AI-102 Implement content moderation solutions — 20 Questions

Question 1

A company uses Azure Content Moderator to review user-generated images in a social media app. Recently, the team noticed that images containing subtle adult content are not being flagged. What should they do to improve detection without increasing false positives?

Accepted Answer

Configure a human review team using the Review tool to manually inspect flagged content.. Option C is correct because Azure Content Moderator is designed to work with human review teams via the Review tool to handle edge cases where automated detection fails. By configuring a human review team, flagged images can be manually inspected to catch subtle adult content that the machine learning model misses, without lowering thresholds that would increase false positives. This approach leverages human judgment to improve detection accuracy while maintaining the existing automated moderation settings.

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Increase the moderation thresholds for adult content.

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Disable the adult classification tier to allow all images to pass through.

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Retrain the Content Moderator model with additional labeled images of adult content.

Question 2

A company uses Azure Content Moderator to moderate text in a chat application. They want to automatically reject messages that contain profanity or personal data. Which API should they use?

Accepted Answer

Text Moderation API. The Text Moderation API (D) is the correct choice because it is specifically designed to scan text content for profanity, personally identifiable information (PII), and other unwanted text patterns. This API returns a moderation score and a list of detected terms, enabling automated rejection of messages that violate the defined policies.

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Review API

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Video Moderation API

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Image Moderation API

Question 3

A company uses Azure Content Moderator to moderate user-generated content. They need to ensure that content moderation workflows comply with regional regulations. Which TWO actions should they take?

Accepted Answer

Deploy Content Moderator resources in the required geographic regions to meet data residency requirements.. Deploying Azure Content Moderator resources in the required geographic regions ensures that user-generated content is processed and stored within specific data boundaries, directly addressing data residency regulations. This is a fundamental compliance requirement because Azure resources are region-bound, and data does not leave the selected region unless explicitly configured otherwise.

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Use the free tier to reduce costs while meeting compliance needs.

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Enable geo-tagging on the Content Moderator API to automatically apply region-specific moderation.

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Configure the API to automatically reject any content that violates regional laws.

Question 4

An image was submitted to Azure Content Moderator's Image Moderation API. The application uses a threshold of 0.5 for Category1 (adult) to trigger a review. Based on the exhibit, what should the application do with this image?

Accepted Answer

Escalate the image to a human reviewer because Category1 score exceeds the threshold.. The application's threshold for Category1 (adult) is 0.5, and the exhibit shows a Category1 score of 0.6, which exceeds this threshold. Even though ReviewRecommended is false, the application's custom threshold overrides the default recommendation, requiring escalation to a human reviewer for manual judgment. This aligns with Azure Content Moderator's design where you can set your own thresholds to trigger reviews regardless of the API's built-in recommendation.

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Auto-approve the image because ReviewRecommended is false.

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Auto-reject the image because no offensive terms were found.

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Log the inconsistency and continue processing without action.

Question 5

You are the AI engineer at a global e-commerce company that allows users to upload product images and descriptions. You use Azure Content Moderator to automatically moderate images for adult and racy content, and text for profanity and personal data. Recently, you noticed that some product descriptions containing profanity in French are not being flagged. Your Content Moderator text moderation API call includes the language parameter set to 'eng'. The profanity list appears to be English-only. You have a requirement to support French and Spanish in addition to English. You also need to ensure that false positives for legitimate product descriptions are minimized. You cannot use a custom term list because the profanity terms are dynamic. What should you do?

Accepted Answer

Set the language parameter to 'auto-detect' in the text moderation API request.. Option C is correct because setting the language parameter to 'auto-detect' allows the Azure Content Moderator text moderation API to automatically identify the language of the input text and apply the corresponding built-in profanity list (including French and Spanish). This meets the requirement to support multiple languages without using a custom term list, and it minimizes false positives by using the appropriate language-specific moderation model rather than a generic English-only list.

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Disable the language parameter so that the API defaults to all languages.

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Create separate API calls for each language and specify the language code in the request.

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Add French and Spanish profanity terms to a custom term list and use the list in the API call.

Question 6

You are developing a content moderation solution that uses Azure Content Moderator to review images uploaded by users. The solution must flag images for potential adult content. Which TWO actions should you take to achieve this goal?

Accepted Answer

Configure a human review loop using the Review tool. Option C is correct because Azure Content Moderator's Review tool enables human-in-the-loop review, which is essential for accurately flagging adult content when automated confidence scores are borderline. This allows human moderators to confirm or override the automated classification, ensuring compliance with content policies. Option D is correct because the Evaluate operation on the image endpoint directly analyzes images for adult and racy content using pre-trained models, returning a confidence score that can trigger further action.

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Train a custom image classifier using Content Moderator's training API

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Use the List Management API to add images to a blocklist

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Use the OCR operation to extract text from images

Question 7

You are a content moderator for a social media platform that uses Azure Content Moderator. The platform has a custom blocklist of URLs (e.g., 'example.com/spam') and a custom term list for hate speech. Recently, users have been posting comments that contain a new form of hate speech not yet in the term list. The comments are being allowed through moderation. You need to update the solution to catch these new phrases as quickly as possible. What should you do?

Accepted Answer

Add the new phrases to the existing custom term list using the List Management API. Option C is correct because Azure Content Moderator's custom term lists allow you to dynamically add new offensive terms or phrases via the List Management API, which immediately updates the moderation screening without retraining or redeploying any model. This provides the fastest way to catch new hate speech patterns as they emerge, as the term list is checked in real-time during content review.

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Create a new custom model using Custom Vision to detect the new phrases in text

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Retrain the image classification model using the new phrases as training data

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Delete the existing term list and recreate it with the new phrases included