- A
AI that automatically controls traffic lights to reduce congestion at intersections
Why wrong: Traffic signal control is smart city infrastructure AI — autonomous vehicles focus on individual vehicle perception and decision-making.
- B
Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI
Self-driving vehicles integrate multiple AI disciplines — perception, prediction, and planning working together in real time.
- C
AI that automatically parallel parks a car using sensors and pre-programmed rules
Why wrong: Parking assist uses sensors and rules — autonomous vehicles aim for full self-driving across all road conditions using ML.
- D
Using AI to optimise traffic routing in GPS navigation applications
Why wrong: GPS routing is navigation AI — autonomous vehicles AI focuses on vehicle perception, control, and decision-making.
Quick Answer
The correct answer is the combination of computer vision, sensor fusion, localization, behavior prediction, and path planning AI. This is correct because autonomous vehicles must perceive their environment through cameras and sensors, fuse that data into a coherent model, know their exact position, anticipate what pedestrians or other cars will do, and then plan a safe path forward—each step relying on a distinct AI technology. On the AI-900 exam, this concept tests your understanding of complex AI workloads that integrate multiple Azure AI services; a common trap is choosing an answer that lists only computer vision or only path planning, missing the holistic nature of the system. A useful memory tip is to think of the acronym “SCLBP”: Sensors, Computer vision, Localization, Behavior prediction, and Path planning—the five pillars that make a car drive itself.
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'autonomous vehicles' AI and what AI technologies do they combine?
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
Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI
Autonomous vehicles represent a complex AI workload that integrates multiple AI technologies to perceive the environment, understand context, and make safe driving decisions. Option B is correct because it specifically lists the core AI technologies—computer vision for object detection, sensor fusion for combining data from cameras, LiDAR, and radar, localization for precise positioning, behavior prediction for anticipating actions of other road users, and path planning for determining the optimal route—that are essential for a vehicle to operate without human intervention.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI that automatically controls traffic lights to reduce congestion at intersections
Why it's wrong here
Traffic signal control is smart city infrastructure AI — autonomous vehicles focus on individual vehicle perception and decision-making.
- ✓
Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI
Why this is correct
Self-driving vehicles integrate multiple AI disciplines — perception, prediction, and planning working together in real time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI that automatically parallel parks a car using sensors and pre-programmed rules
Why it's wrong here
Parking assist uses sensors and rules — autonomous vehicles aim for full self-driving across all road conditions using ML.
- ✗
Using AI to optimise traffic routing in GPS navigation applications
Why it's wrong here
GPS routing is navigation AI — autonomous vehicles AI focuses on vehicle perception, control, and decision-making.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse a single, narrow AI feature (like automatic parking or traffic routing) with the comprehensive integration of multiple AI technologies required for full autonomous driving, leading them to select options that describe simpler, isolated AI workloads.
Detailed technical explanation
How to think about this question
Autonomous vehicles typically use a modular pipeline where sensor fusion combines data from multiple sensor types (e.g., LiDAR point clouds, camera images, radar returns) into a unified representation, often using Kalman filters or deep learning models. Localization frequently relies on simultaneous localization and mapping (SLAM) algorithms to achieve centimeter-level accuracy, while behavior prediction models (e.g., using recurrent neural networks or transformers) forecast the trajectories of pedestrians and other vehicles up to several seconds ahead. Path planning then uses techniques like A* or model predictive control to generate a safe, collision-free trajectory that the vehicle's control system executes.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe Artificial Intelligence workloads and considerations — study guide chapter
Learn the concepts, then practise the questions
- →
Describe Artificial Intelligence workloads and considerations practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI — Autonomous vehicles represent a complex AI workload that integrates multiple AI technologies to perceive the environment, understand context, and make safe driving decisions. Option B is correct because it specifically lists the core AI technologies—computer vision for object detection, sensor fusion for combining data from cameras, LiDAR, and radar, localization for precise positioning, behavior prediction for anticipating actions of other road users, and path planning for determining the optimal route—that are essential for a vehicle to operate without human intervention.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.