AI-102 Exam Dumps - Designing and Implementing a Microsoft Azure AI Solution

Searchable → Field content is tokenized and added to the full-text index (supports free-text queries).

Retrievable → Field values can be included in search results.

Sortable → Used for ordering results, not required here.

Facetable → Used for aggregation/filtering by categories, not required here.

Filterable → Used for structured filters (e.g., WHERE conditions), not required here.

The requirement is to optimize the reading experience for users with dyslexia and learning differences .

Azure AI Immersive Reader is specifically designed to make text more accessible, improving comprehension by enabling features like text-to-speech, line focus, and translation.

Translator (A) is for translating text, not accessibility.

Document Intelligence (B) is for extracting information from documents.

Azure AI Language (D) is for NLP tasks, not improving reading accessibility.

Microsoft References:

Azure AI Immersive Reader overview

The chatbot needs to recognize two different kinds of information:

Code names for internal product development

These are not standard entities like " locations " or " organizations " that prebuilt NER can detect.

You need to train a custom model to recognize internal terms or codenames (for example, " Project Falcon " ).

The correct Azure Cognitive Service for Language feature is Custom Named Entity Recognition (Custom NER), which allows defining and training entity categories specific to your business.

Messages that include credit card numbers

Credit card numbers are sensitive data falling under Personally Identifiable Information (PII).

Azure Cognitive Service for Language provides a PII detection feature that automatically identifies and masks sensitive information such as credit card numbers, SSNs, and phone numbers.

This minimizes development effort since it is prebuilt and ready to use.

Correct Answer Mapping:

Identify code names for internal product development → Custom named entity recognition (NER)

Identify messages that include credit card numbers → Personally Identifiable Information (PII) detection

Microsoft References

Custom Named Entity Recognition in Azure AI Language

PII detection in Azure AI Language

For a phone-style bot that listens to customers and replies with spoken output, you use two core Azure AI Speech SDK objects:

SpeechRecognizer performs speech-to-text (STT), turning the caller’s audio into text that your app can use to query the orders database. This minimizes effort because it handles audio capture/streaming, language models, and returns recognized text events/results directly.

SpeechSynthesizer performs text-to-speech (TTS), converting the retrieved order status text into natural-sounding audio to play back to the customer. It supports neural voices and simple calls like SpeakTextAsync() .

Other options are not appropriate here:

TranslationRecognizer is for real-time speech translation, not needed for simple STT.

VoiceProfileClient is for speaker verification/identification, not for recognition or synthesis of the conversation content.

Microsoft References

Azure AI Speech SDK – Speech to text (SpeechRecognizer) overview and quickstarts.

Azure AI Speech SDK – Text to speech (SpeechSynthesizer) overview and quickstarts.

Azure AI Speech SDK concepts – recognition vs. synthesis pipelines.

The given data format is:

FirstName,LastName,Age,LeisureHobby,SportsHobby

John,Smith,23,Reading,Basketball

Ben,Smith,21,Guitar,Curling

This format uses commas to separate fields, with the first row as column headers and subsequent rows as records.

This is the definition of CSV (Comma-Separated Values) format.

Now let’s eliminate the other options:

JSON: Uses { } braces and key-value pairs like " FirstName " : " John " . Not shown here.

HTML: Uses < tags > for structure (e.g., < table > , < tr > , < td > ). Not shown here.

YAML: Uses indentation and key-value pairs (e.g., FirstName: John ). Not shown here.

Therefore, the correct format is CSV.

Correct Answer: A. CSV

Microsoft Reference

Common data formats: CSV, JSON, Avro, and Parquet

Box 1: QnA Maker Editor

Scenario: Provide all employees with the ability to edit Q & As.

The QnA Maker Editor (read/write) has the following permissions:

Create KB API

Update KB API

Replace KB API

Replace Alterations

" Train API " [in

new service model v5]

Box 2: Contributor

Scenario: Only senior managers must be able to publish updates.

Contributor permission: All except ability to add new members to roles

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text , you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream , because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams .

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text .

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.

https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library .

https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards