Free Practice Questions for Microsoft AI-102 Exam

The scenario:

You are building an in-vehicle application that uses Azure AI Speech for text-to-speech (TTS).

Requirement: Optimize the quality of synthesized voice output.

You must choose the most appropriate SSML (Speech Synthesis Markup Language) attribute.

Used to apply expressive styles like "cheerful," "empathetic," or "angry."

Helpful for emotion/tone, but not specifically about audio quality for clarity in motor vehicles.

Controls how strongly certain words are emphasized.

Improves meaning and focus, but does not optimize overall voice quality.

The element allows adjustments to pitch, rate, and volume.

Adjusting pitch directly affects the naturalness and clarity of the synthesized speech.

Especially useful in noisy environments such as cars, because modifying pitch can make speech stand out better against background noise.

This directly improves perceived quality of voice output.

Used for special voice effects (like whispering).

Not typically used for general clarity or quality improvements.

Option A – style attribute of the mstts:express-as elementOption B – level attribute of the emphasis elementOption C – pitch attribute of the prosody element ✅Option D – effect attribute of the voice element

Correct Answer: C. the pitch attribute of the prosody element

SSML support in Azure AI Speech

Prosody element in SSML

Microsoft References

The requirement is to process images of forms with Azure Form Recognizer (now part of Azure AI Document Intelligence) while ensuring that the image files are not stored in the cloud.

When sending images directly to the Form Recognizer REST API endpoint, you have two options:

Send the file as binary data in the request body (for local file upload).

Send a URL reference (if the file is stored in Azure Blob Storage or another public location).

Since the requirement explicitly states that the image files must not be stored in the cloud, you cannot send a URL. Instead, you must send the raw binary content of the file directly in the request body.

Option A – raw image binary

Correct. This allows you to directly send the local image file to the API without uploading/storing it in the cloud.

Option B – form URL encoded

Incorrect. Form Recognizer does not accept URL-encoded form data.

Option C – JSON

Incorrect. JSON is only used when providing the API with the URL of a cloud-stored document, not when sending the actual file.

Correct Answer: A. raw image binary

Form Recognizer REST API – Analyze a document

Quickstart: Extract layout and text – Document Intelligence"If your document is available locally, pass the file as binary data in the request body."

Microsoft References

To detect aggressive language (e.g., profanity/abuse) and sexually explicit content in text using the Content Moderator Text – Screen API:

classify = trueEnables machine-assisted text classification that returns category scores (e.g., sexually explicit/suggestive categories). This is required to identify sexual content.

language = (for example, eng)Specifies the language so profanity/abusive terms are detected correctly. The profanity detection in Text-Screen is language dependent.

autocorrect = trueHelps normalize intentionally misspelled or obfuscated words so that profanity or aggressive terms are still detected (e.g., “f@#$” → “fuck”).

Other parameters in the blade are not necessary for this goal:

PII targets personal data extraction, not aggression/sexuality.

listId is for matching against custom blocklists.

Headers like Ocp-Apim-Subscription-Key and Content-Type are required to call the API, but they are not the content-analysis settings that control detection behavior asked for in this question.

Microsoft Azure AI References

Azure AI Content Moderator – Screen text API parameters (autocorrect, language, classify) and category outputs.

Azure AI Content Moderator – Text moderation concepts: profanity detection and classification categories.