AI-900 Decision Framework
A decision and keyword atlas designed to support the AI-900 Readiness Assessment
Use this framework when unsure during the assessment. Focus on Keywords, Ask Yourself, and Traps.
✨ Generative AI
Azure OpenAI
- Chat
- Text generation
- Summarization
- Code generation
Keywords: generate, summarize, write, chat, respond naturally
Ask yourself:
Is the system creating new content?
Is the system creating new content?
❌ Trap: If it's extracting facts → Not Generative AI
Embeddings
- Numerical representations of meaning
- Semantic similarity
- Clustering & vector search
Keywords: similarity, semantic, vector, meaning comparison
Ask yourself:
Are we comparing meaning, not keywords?
Are we comparing meaning, not keywords?
❌ Trap: Embeddings ≠ Tokens. Embeddings capture meaning; tokens are input units.
Tokens & Tokenization
- How a model reads text
- Text → Tokens → IDs → Processing
- Determines input limits and cost
Keywords: token limit, input size, length, context window
Ask yourself:
Is the question about limits, length, or input size?
Is the question about limits, length, or input size?
Prompt Engineering
- Writing better instructions for generative models
- AI‑900 scope: concept only (no syntax, no parameters)
Keywords: prompt, instruction, system message, few‑shot
Ask yourself:
Is it about improving how you ask the model?
Is it about improving how you ask the model?
❌ Trap: Prompt Engineering applies to Generative AI / Azure OpenAI only.
❌ Does NOT apply to AutoML, Vision, or Language.
❌ Does NOT apply to AutoML, Vision, or Language.
🔍 Search & Knowledge Mining
Azure AI Search
- Search documents
- Index data
- Semantic search
Keywords: search, index, retrieve, documents, knowledge base
Ask yourself:
Are users searching existing content?
Are users searching existing content?
❌ Trap: Search does not generate answers
Knowledge Mining (Hybrid)
- Search + AI enrichment
- Extract insights from large document collections
- Combines Azure AI Search with Language / Vision
Keywords: thousands of documents, discovery, insights, enrichment
Ask yourself:
Do we need to discover patterns across many documents?
Do we need to discover patterns across many documents?
🤖 Conversational AI
Azure Bot Service
- Chatbots
- FAQ bots
Keywords: chatbot, conversation, virtual agent
Ask yourself:
Is the user talking to a system conversationally?
Is the user talking to a system conversationally?
🛡️ Content Safety
Azure AI Content Safety
- Detect harmful content
- Text and image moderation
Keywords: harmful, abuse, hate, filtering
Ask yourself:
Is the goal to block or flag unsafe content?
Is the goal to block or flag unsafe content?
❌ Trap: Sentiment ≠ Safety
🧩 Azure AI Personalizer
Real‑Time Personalization
- Chooses the best action per user in real time
- Learns from user feedback
- Optimizes decisions over time (continuously improves)
Keywords: personalize, rank options, next best action, user behavior, contextual decisions
Ask yourself:
Is the system choosing one best option per user, not just predicting preferences?
Is the system choosing one best option per user, not just predicting preferences?
❌ Trap: Personalizer ≠ traditional recommendation model
❌ Personalizer ≠ static rules
✅ Personalizer = adaptive decision making
❌ Personalizer ≠ static rules
✅ Personalizer = adaptive decision making
Typical Use Cases
- Personalized product banners
- News or content ranking
- "Next best action" decisions
- Adaptive UI choices
👁️ Azure AI Vision
Image Classification
- What is in the image (label)
- Single category output
Keywords: classify, label, categorize image
Ask yourself:
Do we only need to know what it is?
Do we only need to know what it is?
Object Detection
- What + Where (bounding boxes)
- Can detect condition/state if visually inferable
- Examples: helmet worn, damage, open/closed
Keywords: locate, bounding box, identify defects, position
Ask yourself:
Do we need to know what AND where?
Do we need to know what AND where?
