Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?
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.
A
Distractor review
Use the pre-built Computer Vision object detection API directly.
The pre-built object detection model can detect common objects like cars, people, and animals, but it is not trained to recognize penguins. Its accuracy would be very low for this specific use case.
B
Best answer
Train a Custom Vision object detection model using the labeled images.
Custom Vision enables training a specialized object detection model with a small set of labeled images. With only one object class ('penguin'), 500 images are more than sufficient to achieve good accuracy for detection and counting.
C
Distractor review
Use the Computer Vision Image Analysis API with the 'dense captioning' feature.
Dense captioning generates descriptions for regions of an image, but it is not designed for precise object detection or counting. It also relies on pre-trained models that do not include penguins.
D
Distractor review
Train a Custom Vision image classification model with the labeled images.
Image classification only tells whether a penguin is present in the image, not where it is or how many there are. For counting exact individuals, object detection or instance segmentation is required.
Common exam trap
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Technical deep dive
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
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Question 2
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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?
FAQ
Questions learners often ask
What does this AI-900 question test?
OSPF neighbours must agree on key parameters.
What is the correct answer to this question?
The correct answer is: Train a Custom Vision object detection model using the labeled images. — The pre-built Computer Vision object detection model is not trained to detect penguins, so a custom model is necessary. Custom Vision allows training with as few as 50 images per class (here just one class: penguin), and object detection is the right capability for counting and localizing. Image classification does not provide location, and semantic segmentation produces pixel masks but for counting, object detection is usually more efficient and requires less labeling effort. Training a custom model with Custom Vision is the most effective and cost-efficient given the small dataset.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
Discussion
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