Microsoft Updated AI-102 Exam Questions and Answers by kasper

The requirement: A chatbot that answers based on a product knowledgebase (multiple PDFs) .

Best choice: Custom Question Answering in Azure AI Language .

It allows you to ingest documents like PDFs, FAQs, manuals.

Minimal development effort since you just upload documents and the service automatically indexes them.

Other options:

CLU → for intent/intent-based classification, not for knowledgebases.

Language detection → only detects language, not Q & A.

Azure OpenAI → could work, but more costly and requires prompt engineering (not the “minimize effort/cost” requirement).

Microsoft Reference:

Custom question answering in Azure AI Language

Comprehensive Detailed Explanation

You are asked to construct a URI for the Microsoft Translator API call that:

Translates webpage content into Greek (el).

Returns a transliteration in the Roman alphabet (Latin script).

The base URI given is:

https: //api.cognitive.microsofttranslator.com/translate?api-version=3.0

Now, let’s evaluate the required query parameters.

1. to=el

This specifies the target language for translation.

Since the requirement is to translate into Greek, we must include:

to =el

Correct

2. textType=html

The source content is from a webpage.

Translator API requires textType=html for web page or HTML-formatted content.

If omitted, the default is plain text.

Correct

3. toScript=Latn

By default, Greek text is written in its native Greek script.

To provide a transliteration in the Roman alphabet, we must set toScript=Latn .

Latn = Latin script.

Correct

Incorrect Options

A. toScript=Cyrl

Cyrl = Cyrillic script. Not required here.

B. from=el

This would mean the source language is Greek.

In the requirement, the target is Greek, so this is incorrect.

E. textType=plain

Incorrect, since we are translating HTML webpage content, not plain text.

Correct Answer: C. textType=html, D. to=el, F. toScript=Latn

Microsoft References

Translator Text API v3.0 Reference – Translate

Language and Script Parameters (to, from, toScript)

Scenario Recap

You are building a remote training monitoring solution.

Requirement: Use video and audio feeds to detect if a learner is present, paying attention, and talking.

Services available: Face, Speech, Text Analytics.

Analysis

From a learner’s video feed, verify whether the learner is present.

The Face API can detect and identify faces in a video feed.

It can tell if a person is present and recognized, fulfilling the requirement.

From a learner’s facial expression in the video feed, verify whether the learner is paying attention.

Again, the Face API provides facial expression and emotion recognition (happiness, anger, neutral, etc.).

This can be mapped to “paying attention vs. distracted.”

From a learner’s audio feed, detect whether the learner is talking.

The Speech service detects spoken input and can determine if speech is present.

Text Analytics works on text (not raw audio) and is therefore not appropriate here.

Final Answer (Answer Area Selections)

From a learner’s video feed, verify whether the learner is present: Face

From a learner’s facial expression in the video feed, verify whether the learner is paying attention: Face

From a learner’s audio feed, detect whether the learner is talking: Speech

Microsoft References

Face API – Face detection & identification

Face API – Emotion recognition

Azure Speech service