Azure AI-102 Exam Preparation: Technical Overview

Sharing Session for Technical Team, 30–45 minutes

What is the Azure Al Engineer Associate?

The Microsoft Azure Al Engineer Associate (Al-102) is an Al certification focused on:

Using Azure's managed Al services
Working with Large Language Models (LLMs)

Who is this Certification for?

Consider the Al-102 certification if

You want to directly learn how to work with OpenAl's LLMs pragmatically.

You want to have deep knowledge on implementing Azure's Managed AI Offering

You are a Cloud Engineer upskilling to take on AI Engineer responsbilities.

Alt Image Text

Exam Guide - Content Outline

15-20% Domain 1: Plan and manage an Azure Al solution
10-15% Domain 2: Implement content moderation solutions
15-20% Domain 3: Implement computer vision solutions
30-35% Domain 4: Implement natural language processing solutions
10-15% Domain 5: Implement knowledge mining and document intelligence solutions
10-15 % Domain 6: Implement generative AI solutions

Exam Guide - Response Types

There are ~40-60 Questions

You can afford to get roughly 12-18 questions wrong

Format of Questions

Multiple Choice
Multiple Answer
Drag and Drop
Yes and No
And more...

1. Introduction (3 minutes)

What is AI-102?
Exam: Designing and Implementing a Microsoft Azure AI Solution
Focus: Building AI solutions using Azure Cognitive Services, Azure Applied AI Services, and Azure AI infrastructure.
Why it matters for our team?
Learn to integrate AI capabilities into existing apps
Understand best practices for scalable, secure, and responsible AI
Prepare for real-world projects using Azure AI

2. Core AI Services Overview (12 minutes)

a. Language & Text

Language Understanding (LUIS / CLU)
Intents, entities, utterances
Example: chatbot intent recognition
Text Analytics (Sentiment, NER, PII, Key Phrases)
Use case: customer feedback analysis
Translator
Text and document translation with data residency control

b. Vision

Computer Vision
OCR, image analysis, tagging, description generation
Custom Vision
Train custom image classifiers or object detectors
Face API
Face detection, recognition, liveness verification

c. Speech

Speech-to-Text, Text-to-Speech
Real-time transcription, custom neural voices
Speech Translation
Real-time multilingual speech translation

d. Decision & Document AI

Anomaly Detector
Time-series anomaly detection
Form Recognizer / Document Intelligence
Extract structured data from forms, invoices, receipts

3. Building Conversation AI (8 minutes)

a. QnA Maker & Custom Question Answering

Create knowledge bases from URLs, files, or manual QnA pairs
Integrate with bots for FAQ handling

b. Bot Framework

Bot Framework SDK, Composer, Emulator
Channels: Teams, Web Chat, Direct Line
Use LUIS + QnA Maker + Dispatch for multi-turn conversations

4. Advanced Topics (10 minutes)

a. Containers for AI

Run Cognitive Services on-premises or in private networks
Use Docker containers for Speech, Language, Vision, etc.
Benefits: data sovereignty, low latency, air-gapped environments

b. Responsible AI

Principles: Fairness, Inclusiveness, Transparency, Privacy
Tools: Content Moderator, PII detection, logging control

c. Azure AI Search

Index structured and unstructured data
Enrich with AI skills (OCR, entity extraction, key phrases)
Secure with private endpoints, RBAC, IP filtering

d. Deployment & Monitoring

ARM templates, Azure CLI, SDKs (C#, Python)
Logging, diagnostics, Azure Monitor
Manage keys, endpoints, and network security

5. Exam Tips & Common Scenarios (5 minutes)

Scenario-based questions: How would you design…?
Code completion: Know SDK methods (e.g., AddFaceFromStreamAsync, StartRecognizeReceiptsFromUrl)
Security & compliance: Private endpoints, VNet integration, data residency
Cost optimization: Choose between F0 (free) and S0/S1 tiers based on scale

6. Hands-On Recommendations (2 minutes)

Microsoft Learn Path: AI-102 Learning Path
Labs: Use Azure Sandbox for hands-on practice
Quickstarts: Deploy a bot, analyze an image, translate text
Sample Code: GitHub Azure-Samples/Cognitive-Services

7. Q&A (5 minutes)

Open floor for team questions
Discuss real use cases from current projects

Perfect 👍 Based on your AI-102 material + engineer audience + exam-focused workshop, here’s a 30–45 min structured tech sharing session that works well in real teams and maps directly to exam scoring patterns.

