How to Improve Caption Accuracy in Image Analysis API
This AI-102 practice question tests your understanding of implement computer vision solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Exhibit
Refer to the exhibit.
{
"url": "https://example.com/image.jpg",
"features": [
"tags",
"objects",
"caption"
],
"language": "en",
"model-version": "latest",
"gender-neutral-caption": true
}
You are calling the Azure AI Vision Image Analysis API with the above JSON request. The API returns a response that includes tags, objects, and a caption. However, the caption does not describe the image accurately. Which change should you make to improve the caption quality?
Exhibit
Refer to the exhibit.
{
"url": "https://example.com/image.jpg",
"features": [
"tags",
"objects",
"caption"
],
"language": "en",
"model-version": "latest",
"gender-neutral-caption": true
}
A
Change model-version to a newer version
Why wrong: The latest version is already being used.
B
Add 'read' feature to the request
Why wrong: The 'read' feature is for OCR, not for improving captions.
C
Set gender-neutral-caption to false
Gender-neutral captions may omit descriptive terms, reducing accuracy.
D
Set language to 'en-US'
Why wrong: The language 'en' is sufficient for English captions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set gender-neutral-caption to false
Setting `gender-neutral-caption` to false forces the Azure AI Vision Image Analysis API to generate captions that include gender-specific terms (e.g., 'man' or 'woman') instead of neutral terms like 'person'. This improves caption accuracy when the image clearly depicts a person whose gender is identifiable, because the default (true) may produce a vague description that fails to capture the subject's characteristics.
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.
✗
Change model-version to a newer version
Why it's wrong here
The latest version is already being used.
✗
Add 'read' feature to the request
Why it's wrong here
The 'read' feature is for OCR, not for improving captions.
✓
Set gender-neutral-caption to false
Why this is correct
Gender-neutral captions may omit descriptive terms, reducing accuracy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set language to 'en-US'
Why it's wrong here
The language 'en' is sufficient for English captions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume caption quality is always improved by newer models or additional features like OCR, when in fact the issue is a specific configuration parameter (`gender-neutral-caption`) that directly controls the level of detail in person descriptions.
Detailed technical explanation
How to think about this question
The `gender-neutral-caption` parameter (default: true) controls whether the API uses gender-neutral terms (e.g., 'person') or gender-specific terms (e.g., 'man', 'woman') in generated captions. Under the hood, the captioning model is trained on datasets with both types of labels; setting this parameter to false biases the model toward using gendered labels when confidence is high, which can produce more precise descriptions for images with clear gender cues. In real-world scenarios, this is critical for applications like accessibility tools where accurate subject identification matters, but it must be used carefully to avoid misgendering.
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.
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: Set gender-neutral-caption to false — Setting `gender-neutral-caption` to false forces the Azure AI Vision Image Analysis API to generate captions that include gender-specific terms (e.g., 'man' or 'woman') instead of neutral terms like 'person'. This improves caption accuracy when the image clearly depicts a person whose gender is identifiable, because the default (true) may produce a vague description that fails to capture the subject's characteristics.
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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