AI-102 Microsoft Designing and Implementing a Microsoft Azure AI Solution Free Practice Exam Questions (2026 Updated)

Comprehensive Detailed Explanation

When you develop a custom question answering project (formerly QnA Maker, now part of Azure Cognitive Service for Language – Question Answering), you can configure it for multi-turn conversations.

Follow-up prompts

Used to create multi-turn conversations.

They allow the system to present additional questions to the user after an initial question, guiding the conversation flow.

Example:

User: " Tell me about Azure Storage. "

Bot: " Which type of storage: Blob, File, Queue, or Table? "

These follow-up options are configured using follow-up prompts.

Correct choice for this question.

Active learning

Helps improve the knowledge base by suggesting alternative questions from user queries.

Not related to multi-turn conversation flow.

Alternate questions

Used to add different phrasings for the same question.

Example: “What is Azure Blob Storage?” and “Explain Blob Storage in Azure.”

This improves recognition but does not enable multi-turn conversations.

Chit-chat

Adds small talk and casual conversation handling (e.g., greetings, jokes).

Not related to multi-turn Q & A about a knowledge base.

Correct Answer: A. Add follow-up prompts

Microsoft References

Multi-turn conversations in Question Answering

Add follow-up prompts in Question Answering

To detect harmful content with Azure AI Content Safety using the .NET SDK, you instantiate a ContentSafetyClient with the endpoint and key. For text moderation, you construct an AnalyzeTextOptions object with the input string (and optionally specify categories, blocklists, etc.). You then call client.AnalyzeText(AnalyzeTextOptions options) and inspect the result (e.g., categories analysis and blocklist matches).

Code completed:

var client = new ContentSafetyClient(new Uri(endpoint), new AzureKeyCredential(key));

var request = new AnalyzeTextOptions( " what is the weather forecast for Seattle " );

Response < AnalyzeTextResult > response = client.AnalyzeText(request);

Microsoft Azure AI References

Azure AI Content Safety – SDK usage for text analysis with ContentSafetyClient and AnalyzeTextOptions (Analyze Text API).

Azure AI Content Safety – Concepts: text categories (hate/sexual/violence/self-harm) and blocklists, returned in AnalyzeTextResult .

1. api-nam.cognitive.microsofttranslator.com

2. translate

https://learn.microsoft.com/en-us/azure/cognitive-services/Translator/reference/v3-0-reference#base-urls

Requests to Translator are, in most cases, handled by the datacenter that is closest to where the request originated. If there ' s a datacenter failure when using the global endpoint, the request may be routed outside of the geography.

To force the request to be handled within a specific geography, use the desired geographical endpoint. All requests are processed among the datacenters within the geography.

- United States

api-nam.cognitive.microsofttranslator.com

https://learn.microsoft.com/en-us/azure/cognitive-services/translator/reference/rest-api-guide

- translate

Translate specified source language text into the target language text.

A data analyst

The statement is:

“________ is responsible for creating visuals and charts that help a company make informed decisions.”

Let’s analyze the options:

A data analyst

Main responsibility: interpreting data, creating visuals, dashboards, and reports using tools like Power BI, Excel, or Tableau.

Helps stakeholders make business decisions from data.

Correct answer.

A data engineer

Focuses on designing, building, and maintaining data pipelines, ETL processes, and storage systems.

Not primarily responsible for creating visuals.

A data scientist

Specializes in advanced analytics, machine learning, and statistical modeling.

They may present insights but their focus is on predictions and algorithms, not routine visuals.

A database administrator (DBA)

Responsible for managing databases, ensuring security, availability, backups, and performance.

Not responsible for creating visuals or dashboards.

Correct Answer: A data analyst

Microsoft Reference

Roles in data analytics

Microsoft Learn: Data Analyst role description — responsible for creating reports and visualizations to support decision-making.

Azure AI Content Safety supports custom blocklists —dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built-in way to meet this requirement; you don’t need to build a classifier.

References

Create and use blocklists in Azure AI Content Safety.

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/blocklists

Text moderation overview (using built-in classifiers with optional blocklists).

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/text-moderation

Conversational Language Understanding (CLU) is trained using utterances .

Adding these example phrases as utterances for the FindContact intent is exactly how training is done.

This ensures the model learns to recognize similar queries.

Correct Answer: Yes

You need to monitor a video stream while ensuring the microphone stays enabled and detect when the user asks a question. The simplest/lowest-effort approach is to use Azure AI Speech-to-text to transcribe the user’s audio in real time, then apply a lightweight check (e.g., interrogatives like “who/what/how” or final “?”) to detect a question. Object detection or the Face service analyze visual content (objects/faces/emotions) and cannot tell whether a spoken utterance is a question. Language detection only identifies which language is being spoken; it does not transcribe or classify questions.