I’ll give you:

⏱ Time-boxed agenda (30–45 min)
🧠 High-frequency exam themes (what MS loves to test)
📊 Slide-by-slide outline with talking points
❓ Interactive exam drills (hands-on thinking, no labs needed)
📝 Facilitator tips (how to run it smoothly)

1️⃣ Session Overview (Exam-Focused Workshop)

Title

AI-102 for Engineers: How to Think Like the Exam

Audience Backend / Cloud / Platform engineers (assumes Azure basics, not AI experts)

Goal By the end, engineers should be able to:

Instantly pick the correct Azure AI service
Recognize trick options
Answer scenario-based questions under time pressure

2️⃣ Time-Boxed Agenda (30–45 min)

⏱ 30-Minute Version (fast & sharp)

Time	Topic
0–5 min	Exam mindset & service map
5–12 min	Language & Bots (LUIS, QnA, Text Analytics)
12–18 min	Vision & Documents
18–24 min	Containers, Security, Networking
24–28 min	Cognitive Search & Encryption
28–30 min	Lightning exam drill

⏱ 45-Minute Version (recommended)

Time	Topic
0–5 min	Exam strategy
5–15 min	Language, Bots & Speech
15–25 min	Vision & Form Recognizer
25–33 min	Containers, Keys, RBAC
33–40 min	Cognitive Search (Private Link, CMK, throttling)
40–45 min	Exam drills + Q&A

3️⃣ Slide-by-Slide Workshop Outline (Exam-Focused)

🟦 Slide 1 — Exam Mindset (5 min)

Key Message

AI-102 is NOT ML theory It’s service selection + deployment decisions

Say this explicitly

80% scenario-based
Wrong answers are technically valid but wrong context
Keywords matter more than code

📌 Exam Trigger Words

Keyword	Meaning
Minimize dev effort	Prebuilt service
Offline / on-prem	Container
Single key	CognitiveServices (multi-service)
Private traffic	Private Endpoint

🟦 Slide 2 — Azure AI Service Decision Map (5 min)

Golden Rule

If Microsoft already solved it → don’t train

Requirement	Correct Service
Chit-chat intent	LUIS
Knowledge base	QnA Maker
Sentiment	Text Analytics
Language detection	Text Analytics
Receipt extraction	Form Recognizer
OCR only	Computer Vision
Predictive maintenance	Anomaly Detector
Correlated sensors	Metrics Advisor

🚫 Trap Custom Vision / Azure ML when “minimize effort” is stated.

🟦 Slide 3 — Language + Bot Scenarios (10 min)

High-Frequency Exam Combo

“Chit-chat + KB + sentiment + multilingual”

✅ Correct Answer Pattern

LUIS + QnA Maker + Text Analytics

❌ Why Dispatch is often wrong

Dispatch is for routing between bots
Exam often does not require routing

💡 Exam Drill

“Which service automatically detects language?” ➡️ Text Analytics, not Translator

🟦 Slide 4 — Speech & Streaming (5 min)

Exam Pattern

“Streaming MP3 speech to text”

✅ Required:

AudioStreamFormat.GetCompressedFormat(MP3)
SpeechRecognizer

🚫 Wrong:

SpeechSynthesizer (TTS)
KeywordRecognizer

📌 Keyword: streaming, MP3 → CompressedFormat

🟦 Slide 5 — Vision vs Form Recognizer (8 min)

Decision Table (very exam-heavy)

