AI-900 Practice Question: 'depth estimation' in computer vision and what…
This AI-900 practice question tests your understanding of 'depth estimation' in computer vision and what…. 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 'depth estimation' in computer vision and what are its applications?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
Distractor review
Measuring the depth of colour in an image (number of bits per pixel)
Bit depth is image colour encoding — depth estimation measures spatial distance of objects from the camera.
Distractor review
Analysing how deeply a subject is embedded in a complex background scene
Background complexity is an image composition concept — depth estimation measures real-world distance to objects.
Distractor review
Determining how much detail is captured in a photograph based on lens quality
Lens quality affects sharpness — depth estimation is a computer vision task for measuring object distances from imagery.
Best answer
Inferring the distance of objects from the camera to produce a spatial depth map
Depth estimation produces per-pixel distance measurements — enabling obstacle avoidance, 3D reconstruction, and AR scene understanding.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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Question 3
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Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
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FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Inferring the distance of objects from the camera to produce a spatial depth map — Depth estimation infers how far objects are from the camera — producing a depth map where each pixel has a distance value. It can use stereo cameras (two cameras like human eyes), structured light (LiDAR), or monocular depth estimation (single camera using learned cues). Applications: autonomous driving (obstacle distance), robotics (manipulation), AR/VR (scene understanding), and 3D reconstruction from photos.
What should I do if I get this AI-900 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-900 NAT questions on configuration and troubleshooting.
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