Microsoft Updated AI-102 Exam Questions and Answers by eddison

You are building a Text-to-Speech (TTS) solution in Azure Cognitive Services Speech that must:

Convert up to 400 characters (short-form TTS, not long audio synthesis).

Provide multiple voice options to users.

Minimize costs (short-form TTS is cheaper than long-form neural synthesis).

To provide a list of available voices, Azure Speech exposes a REST API endpoint:

Correct endpoint:

https: / / < region > .tts.speech.microsoft.com/cognitiveservices/voices/list

This endpoint returns a list of all available voices (standard + neural) supported in the region, including locale, gender, and style.

Evaluating the Options:

Option A: /longaudiosynthesis/voices

Wrong. This is for long audio synthesis (narration/long documents).

More expensive and not needed for short social media messages.

Option B: /cognitiveservices/voices/list ✅

Correct. This endpoint lists available voices for short-form text-to-speech, which matches the requirements.

Option C: /cognitiveservices/v1?deploymentId={deployment}

Wrong. This is for using a custom voice deployment.

Not required here since the solution should minimize cost and use standard available voices.

Option D: /speechtotext/v3.0/models/base

Wrong. This is for speech-to-text (STT), not text-to-speech.

Correct Answer:

B. https://uksouth.tts.speech.microsoft.com/cognitiveservices/voices/list

Microsoft References

Azure Speech Service REST API reference

List voices in Azure Text-to-Speech

Comprehensive Detailed Explanation

Looking at the exhibit:

The GetWeather dialog is set up with a trigger phrase:

“what is the weather like in {city=Seattle}”

Here, city is an entity (ex: Seattle, New York, London, etc.).

1. User asks “what is the weather like in New York”

Since “New York” matches the city entity, the bot will correctly identify New York as a city entity.

It will not confuse it with a state entity, nor default to Seattle, because entity recognition extracts the correct city name dynamically.

Correct choice: Identify New York as a city entity

2. GetWeather dialog trigger

In Bot Framework Composer, the trigger type determines how the dialog is activated.

Since GetWeather is linked to the intent “GetWeather” from the Language Understanding model (LUIS/CLU), the trigger is Language Understanding Intent recognized.

This means whenever the user utterance maps to the GetWeather intent, the dialog starts.

Correct choice: Language Understanding Intent recognized

Correct Answer:

Identify New York as a city entity

Language Understanding Intent recognized

Microsoft References

Bot Framework Composer – Triggers

Entities in Conversational Language Understanding

Comprehensive Detailed Explanation

There are two main types of data workloads: Batch and Streaming.

Batch Workload

Processes data in large groups (batches) at scheduled intervals.

Common when real-time is not required.

Examples: daily sales reports, hourly data refreshes, periodic ETL (Extract, Transform, Load).

Streaming Workload

Processes data in real time or near real time as it arrives.

Used for scenarios where immediate insights or actions are required.

Examples: fraud detection, IoT sensor monitoring, live purchase tracking.

Applying to the scenarios:

“Data for a product catalog will be loaded every 12 hours to a data warehouse.”

This is scheduled, not continuous. → Batch

“Thousands of data sets per second for online purchases will be loaded into a data warehouse in real time.”

This requires real-time processing. → Streaming

“Updates to inventory data will be loaded to a data warehouse every 1 million transactions.”

Data is grouped and loaded after reaching a threshold (1 million). → Batch

Correct Matches:

Product catalog (12 hours) → Batch

Online purchases (real time) → Streaming

Inventory data (1 million transactions) → Batch

Microsoft References

Batch processing vs. Stream processing in Azure

Azure Stream Analytics overview

Data processing approaches: Batch vs Real-time

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.