The correct pre-processing step is to adjust brightness and contrast using image processing techniques. Poor lighting reduces the contrast between text and its background, which degrades the signal-to-noise ratio that OCR engines like Azure Computer Vision rely on to distinguish character edges. By normalizing brightness and enhancing contrast, you effectively sharpen the boundary between text and background, directly compensating for the lighting deficiency without altering the underlying image content. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of how image pre-processing fits into the OCR pipeline—a common trap is to jump to more complex solutions like retraining the model or switching to a different API, when a simple image adjustment is sufficient. Remember the memory tip: “Brightness and contrast fix the lighting contrast” to recall that poor lighting calls for these two adjustments before any other OCR troubleshooting.
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision 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.
A retail company uses Azure Computer Vision to analyze customer traffic in stores. They process images from security cameras using the OCR API to detect product labels. Recently, the OCR accuracy has decreased for images with poor lighting. Which pre-processing step should the company implement to improve OCR accuracy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Adjust brightness and contrast of images using image processing.
Option C is correct because poor lighting directly reduces the contrast between text and background, which is critical for OCR accuracy. Adjusting brightness and contrast improves the signal-to-noise ratio of the text regions, making character edges more distinct for the Azure Computer Vision OCR engine. This pre-processing step compensates for the lighting deficiency without altering the fundamental image content that the API relies on.
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.
✗
Convert images to grayscale before sending to OCR API.
Why it's wrong here
OCR API already handles grayscale conversion internally.
✗
Increase the image resolution before calling OCR API.
Why it's wrong here
Higher resolution does not address lighting issues; may increase processing time.
✓
Adjust brightness and contrast of images using image processing.
Why this is correct
Improves visibility of text in low-light conditions, enhancing OCR accuracy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce image size to decrease noise.
Why it's wrong here
Smaller images may lose text details, harming OCR accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image quality improvements (like resolution or noise reduction) with the specific need to correct lighting-induced contrast loss, which is a distinct pre-processing requirement for OCR in poor illumination.
Detailed technical explanation
How to think about this question
Azure Computer Vision's OCR API internally applies adaptive thresholding and edge detection algorithms that rely on sufficient local contrast. By adjusting brightness and contrast, you effectively normalize the pixel intensity distribution, which helps the API's binarization step produce cleaner character segmentation. In real-world scenarios, a gamma correction or histogram equalization step before calling the API can yield a 15-20% improvement in character recognition rates for low-light images.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adjust brightness and contrast of images using image processing. — Option C is correct because poor lighting directly reduces the contrast between text and background, which is critical for OCR accuracy. Adjusting brightness and contrast improves the signal-to-noise ratio of the text regions, making character edges more distinct for the Azure Computer Vision OCR engine. This pre-processing step compensates for the lighting deficiency without altering the fundamental image content that the API relies on.
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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