References

Azure AI Speech: real-time speech-to-text (streaming) for transcription and downstream analysis.

https://learn.microsoft.com/azure/ai-services/speech-service/speech-to-text

Face service overview (not for speech/question detection).

https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity

Custom Vision object detection overview (visual objects, not spoken questions).

https://learn.microsoft.com/azure/ai-services/custom-vision-service/object-detection-overview

Language detection capability description (identifies language only).

https://learn.microsoft.com/azure/ai-services/language-service/language-detection/overview

According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.

According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.

According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.

Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don’t need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

Step 1: The Authorization Header

When calling Azure AI Document Intelligence REST API, the correct header to authenticate is:

-H " Ocp-Apim-Subscription-Key: {yourKey} "

Key1 and Secret are not valid header names.

Subscription-Key is also not the standard header.

Correct choice: Ocp-Apim-Subscription-Key

Step 2: Selecting the Prebuilt Model

Since the file contains tabular data (tables, structured document), the right prebuilt model is:

prebuilt-document → extracts text, tables, structure, key-value pairs.

prebuilt-contract → specialized for contracts.

prebuilt-layout → extracts layout, lines, and words but not tables in structured form.

prebuilt-read → simple OCR only.

So the correct choice is: prebuilt-document

Final Completed Command

curl -i -X POST " {endpoint}/formrecognizer/documentModels/prebuilt-document:analyze?api-version=2023-07-31 " \

-H " Content-Type: application/json " \

-H " Ocp-Apim-Subscription-Key: {yourKey} " \

-d " { ' urlSource ' : ' https:// < your-storage > /test.pdf ' } "

Verified Answer:

Left box: Ocp-Apim-Subscription-Key

Right box: prebuilt-document

Microsoft References:

Azure AI Document Intelligence REST API

Prebuilt Document model

Paginated reports are designed for printing or PDF generation, with table-style reports that span multiple pages.

These are built using Power BI Report Builder , a standalone tool.

B. Charticulator is for creating custom visuals, not paginated reports.

C. Power BI Desktop builds interactive reports, dashboards, and data models, not paginated reports.

D. Power BI Service is for publishing, sharing, and consuming reports, but not building paginated reports.

Correct Answer: A. Power BI Report Builder

The system must analyze multiple sensor data points (rotation speed, angle, temperature, pressure) and generate alerts on anomalies.

Application Insights → used for monitoring applications, not IoT sensor data.

Metric alerts in Azure Monitor → works with single metrics but does not handle complex correlations.

Univariate Anomaly Detection → only works on a single time series (e.g., just temperature).

Multivariate Anomaly Detection → specifically built to detect anomalies across multiple correlated variables (sensor data).

Thus, the correct choice is Multivariate Anomaly Detection.

The app must:

Identify road signs and extract text from images

This is an OCR (Optical Character Recognition) task.

Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).

Correct choice: Azure AI Vision.

Analyze the text to identify well-known locations

Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).

This is a Named Entity Recognition (NER) problem.

Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.

Correct choice: Azure AI Language.

Other options:

Azure AI Document Intelligence → Focused on structured documents (invoices, forms), not road signs.

Azure AI Search → Useful for indexing and search, not text extraction or entity recognition.

Correct Answer Matrix:

Extract the text → Azure AI Vision

Identify well-known locations → Azure AI Language

Microsoft References

Azure AI Vision – OCR text extraction

Azure AI Language – Named Entity Recognition (NER)

 HTTP Method: POST

 Visual Feature: imageType

Comprehensive Detailed Explanation

The question requires configuring an Azure AI Vision API request that determines if an image is clipart or a line drawing.

Step 1 – Determine the HTTP Method

The Analyze Image endpoint ( /analyze ) of Azure AI Vision expects an HTTP POST request.

GET is not valid for analysis with an image body.

PATCH is irrelevant here.

POST is correct because you are submitting image content or a URL for analysis.

To quickly improve a GPT-3.5 chatbot’s response quality with minimal development effort:

Provide grounding content (B): Ground the model with authoritative data (e.g., the travel agent’s policies, inventory, fares) using Azure OpenAI on your data or a lightweight RAG pattern. Grounding reduces hallucinations and makes answers more accurate and context-aware without retraining the model. This typically involves configuring a data connection (Azure Cognitive Search/Blob) and augmenting prompts—far less effort than training.

