 50 seconds per question across 45 minutes. The exam is tight if you over-think.

Additional resources

Sources

Official Microsoft Certified: Azure AI Fundamentals 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

++Five domain reviews mapped to the official Microsoft AI-900 exam study guide

++Three full-length 40 to 60 question mock exams in Microsoft's foundation format

++AI workload taxonomy: regression, classification, clustering, NLP, computer vision, generative

++Responsible AI principles and the Azure AI service catalog overview

++Direct link to the official Microsoft Learn AI-900 exam page as the canonical reference

Domain reviews

AI Workload Identification

Recognizing the AI workload type from a scenario.

++Regression, classification, clustering
++NLP tasks: classification, NER, summarization, translation
++Computer vision tasks: classification, object detection, segmentation
++Generative AI: text, image, code generation
++Anomaly detection, knowledge mining

Primary sources:

AI-900 study guide

ML Fundamentals

Foundational ML concepts at AI-900 depth.

++Training vs validation vs test splits
++Common metrics: accuracy, precision, recall, F1, RMSE
++Overfitting and underfitting
++Feature engineering basics

Primary sources:

Microsoft ML fundamentals on Learn

Azure AI Service Catalog

Identifying the right Azure AI service for a scenario.

++Azure OpenAI, AI Search, AI Foundry
++AI Vision, AI Document Intelligence
++AI Language, AI Translator, AI Speech
++Azure Machine Learning

Primary sources:

Azure AI services overview

Responsible AI

Microsoft Responsible AI Standard at foundation depth.

++Six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability
++Azure AI Content Safety
++Recognized AI risks (bias, hallucination, privacy)

Primary sources:

Microsoft Responsible AI Standard v2

Practice scenarios (5)

Preview scenario 1 of 5 · AI Workload Identificationfree preview

A retail company wants to predict whether a customer will respond to a promotional email (yes / no). Which ML task is this?

A. Regression
B. Binary classification
C. Clustering
D. Anomaly detection

Answer: B

A yes/no prediction is a binary classification task. Regression predicts continuous values. Clustering groups unlabeled data. Anomaly detection identifies outliers.

Reference: AI-900 study guide

Unlock the rest

4 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. $97 one-time, lifetime access.

Exam-day strategy

++Read each question for the workload type (classification, regression, clustering, NER, OCR, generation). The right answer almost always maps to one specific workload.

++Match scenarios to Azure service names. The exam tests whether you can name the right service for a use case.

++Memorize the six Microsoft Responsible AI principles. Distractors swap them.

++Pace at roughly 50 seconds per question across 45 minutes. The exam is tight if you over-think.