Scenario	Correct
Receipts, invoices	Form Recognizer
Structured fields	Form Recognizer
OCR only	Computer Vision
PPE detection	Face API
Captions for blind users	`describeImage`

🚫 Trap

“Receipts” ≠ OCR Receipts = prebuilt Form Recognizer

🟦 Slide 6 — Containers & Security (8 min)

Exam Keywords

On-prem
Limited connectivity
No internet
Secure keys

✅ Correct Order Pattern

Pull image from MCR
Push to ACR (RBAC)
Distribute docker run script
Run container

📌 Mandatory container flags

Eula=accept
Billing={ENDPOINT}
ApiKey={KEY}

🚫 Don’t put keys directly in CLI history.

🟦 Slide 7 — Cognitive Search (7 min)

Top 3 Exam Themes

🔐 Private Access

Solution	Correct?
NSG	❌
Firewall	❌
Public endpoint + IP rule	❌
Private Endpoint	✅

🔐 CMK Encryption

Implications

Index size ↑
Query time ↑
Requires Key Vault

⚡ Throttling

Fix	Correct
Add replicas	✅
Higher tier	✅
Add indexes	❌

🟦 Slide 8 — Responsible AI (5 min)

Microsoft LOVES this

Scenario	Principle
Notify users	Transparency
Avoid bias	Fairness
Accessibility	Inclusiveness
Money decisions	Human-in-loop

💡 Exam Tip If AI affects salary / hiring / punishment ➡️ Always add human review

4️⃣ Final Exam Drill (5 min)

🔥 Rapid Fire (Ask audience)

1️⃣ “Receipts, minimize effort” ➡️ Form Recognizer

2️⃣ “Private traffic, no internet” ➡️ Private Endpoint

3️⃣ “Multiple correlated sensors” ➡️ Metrics Advisor

4️⃣ “Blind users, image output” ➡️ describeImage

5️⃣ Facilitator Tips (Very Important)

Don’t read slides → ask “why not the others?”
Always highlight why wrong answers are tempting
Use Microsoft wording (“minimize effort”, “secure”, “single key”)

6️⃣ Want Next?

I can:

📝 Add speaker notes to your PPT
❓ Create 20 exam-style MCQs for live polling
🧩 Convert this into a 90-min deep-dive workshop
📄 Produce a 1-page AI-102 cheat sheet

---

Love it—this topic is perfect for a 90-minute deep dive. Below is a battle-tested, engineer-friendly, exam-optimized 90-minute workshop that you can run straight from your existing deck + a few added slides.

I’ll structure this like a trainer’s runbook so you can facilitate, not just present.

🧠 AI-102 — 90-Minute Deep-Dive Exam Workshop (Engineer Edition)

Audience: Engineers / architects Style: Scenario-driven, exam-pattern focused Outcome: Engineers answer AI-102 questions by elimination logic, not memorization

⏱️ Full 90-Minute Agenda

Time	Module	Mode
0–10	Exam mindset & service map	Instructor
10–25	Language, Bots & Speech	Guided analysis
25–40	Vision & Document Intelligence	Scenario breakdown
40–55	Containers, Security & RBAC	Architecture thinking
55–70	Cognitive Search deep dive	Whiteboard logic
70–80	Responsible AI (scoring booster)	Fast drills
80–90	Full exam simulation	Group challenge

0–10 min — Exam Mindset (Set the Rules)

🎯 Goal

Get engineers to stop over-engineering.

Key Talking Points

“AI-102 does NOT reward clever solutions. It rewards Microsoft’s intended solution.”

The 5 Exam Laws

Minimize development effort → Prebuilt
On-prem / no internet → Container
Single key / billing → CognitiveServices
Private traffic → Private Endpoint
Human impact → Responsible AI

Interactive (2 min)

Ask:

“Who here would pick Azure ML for receipts?”

Then explain why that loses points.

10–25 min — Language, Bots & Speech (High ROI Section)

🔍 Why This Matters

This section appears constantly and has many trap answers.

