1.3 Identifying Common AI Workloads and Use Cases
Key Takeaways
- Prediction workloads use regression to forecast continuous values (prices, temperatures, quantities) from historical data.
- Anomaly detection identifies unusual patterns in data — fraud detection, equipment failure prediction, and quality control are common use cases.
- Computer vision workloads extract information from visual data — classification, detection, OCR, and facial analysis are the key subtypes.
- Conversational AI combines NLP and generative AI to create chatbots and virtual assistants that interact naturally with users.
- The exam tests your ability to match real-world scenarios to the correct AI workload type — focus on identifying the input data type and desired output.
Identifying Common AI Workloads and Use Cases
Quick Answer: The AI-900 exam expects you to identify seven common AI workload types: prediction/forecasting, classification, anomaly detection, computer vision, NLP, conversational AI, and generative AI. The key to identifying the correct workload is analyzing the input data and desired output in the scenario.
Workload Identification Framework
When the exam presents a business scenario, use this decision tree:
Step 1: What is the input?
- Images or video → Computer Vision
- Text → NLP or Generative AI
- Speech/audio → NLP (Speech) or Generative AI
- Numerical/tabular data → Machine Learning (regression, classification, or clustering)
- Time-series data → Anomaly Detection or Prediction
Step 2: What is the desired output?
- A continuous number (price, temperature, quantity) → Regression
- A category or label → Classification
- Groups of similar items → Clustering
- Unusual patterns flagged → Anomaly Detection
- New content (text, images, code) → Generative AI
- Understanding of text meaning → NLP
- Understanding of visual content → Computer Vision
- Natural conversation → Conversational AI
Detailed Workload Types with Use Cases
Prediction and Forecasting (Regression)
Regression models predict continuous numerical values based on historical data and input features.
| Use Case | Input Features | Predicted Output |
|---|---|---|
| House price prediction | Size, location, bedrooms, age | Price in dollars |
| Sales forecasting | Historical sales, season, promotions | Future sales volume |
| Temperature prediction | Date, location, historical weather | Temperature in degrees |
| Energy consumption | Time of day, season, building size | Kilowatt-hours |
| Delivery time estimation | Distance, traffic, weather | Minutes until delivery |
Classification
Classification models assign items to predefined categories based on their features.
| Use Case | Input | Categories |
|---|---|---|
| Email spam detection | Email content, sender, metadata | Spam / Not Spam |
| Medical image diagnosis | X-ray or MRI image | Disease / No Disease |
| Credit risk assessment | Income, credit history, employment | Approve / Deny |
| Product defect detection | Product image | Defective / Not Defective |
| Customer churn prediction | Usage patterns, payment history | Churn / Stay |
Anomaly Detection
Anomaly detection identifies data points that deviate significantly from expected patterns.
| Use Case | Normal Pattern | Anomaly |
|---|---|---|
| Credit card fraud | Typical spending patterns | Unusual large purchase in a foreign country |
| Equipment monitoring | Normal vibration and temperature | Sudden spike in vibration before failure |
| Network security | Normal traffic patterns | Spike in traffic from unknown source (DDoS) |
| Quality control | Consistent product dimensions | Product outside acceptable tolerance |
| Healthcare monitoring | Normal heart rhythm | Irregular heartbeat detected by wearable |
Computer Vision
Computer vision extracts information from images and videos.
| Task | Description | Example |
|---|---|---|
| Image classification | Assign a label to an entire image | "This is a photo of a cat" |
| Object detection | Identify and locate objects with bounding boxes | "There is a car at position (x, y, w, h)" |
| Semantic segmentation | Classify every pixel in an image | Autonomous driving — road, sidewalk, car, pedestrian |
| OCR | Extract text from images | Reading text from a scanned receipt |
| Facial detection | Detect and analyze human faces | Identifying age, emotion, and head pose |
| Facial recognition | Identify or verify a specific person | Unlocking a phone with Face ID |
Natural Language Processing (NLP)
NLP understands, interprets, and generates human language.
| Task | Description | Example |
|---|---|---|
| Sentiment analysis | Determine if text is positive, negative, or neutral | "Great product!" → Positive |
| Key phrase extraction | Identify main topics in text | "The hotel room was clean and spacious" → "hotel room", "clean", "spacious" |
| Named entity recognition | Identify entities (people, places, dates) | "John visited Paris on March 5" → Person: John, Location: Paris, Date: March 5 |
| Language detection | Identify the language of text | "Bonjour le monde" → French |
| Text summarization | Condense long text into key points | Summarize a 10-page article into 3 sentences |
| Speech-to-text | Convert spoken words to written text | Voice dictation |
| Text-to-speech | Convert written text to spoken audio | Screen reader for visually impaired users |
| Translation | Convert text from one language to another | English → Spanish |
Conversational AI
Conversational AI builds intelligent chatbots and virtual assistants that interact naturally with users.
| Use Case | Description |
|---|---|
| Customer service chatbot | Answer FAQs, process orders, handle complaints |
| IT help desk bot | Reset passwords, troubleshoot issues, create tickets |
| Healthcare triage bot | Assess symptoms, recommend next steps, schedule appointments |
| E-commerce assistant | Product recommendations, order tracking, returns |
| Internal knowledge bot | Answer employee questions from company documents |
Generative AI
Generative AI creates new content based on prompts or input data.
| Use Case | Input | Output |
|---|---|---|
| Content writing | "Write a product description for..." | Marketing copy |
| Code generation | "Write a Python function that..." | Working code |
| Image generation | "A sunset over a mountain lake" | Generated image |
| Summarization | Long document | Concise summary |
| Translation | Text in English | Text in Japanese |
| Q&A over documents | Question + context documents | Grounded answer |
On the Exam: The most common question pattern is: "A company needs to [business scenario]. Which type of AI workload is this?" Identify the input type and desired output to determine the correct workload. If the scenario involves creating new content, it is generative AI. If it involves understanding existing content, it is NLP or computer vision.
A manufacturing company wants to monitor sensor data from factory equipment to detect when machines are about to fail. Which AI workload is most appropriate?
A real estate company wants to predict house prices based on features like square footage, number of bedrooms, and location. Which AI workload type is this?
An HR department wants to automatically sort incoming job applications into categories: "Engineering", "Marketing", "Sales", and "Operations" based on resume content. Which AI workload is this?
A company wants an AI system that can write personalized marketing emails based on customer data and a brief prompt. Which AI workload type is this?
Put these AI concepts in order from BROADEST to MOST SPECIFIC:
Arrange the items in the correct order