The answer is to reduce the number of training images and use a compact domain. Reducing the dataset size directly lowers the compute hours required for training, as Azure Custom Vision charges per training hour based on image volume. Using a compact domain further cuts costs by simplifying the model architecture, which demands fewer resources and shorter training cycles, making it ideal for edge deployment scenarios. On the AI-102 exam, this question tests your understanding of cost optimization within the Custom Vision service, often appearing as a multi-select item where distractors include upgrading to a higher performance tier or adding more iterations. A common trap is assuming more data always improves accuracy, but the exam emphasizes that a smaller, high-quality dataset paired with a compact domain balances cost and performance. Remember the mnemonic “Fewer Pixels, Smaller Bills” to associate reduced image counts and compact domains with lower Azure spend.
AI-102 Practice Question: Implement image and video processing solutions
This AI-102 practice question tests your understanding of implement image and video processing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Which TWO actions can reduce the cost of using Azure Custom Vision for image classification? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the compact domain for faster training.
Option B is correct because using the compact domain in Azure Custom Vision reduces model complexity and training time, which directly lowers compute costs. Compact domains are optimized for edge deployment and require fewer resources, making them more cost-effective for image classification tasks.
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.
✗
Include negative samples in the dataset.
Why it's wrong here
Negative samples improve accuracy but increase cost.
✓
Use the compact domain for faster training.
Why this is correct
Compact domain reduces training time and cost.
Related concept
Read the scenario before looking for a memorised answer.
✓
Reduce the number of training images.
Why this is correct
Fewer images reduce transaction costs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a larger image size for higher accuracy.
Why it's wrong here
Larger images require more compute.
✗
Increase the number of training iterations.
Why it's wrong here
More iterations cost more.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'faster training' with 'reduced cost' but may overlook that compact domains specifically lower resource consumption, while options like reducing images or iterations seem intuitive but are not explicitly cost-reduction features in the exam context.
Detailed technical explanation
How to think about this question
Azure Custom Vision's compact domains use a smaller neural network architecture (e.g., MobileNet or similar) that requires fewer floating-point operations (FLOPs) per image, reducing GPU/CPU usage. This is particularly beneficial for real-time or edge scenarios where inference speed and cost are critical, as the model can be exported to TensorFlow, ONNX, or CoreML for local execution, avoiding ongoing cloud inference charges.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Implement image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the compact domain for faster training. — Option B is correct because using the compact domain in Azure Custom Vision reduces model complexity and training time, which directly lowers compute costs. Compact domains are optimized for edge deployment and require fewer resources, making them more cost-effective for image classification tasks.
What should I do if I get this AI-102 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.
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Question Discussion
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