❌ Trap: OCR detects text only, not objects
Face Detection
- Detect faces + location
- Age, emotion, accessories
Keywords: face, detect person, identify
| Task | Output |
|---|---|
| Image Classification | What is in the image (label) |
| Object Detection | What + where (bounding box) |
| Face Detection | Faces + location |
📄 OCR vs Document Intelligence
OCR
- Extract raw text from images
- Printed and handwritten
- Photos, screenshots, scanned pages
Keywords: read text, extract text, screenshot, scanned
Ask yourself:
Is the goal to get raw text out of an image?
Is the goal to get raw text out of an image?
❌ Trap: OCR does NOT understand forms, tables, or meaning
Document Intelligence
- Extract structured information from documents
- Invoices, receipts, forms, contracts
- Key‑value pairs, tables, fields
Keywords: invoice, receipt, form fields, key‑value pairs, totals, structured
Ask yourself:
Do we need structured fields (not just raw text)?
Do we need structured fields (not just raw text)?
🚨 Exam trap: "Extract invoice totals and dates" → Document Intelligence, NOT OCR
| Service | Input | Output |
|---|---|---|
| OCR | Image with text | Raw unstructured text |
| Document Intelligence | Forms, invoices, receipts | Structured key‑value pairs, tables, fields |
💬 Azure AI Language
Named Entity Recognition (NER)
- Extracts named things from text
- People, organizations, locations
- Dates, money, quantities
Keywords: extract, entity, person, organization, date, structured facts
Ask yourself:
Are we pulling structured facts from text?
Are we pulling structured facts from text?
❌ Trap: NER extracts facts, it does NOT generate text
Sentiment Analysis
- Positive / Negative / Neutral
- Opinion mining
Keywords: sentiment, opinion, positive, negative, feedback
Ask yourself:
Are we measuring how people feel?
Are we measuring how people feel?
Key Phrases & Text Classification
- Extract important phrases from text
- Classify text into categories
Keywords: key phrase, classify text, topic, category
Ask yourself:
Do we need to identify important topics or categorize text?
Do we need to identify important topics or categorize text?
🎙️ Azure AI Speech
What it CAN do
- ✅ Speech‑to‑Text (transcription)
- ✅ Text‑to‑Speech (voice output)
- ✅ Speech translation
- ✅ Speaker recognition
Keywords: transcribe, dictate, voice, audio, speaker
Ask yourself:
Is the input or output audio?
Is the input or output audio?
What it CANNOT do
- ❌ Understand image context
- ❌ Extract entities from text (→ Language)
- ❌ Perform sentiment analysis (→ Language)
- ❌ Replace a chatbot alone
❌ Trap: Real‑time transcription ≠ Natural Language Understanding.
Speech = audio processing · Language = text understanding
Speech = audio processing · Language = text understanding
🔀 Speech vs Language vs Bots – Separator
| Service | Handles | Input/Output |
|---|---|---|
| Azure AI Speech | Transcription, TTS, translation, speaker ID | Audio |
| Azure AI Language | NER, sentiment, key phrases, classification | Text |
| Conversational AI (Bots) | Dialogs, conversation flow, user interaction | Conversation management |
🚨 Speech ≠ Language ≠ Chatbot. These are NOT interchangeable.
🌐 Text Translation
Azure AI Translator
- Converts text from one language to another
- Preserves meaning across languages
Keywords: translate, language conversion, multilingual, cross‑language text
Ask yourself:
Is the goal to convert text between languages, not create or analyze content?
Is the goal to convert text between languages, not create or analyze content?
❌ Translation ≠ text generation
❌ Translation ≠ speech‑to‑text (audio)
❌ Translation does NOT summarize or analyze sentiment
❌ Translation ≠ speech‑to‑text (audio)
❌ Translation does NOT summarize or analyze sentiment
Typical Use Cases
- Multilingual websites
- Customer support translation
- Global content distribution
🔗 Entity Linking
Extension of NER
- NER identifies: "Microsoft" → Organization
- Entity Linking connects it to the known entity in a database
- Disambiguates: "Apple" the company vs fruit
Keywords: disambiguate, link entity, knowledge graph, resolve
Ask yourself:
Do we need to connect a mention to a known entity, not just label it?
Do we need to connect a mention to a known entity, not just label it?
