Microsoft AuthorisedAssociate · AI Engineer2026 + GenAIHighest-Demand AI Cert
AI-102 Azure AI Engineer Associate
Build, deploy and operate AI solutions on Azure — Azure OpenAI, AI Search, Document Intelligence, Vision and Language services — mapped to the Microsoft AI-102 exam.
⏱Duration: 4 days / 32 hrs
💻Format: Instructor-Led + Azure AI Sandbox
🌐Delivery: On-site · Virtual · Hybrid
✅Pass rate: 92%
📅Next intake: 26 May 2026
🤖
Azure OpenAI mastery
Models, prompt engineering, RAG patterns, Assistants API at production scale
🔍
AI Search
Vector + hybrid retrieval and grounded GenAI applications
📊
Document Intelligence
Form recognition, layout, custom extraction models
🌐
Multi-service architecture
Vision, Speech, Language and Translator orchestrated together
What this course is
Where AI stops being a demo.
AI-102 is the certification that proves you can build, deploy and operate AI solutions on Azure that work in production. It covers Azure OpenAI, AI Search, Document Intelligence, the Vision and Language services, and the application patterns that hold up under real load.
At Nexperts, AI-102 is delivered with a personal Azure AI sandbox, real OpenAI deployments and a focus on RAG patterns. By day 4 you've shipped a grounded GenAI assistant on your own corpus — not a slide deck.
Azure OpenAI is not magic. It is a careful pipeline of retrieval, prompt engineering, evaluation and observability. AI-102 teaches the engineering, not the hype.
The 2026 AI-102 objectives expanded coverage of Azure OpenAI Assistants, AI Search vector indexes, fine-tuning, content safety and prompt-injection mitigation. We cover all five with hands-on labs.
Who should take this course
👨💻
Software developers
Already shipping software, now adding AI capabilities. AI-102 is the formal credential.
📚
Data scientists
Strong on modelling, lighter on Azure deployment. AI-102 is the bridge to production.
☁️
Cloud engineers
AZ-204 / AZ-104 holders branching into AI. AI-102 stacks cleanly.
🌐
Solutions architects
Designing AI-augmented workloads. AI-102 keeps you honest in technical reviews.
🌟
ML practitioners
Coming from open-source ML, learning Azure-native deployment patterns.
💼
Senior IT leaders
Owning AI strategy. AI-102 gives the technical depth to lead from.
Prerequisites
✓ 1–2 years of professional development experience
✓ Comfortable with REST APIs, JSON and HTTP fundamentals
✓ Familiarity with Python OR C# at intermediate level
✓ Working knowledge of Azure portal navigation (AZ-900 helpful)
→ Don't have AZ-900? Ask about our AZ-900 → AI-102 bundle.
Course Curriculum
Five domains. One AI-engineering toolkit.
AI-102 is structured into Plan AI Solutions, Implement Generative AI, Implement Computer Vision, Implement Natural Language Processing, and Implement Knowledge Mining & Document Intelligence. We deliver in build-first order.
Hands-On Lab Stack
9 builds. On real Azure AI.
Every learner gets an Azure AI sandbox subscription with OpenAI access pre-approved. You don't simulate AI — you ship a real assistant by day 4.
01
AI Multi-Service Setup
Provision a multi-service Azure AI resource. Wire Managed Identity. Trace the calls.
Setup
02
Prompt Engineering Lab
Build a 6-prompt evaluation harness. Compare zero-shot, few-shot and ReAct on the same task.
Prompts
03
Function Calling Pipeline
Build a function-calling assistant that queries an internal API and a Cosmos DB store.
GenAI
04
RAG with AI Search
Build a vector index over a real corpus. Implement hybrid retrieval. Cite sources in the response.
Retrieval
05
Document Intelligence Custom
Train a custom invoice extraction model. Validate accuracy on a hold-out set.
Documents
06
Custom Vision Project
Train an object-detection model. Deploy to a container. Validate latency on edge.
Vision
07
Speech + GenAI Pipeline
Wire Speech-to-Text into Azure OpenAI. Generate response. Synthesise via custom voice.
Speech
08
Content Safety Sprint
Apply Azure AI Content Safety. Block 5 prompt-injection attempts. Document each block.
Safety
09
End-to-End Assistant
Tie it all together: indexed corpus, function calling, content safety, telemetry, deployed.
Production
+ 13 micro-tasks across the Azure AI SDK in Python and C#.
Exam Information
One scenario-heavy exam. Code reading focus.
AI-102 has 40–60 questions over 100 minutes, blending scenarios, code-completion items and SDK-recall items. The 2026 update sharpened the GenAI focus — expect 30%+ on Azure OpenAI.
Common gotchasVector index field-type, OpenAI version flags
CoachingDedicated 4-hour code workshop
StrategyRead the SDK call boundary first
OutcomeCode-question score uplift averages +20%
WalkthroughPast code-archive provided
Our 3-Mock Programme
01
Diagnostic Mock
End of day 1. Maps weak service areas. Average score: 56%.
02
Code-Heavy Mock
Mid-course. 50% scenarios, 50% SDK code review. Average score: 70%.
03
Final Clearance
Full timed simulation. 80%+ before we book. Average score: 83%.
0%
Pass Rate
92% of our AI-102 engineers pass on first attempt.
The Microsoft global first-attempt rate for AI-102 sits around 64%. We hit 92% by spending 50% of class time in real Azure AI services, drilling SDK code under timer, and gating booking on a clearance mock.
Real Azure AI sandboxCode-completion workshop92% first attemptFree retake voucherProduction RAG track
Why our pass rate is 92%
Industry average: ~64%
Most candidates over-revise GenAI buzz and under-practise the SDK calls. They walk in confident on terminology and lose on code questions that they could write in 90 seconds at their desk.
Nexperts: 92%
We treat AI-102 as an engineering exam. You write SDK code on the whiteboard. You design RAG patterns under timer. You earn the booking by clearing the simulation.
Your Microsoft AI Path
AI-102 is the entry to AI engineering.
AI-102 is the most senior associate-level Microsoft AI cert. Most graduates stack it with AZ-204 or move into specialty paths in MLOps and Azure Databricks.
Before this
AZ-900 + dev experience
AZ-900 sets the Azure base. Development experience in Python or C# is critical.
Expected salary range after AI-102: RM 10,000 – RM 18,000/month for Azure AI engineering roles in MNC and tech firms.
Student Reviews
What our AI-102 engineers say.
4.8
★★★★★
94 reviews
5★
83%
4★
14%
3★
3%
★★★★★
"The RAG architecture lab was the highlight. We took the exact pattern back to my company and shipped a knowledge-base assistant in two weeks. Cleared AI-102 the week after."
JL
Joel Lim
AI Engineer · Carsome
✓ Passed first attempt · 822/1000
★★★★★
"Came from open-source ML. Always felt Azure was 'enterprise overhead'. AI-102 with Nexperts changed my view — the integration story is genuinely powerful."
SR
Subashini Ramalingam
ML Engineer · Standard Chartered GBS
✓ Passed first attempt
★★★★
"Content safety and prompt-injection labs were unexpectedly the most useful. Most courses skip safety. This one didn't."
ZL
Zairul Latif
Senior Developer · PETRONAS Digital
✓ Passed first attempt · 786/1000
★★★★★
"Document Intelligence custom-model training was straight up the most valuable lab. We're now extracting invoice data at scale at work."
CK
Catherine Khoo
Cloud Solutions Eng · IHH Healthcare
✓ Passed first attempt · 858/1000
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