Part A — Language Stack Decision Tree (10 min)

Draw this live:

User text
 ├─ Intent? → LUIS
 ├─ Knowledge base? → QnA Maker
 ├─ Sentiment / language? → Text Analytics

❌ Translator ≠ language detection ❌ Dispatch ≠ required unless routing bots

Exam Scenario Drill (5 min)

“Build a chatbot that:

Supports chit-chat

Uses a KB

Performs sentiment analysis

Selects best language automatically”

Ask teams to vote.

✅ Correct LUIS + QnA Maker + Text Analytics

🚫 Explain why Dispatch is tempting but wrong.

Part B — Speech Streaming (5 min)

Exam keyword: streaming MP3

Requirement	Correct
MP3	`GetCompressedFormat(MP3)`
STT	`SpeechRecognizer`

🚫 KeywordRecognizer 🚫 SpeechSynthesizer

25–40 min — Vision & Document Intelligence

🔥 One Rule to Drill In

“If it’s a form → Form Recognizer. If it’s pixels → Computer Vision.”

High-Frequency Matrix (Show + Explain)

Scenario	Service
Receipts	Form Recognizer
Invoices	Form Recognizer
PPE / mask	Face
OCR only	Computer Vision
Captions for blind	`describeImage`

Exam Trap Breakdown (10 min)

Question

“Extract vendor and total from receipts. Minimize effort.”

Walk through wrong answers:

❌ Custom Vision → training
❌ OCR → unstructured
❌ Personalizer → irrelevant

✅ Form Recognizer (prebuilt receipts)

40–55 min — Containers, Security & RBAC

🔐 Exam Keywords

Limited connectivity
On-prem
Secure keys
RBAC

Container Deployment Pattern (Must Memorize)

Correct Order

Pull from MCR
Push to ACR
Use RBAC
Distribute docker script
Run container

Mandatory Container Flags (Exam loves this)

Eula=accept
Billing={ENDPOINT}
ApiKey={KEY}

🚫 Missing Billing = container won’t start

Group Exercise (5 min)

“Why is Docker Hub wrong here?”

Expected answer:

No RBAC
Public access
Violates least privilege

55–70 min — Cognitive Search (Hard but Scoring)

Part A — Private Access (7 min)

Goal: No public internet

Solution	Correct
NSG	❌
Firewall	❌
IP rule	❌
Private Endpoint	✅

Explain why NSG doesn’t apply to PaaS.

Part B — CMK Encryption (5 min)

Impacts (Exam expects all 3):

Index size ↑
Query time ↑
Requires Key Vault

🚫 No performance improvement

Part C — Throttling Logic (3 min)

Fix	Works?
Add replicas	✅
Higher tier	✅
Add indexes	❌
CMK	❌

70–80 min — Responsible AI (Free Points)

Rule of Thumb

“If AI affects people → add a human.”

Exam Mapping

Scenario	Principle
Notify users	Transparency
Bias monitoring	Fairness
Accessibility	Inclusiveness
Salary decisions	Human-in-loop

Fast Drill (5 min)

Ask:

“Which principle is violated if users don’t know data is processed?”

Answer: Transparency

80–90 min — Full Exam Simulation

Group Challenge (10 min)

Split into 3–4 groups. Each group gets one full AI-102 style scenario.

They must:

Pick service
Eliminate wrong answers
Justify using keywords

Sample Prompt

“IoT sensors, correlated signals, RCA, alerts, minimal dev”

Correct: ✅ Azure Metrics Advisor

🧾 What You Should Send After the Workshop

I recommend providing:

✅ 1-page service decision cheat sheet
✅ Top 30 exam keywords
✅ Practice MCQs with explanations

Want Me to Deliver the Next Artifacts?

I can:

🧠 Add speaker notes per slide
❓ Create live Kahoot / Slido questions
📄 Generate a printable AI-102 exam cheat sheet
🎯 Build a mock exam (40 questions)