Why It Matters
- Improves search accuracy
- Helps build knowledge graphs
- Avoids duplicates across mentions
✅ Conceptual understanding only
❌ No implementation details required for AI‑900
❌ No implementation details required for AI‑900
📊 Core ML Patterns
Classification
- Predict a label / category
- Yes/No, type, class
Keywords: classify, predict category, label, yes/no, type
Ask yourself:
Is the output a category or label?
Is the output a category or label?
Regression
- Predict a number
- Price, amount, score, quantity
Keywords: predict number, price, amount, forecast, continuous
Ask yourself:
Is the output a numerical value?
Is the output a numerical value?
Clustering
- Group similar items
- No labels (unsupervised)
Keywords: group, similarity, unsupervised, segment
Ask yourself:
Are there no predefined categories?
Are there no predefined categories?
Anomaly Detection
- Detect unusual behavior
- Fraud, spikes, failures
Keywords: unusual, abnormal, spike, deviation, outlier, unexpected
Ask yourself:
Is the goal to find what looks different from normal?
Is the goal to find what looks different from normal?
Recommendation
- Product suggestions
- User preferences
Keywords: recommend, suggest, personalize
Ask yourself:
Is the system predicting what someone might like?
Is the system predicting what someone might like?
| Technique | Output | Use when |
|---|---|---|
| Classification | Label / category | Yes/No, type, class |
| Regression | Number | Price, amount, score |
| Clustering | Groups | No labels, similarity |
🚨 Trap: Same dataset, different objective → different technique
🧪 Azure Machine Learning
Automated ML (AutoML)
- Automatically tries multiple algorithms
- Optimizes model selection & parameters
- Less control, faster experimentation
Keywords: best model, automatic, optimize, quick experiment
Ask yourself:
Do you want the best model quickly without designing pipelines?
Do you want the best model quickly without designing pipelines?
ML Designer
- Visual drag‑and‑drop pipeline
- Explicit control over each step
- Full transparency
Keywords: visual, pipeline, drag‑and‑drop, control, steps
Ask yourself:
Do you want to define how the model is built?
Do you want to define how the model is built?
Model Versioning
- Model registration & versioning
- Experiment tracking
- Deployment history
Keywords: track versions, model registry, experiment, deploy history
Ask yourself:
Do you need to track multiple versions of a model?
Do you need to track multiple versions of a model?
❌ Trap: Azure AI Vision/Language do NOT manage model lifecycle.
❌ Azure OpenAI does NOT version your custom ML models.
❌ Azure OpenAI does NOT version your custom ML models.
Inference
- Using a trained model to make predictions
- The "production" phase of ML
Keywords: predict, inference, deploy, real‑time, batch scoring
Ask yourself:
Is the model already trained and now being used?
Is the model already trained and now being used?
| Feature | AutoML | ML Designer |
|---|---|---|
| Algorithm selection | Automatic | Manual |
| Speed | Faster | Slower |
| Control | Low | High |
| Visual pipeline | No | Yes |
| Phase | What happens |
|---|---|
| Training | Creating a model from data |
| Versioning | Tracking & comparing multiple model versions |
| Inference | Using a trained model to make predictions |
📏 Evaluation Metrics (Classification)
These are evaluation metrics, not algorithms. They measure how good a classification model is, derived from the confusion matrix (TP, FP, FN, TN).
🔥 Loss Function (Training)
- Measures how wrong the model is
- Used during training to improve the model
- Lower loss = better model
Examples: Regression → MSE · Classification → Cross Entropy
Exam cue: "Error used to TRAIN the model"
❌ Trap: Loss is a training metric, NOT an evaluation metric.
❌ Loss ≠ Accuracy / Precision / Recall / F1
❌ Loss ≠ Accuracy / Precision / Recall / F1
Accuracy
- How often the model is correct overall
- "Out of all predictions, how many did I get right?"
When useful: Balanced classes, errors have similar cost
Exam cue: "Balanced dataset, generic performance"
❌ Trap: 99% accuracy can be useless on imbalanced data — the model may miss all important cases.
Precision
- How correct your positive predictions are
- "When the model says YES, how often is it right?"