Add sample request/response pairs (C): Use few-shot examples directly in the system/user messages to demonstrate ideal answers, style, and formatting. Few-shot prompting is a core prompt-engineering technique that meaningfully boosts quality with minimal code changes or infrastructure.

Options A, D, and E involve model training or retraining (fine-tuning or building a custom LLM), which require data preparation, experimentation, monitoring, and higher cost/complexity—contrary to the “minimize development effort” constraint.

Microsoft References

Prompt engineering with examples (few-shot) to improve output quality:

https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering

Azure OpenAI “on your data” (grounding to improve relevance and accuracy):

https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data

Guidance comparing prompt engineering/grounding with fine-tuning and when to use each:

https://learn.microsoft.com/azure/ai-services/openai/concepts/fine-tuning

How-to: Fine-tuning (shows added complexity vs. prompt techniques):

https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning

Let’s carefully analyze the JSON output from the exported LUIS (Language Understanding) model.

The utterance is:

" average amount of rain by month at chicago last year "

The extracted entities are:

Weather.WeatherRange → " average " (positions 0–6)

Weather.WeatherCondition → " rain " (positions 18–21)

Weather.Historic → " by month " (positions 23–30)

We need to determine what text fragment corresponds to Weather.Historic .

Looking at the " startPos " : 23, " endPos " : 30 , these indexes map to the substring " by month " in the utterance.

Why not the other options?

B. chicago → Refers to a location entity, but the entity name is Weather.Location (not shown here).

C. rain → Already captured as Weather.WeatherCondition .

D. location → Would also be “chicago,” but again that’s a different entity type.

So the only correct match for Weather.Historic is " by month " .

Correct Answer: A. by month

Microsoft References

Entities in LUIS

Export LUIS model schema

Labeling utterances with entities

Comprehensive Detailed Explanation

In the provided LUDown file snippet:

Intents represent the purpose or action the user wants to perform.

Examples: ## Confirm , ## ExtractName , ## Logout , ## SelectItem , ## SelectNone .

These are all intent definitions.

So, SelectItem is an intent.

Utterances are the example phrases that users might say to invoke a particular intent.

Under ## SelectItem , one example utterance is:

choose { @DirectionalReference =top right}

This is an example phrase that maps to the SelectItem intent and contains the DirectionalReference entity.

Therefore, Choose {@DirectionalReference=top right} is an utterance.

Why not the other options?

Domain: Predefined category grouping multiple intents (not applicable here).

Entity: Extracted data within utterances (e.g., @DirectionalReference=top right or @personName ).

The entire phrase " choose {@DirectionalReference=top right} " is not an entity but an utterance containing an entity.

Correct Answers

SelectItem → an intent

Choose {@DirectionalReference=top right} → an utterance

Microsoft References

LUDown file format for LUIS

LUIS concepts: intents, utterances, and entities

When deploying Cognitive Services containers (such as Anomaly Detector), the container requires a billing parameter in the docker run command.

This parameter points to the Azure resource key and endpoint for billing.

Fluentd is for logging.

Http Proxy is optional for outbound connectivity.

Mounts are used for local storage but are not mandatory for billing.

Correct Answer: B

Comprehensive Detailed Explanation

You are working with a custom question answering project in Azure AI Language (formerly QnA Maker). The requirement is to enable the chatbot to engage in multi-turn conversations.

Multi-turn conversation: This occurs when a bot needs to ask clarifying questions or present follow-up options to guide the user toward the right answer.

Now let’s evaluate the options:

A. Add alternate questions

Alternate questions help improve query matching by allowing different phrasings of the same question.

This does not enable multi-turn dialogue.

B. Enable chit-chat

Chit-chat adds conversational personality (e.g., greetings, small talk).

Useful for user engagement but not for structured multi-turn dialogues.

C. Add follow-up prompts ✅

Follow-up prompts allow you to define next-step questions after an answer.

They are the key mechanism in custom question answering to enable multi-turn conversation flows.

Example: If the answer is about " subscriptions, " the follow-up prompts might include " How to upgrade? " or " How to cancel? "

D. Enable active learning

Active learning helps improve accuracy by suggesting alternative phrasing for questions based on user feedback.

This is for model refinement, not multi-turn conversation.

Thus, the correct way to configure the project for multi-turn conversations is to add follow-up prompts.

Correct Answer: C. Add follow-up prompts

Microsoft References

Custom question answering in Azure AI Language – Multi-turn conversations

Custom question answering overview