AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A retail store wants to use an AI solution to automatically monitor security camera feeds and detect when a shelf is empty or if a person is in a restricted area. Which type of AI workload is best suited for this task?
⚠ Common exam trap
Many exam-takers confuse Anomaly Detection (a technique) with Computer Vision (a workload), thinking that detecting empty shelves is an anomaly, but the core task requires visual image processing, not just statistical outlier detection.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Computer Vision
Computer Vision is the correct AI workload because it enables the system to analyze video frames from security cameras to detect visual patterns such as empty shelves (object absence) or unauthorized persons in restricted areas (object presence and location). This workload uses image classification, object detection, and semantic segmentation to interpret visual data in real time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Natural Language Processing
Why it's wrong here
Natural Language Processing (NLP) is concerned with understanding and generating human language, including tasks such as entity extraction, sentiment analysis, and question answering. Security feeds are visual, not textual or spoken, so NLP has no input signal to work with. Therefore it cannot automatically monitor camera imagery.
When this WOULD be correct
A question asking which AI workload is used to analyze customer reviews, extract sentiment, or build a chatbot for customer service would have NLP as the correct answer.
- ✓
Computer Vision
Why this is correct
Computer Vision is the correct choice because it enables AI systems to analyze images and video streams by detecting objects, people, and activities in frames. Retail security monitoring tools use computer vision models to recognize suspicious behavior or shoplifting events in CCTV footage in near real time. This directly matches the requirement to automate analysis of visual security feeds.
- ✗
Speech Recognition
Why it's wrong here
Speech recognition, often called speech-to-text, converts spoken audio waveforms into written text and is designed for voice commands or transcription. Video surveillance camera frames contain no speech content, and even when audio is attached to footage, speech recognition would only transcribe conversations—it cannot interpret who is present or what they are doing. Thus it is irrelevant for visual security monitoring.
When this WOULD be correct
A question asks: 'A company wants to automatically transcribe customer service calls to analyze sentiment and keywords. Which AI workload is best?' Speech Recognition would be correct for converting audio to text.
- ✗
Anomaly Detection
Why it's wrong here
Anomaly detection is a broad data-science technique that flags statistically unusual patterns in numeric time-series or event logs, such as sudden spikes in sales or network traffic. It does not include the deep visual processing needed to segment objects, track movement, or classify actions in video frames. While it might later analyze alerts generated by vision models, it is not the primary AI workload for analyzing camera feeds.
When this WOULD be correct
Anomaly detection would be correct if the question asked about identifying unusual behavior in network traffic to detect cyberattacks, or spotting rare events in sensor data (e.g., sudden temperature spikes in a server room).
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Computer VisionCorrect answer▾
Why this is correct
Computer Vision is the correct choice because it enables AI systems to analyze images and video streams by detecting objects, people, and activities in frames. Retail security monitoring tools use computer vision models to recognize suspicious behavior or shoplifting events in CCTV footage in near real time. This directly matches the requirement to automate analysis of visual security feeds.
✗Natural Language ProcessingWrong answer — click to see why▾
Why this is wrong here
Natural Language Processing (NLP) is used for understanding and generating human language, not for analyzing visual data from security camera feeds to detect empty shelves or restricted areas.
★ When this WOULD be the correct answer
A question asking which AI workload is used to analyze customer reviews, extract sentiment, or build a chatbot for customer service would have NLP as the correct answer.
Why candidates choose this
Candidates may confuse the general concept of 'AI' with NLP, or think that monitoring involves understanding commands or descriptions, but the task is purely visual.
✗Speech RecognitionWrong answer — click to see why▾
Why this is wrong here
The task involves analyzing video feeds to detect visual patterns (empty shelves, restricted areas), which requires processing images or video, not audio. Speech Recognition is used for transcribing spoken language, not visual analysis.
★ When this WOULD be the correct answer
A question asks: 'A company wants to automatically transcribe customer service calls to analyze sentiment and keywords. Which AI workload is best?' Speech Recognition would be correct for converting audio to text.
Why candidates choose this
Candidates may confuse 'monitoring' with 'listening' or think that security cameras might involve audio, but the question specifies visual detection from camera feeds.
✗Anomaly DetectionWrong answer — click to see why▾
Why this is wrong here
Anomaly detection identifies unusual patterns in data (e.g., fraudulent transactions), but the task requires analyzing visual feeds to detect specific objects (empty shelves, people in restricted areas), which is a computer vision problem.
★ When this WOULD be the correct answer
Anomaly detection would be correct if the question asked about identifying unusual behavior in network traffic to detect cyberattacks, or spotting rare events in sensor data (e.g., sudden temperature spikes in a server room).
Why candidates choose this
Candidates may confuse 'detecting empty shelves' as an anomaly (unusual state) and overlook that the solution must process images, not just data patterns.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.