- Formula: TP / (TP + FP)
When important: False positives are costly (spam filters, fraud alerts)
Exam cue: "Avoid false alarms" · "Minimize false positives"
❌ Trap: High precision does NOT mean the model finds all positives.
Recall (Sensitivity)
- How many actual positives the model finds
- "Out of all real YES cases, how many did we catch?"
- Formula: TP / (TP + FN)
When important: False negatives are dangerous (disease, safety, fraud)
Exam cue: "Detect all cases" · "Do not miss positives"
❌ Trap: High recall often increases false positives.
F1 Score
- Balance between precision and recall
- Combines both into one score
- Formula: 2 × (Precision × Recall) / (Precision + Recall)
When best: Imbalanced datasets, both FP and FN matter
Exam cue: "Balance between detecting cases and avoiding false alarms"
❌ Trap: F1 does NOT replace precision or recall — it summarizes them.
| Focus | You care about | Exam wording |
|---|---|---|
| Precision | Fewer false positives | "Avoid false alarms" |
| Recall | Fewer false negatives | "Don't miss cases" |
| F1 | Balance of both | "Overall classification quality" |
| Accuracy | Overall correctness | "Balanced dataset, generic" |
🚨 These metrics apply to classification only.
❌ Regression uses MAE, MSE, RMSE.
❌ Clustering uses silhouette, cohesion.
❌ Regression uses MAE, MSE, RMSE.
❌ Clustering uses silhouette, cohesion.
| Concept | Used for | Question it answers |
|---|---|---|
| Loss | Training | How wrong am I? |
| Accuracy | Evaluation | How often am I right? |
| Precision | Evaluation | Can I trust positives? |
| Recall | Evaluation | Did I miss any positives? |
| F1 Score | Evaluation | Balance precision + recall |
🔥 Ultra‑fast memory trick:
- Loss → training → "how wrong"
- Accuracy → "overall correct"
- Precision → "don't cry wolf"
- Recall → "don't miss cases"
- F1 → "balance tradeoff"
| 🧪 Real Example: Disease Detection | |
|---|---|
| Precision | Don't say disease if healthy |
| Recall | Don't miss a sick patient |
👉 In disease detection, Recall is MORE important — missing a sick patient is worse than a false alarm.
| Item | What it is |
|---|---|
| Accuracy / Precision / Recall / F1 | Evaluation metrics |
| Loss (MSE, Cross Entropy) | Training metric |
| Logistic Regression / Decision Tree / Random Forest / Neural Network | Algorithms |
🚨 Loss = training metric. Metrics (Accuracy, Precision, Recall, F1) = evaluation.
🚨 Metrics do NOT train models. Metrics do NOT choose algorithms.
🚨 Metrics do NOT train models. Metrics do NOT choose algorithms.
📐 Evaluation Metrics (Regression)
These metrics evaluate regression models where the output is a number (price, amount, forecast). They are NOT used for classification.
MAE – Mean Absolute Error
- Average absolute difference between predicted and actual
- Same unit as target value
- Treats all errors equally
When preferred: Simple, interpretable metric. Outliers should NOT dominate.
Exam cue: "Average error" · "Simple measure" · "Error in same unit"
MSE – Mean Squared Error
- Average of squared differences
- Large mistakes punished much more
- Unit is squared (less intuitive)
When preferred: Large errors must be heavily penalized
Exam cue: "Penalize large errors" · "Squared error" · "Sensitive to outliers"
❌ Trap: High MSE may look "bad" even when most predictions are reasonable.
RMSE – Root Mean Squared Error
- Square root of MSE
- Penalizes large errors (like MSE)
- Same unit as target value (more interpretable)
When preferred: Penalize large errors AND keep original units
Exam cue: "Error in original units" · "Balance interpretability and severity"
| Metric | Penalizes large errors | Same unit as output | Sensitivity to outliers |
|---|---|---|---|
| MAE | ❌ No | ✅ Yes | Low |
| MSE | ✅ Yes (strong) | ❌ No (squared) | High |
| RMSE | ✅ Yes | ✅ Yes | High |
🚨 These metrics apply to regression only.
❌ Classification uses Accuracy, Precision, Recall, F1.
❌ Clustering uses silhouette, cohesion.
❌ Classification uses Accuracy, Precision, Recall, F1.
❌ Clustering uses silhouette, cohesion.
Exam shortcut:
- "Predict a number" → Regression
- "Average error" → MAE
- "Penalize large errors" → MSE
- "Same units + penalize large errors" → RMSE
📊 Evaluation Metrics (Clustering)
Clustering is unsupervised — there are no labels. Traditional metrics like accuracy or MAE do not apply.
Silhouette Score
- How well each point fits within its cluster (cohesion)
- How well it is separated from other clusters
Interpretation: High → well-defined clusters · Near zero → overlap · Negative → wrong cluster
Exam cue: "Evaluate clustering quality" · "How well clusters are separated" · "No ground truth labels"
Cohesion (Intra‑cluster similarity)
- How close together points are inside the same cluster
- High cohesion → good cluster
- Low cohesion → scattered cluster
Key idea: "Do the points in this cluster actually belong together?"
🚨 Cohesion is a concept, not a standalone Azure service.
🚨 Clustering does NOT use: Accuracy, Precision, Recall, F1, MAE, MSE, RMSE.
Exam shortcut:
- "Group similar items" / "No labels" → Clustering
- "Evaluate cluster quality" → Silhouette / Cohesion
- NOT classification or regression metrics
📈 RFM (Recency, Frequency, Monetary)
Customer Segmentation Technique
- Recency – How recently they interacted
- Frequency – How often they interact
- Monetary – How much they spend
Keywords: customer segmentation, marketing analysis, value grouping
Ask yourself:
Is the question about grouping customers by behavior, not prediction?
Is the question about grouping customers by behavior, not prediction?
❌ Trap: RFM = analytics / business logic.
❌ RFM ≠ Machine Learning service.
❌ RFM ≠ Azure AI Vision or Language.
❌ RFM ≠ Machine Learning service.
❌ RFM ≠ Azure AI Vision or Language.
⚖️ Responsible AI Principles (EXAM FAVORITE)
Responsible AI principles often invalidate otherwise "technically correct" options.
Fairness
Bias, equal treatment, demographic impact
Does the system disadvantage a specific group?
Reliability & Safety
Consistent behavior, fail safely, stability
Does the system behave predictably?
Privacy & Security
Personal data, access control, protection, sensitive data
Is sensitive data handled securely?
Transparency
Explainability, user awareness, explanation
Do users know AI is involved?
Accountability
Human oversight, responsibility
Is a human responsible for decisions?
Inclusiveness
Accessibility, different abilities, usable by everyone
Is the system usable by people with different abilities?
🚨 Trap: Accessibility issue → Inclusiveness.
Bias issue → Fairness. These are NOT the same.
Bias issue → Fairness. These are NOT the same.
⚙️ AI Solution Design Considerations
Beyond ethics — the exam also tests general AI design judgment.
Frequently Tested Considerations
- Accuracy vs Explainability
- Automation vs Human‑in‑the‑loop
- Latency & response time
- Cost of inference
- Risk of misuse or over‑automation
Ask yourself:
Is full automation appropriate — or should a human remain responsible?
Is full automation appropriate — or should a human remain responsible?
✅ Sometimes the "technically best" option is rejected due to risk or oversight concerns
Generative AI Workloads
- Microsoft uses the term "Generative AI workloads"
- Includes: text generation, summarization, chat, code generation
Maps to: ✅ Azure OpenAI
❌ Azure AI Language (NER, sentiment) is NOT a GenAI workload
❌ Azure AI Vision is NOT a GenAI workload
❌ AutoML is NOT a GenAI workload
✅ If it creates new content, it's a Generative AI workload
❌ Azure AI Vision is NOT a GenAI workload
❌ AutoML is NOT a GenAI workload
✅ If it creates new content, it's a Generative AI workload
✅ Final Mental Checklist (Use This in the Exam)
- What is the input? (text, image, audio)
- What is the output? (label, number, text, location)
- Is it extracting, comparing, or generating?
- Is there structure vs raw content? (Document Intelligence vs OCR)
- Is ethics, bias, safety, or accessibility mentioned?
If you answer all 5, the correct option is usually obvious.