Microsoft Designing and Implementing a Microsoft Azure AI Solution AI-102 Exam Dumps: Updated Questions & Answers (July 2026)

Box 1: No

Example: Greet a user whose name is stored in `user.name`

- ${ welcomeUser(user.name) }

Example: Greet a user whose name you don ' t know:

- ${ welcomeUser() }

Box 2: No

Greet(User) is a Send a response action.

Box 3: Yes

Comprehensive Explanation

Speech-to-text converts the caller’s spoken French/German audio into text.

The text must then be translated to English.

Finally, Text-to-speech converts the translated text into spoken English for playback.

Therefore, the combination of Translator + Text-to-speech is required for the replay step.

Microsoft References

Speech-to-text in Azure AI Speech

Azure AI Translator

Text-to-speech in Azure AI Speech

Final Answer:

To capture messages → Speech-to-text

To replay messages → Text-to-speech and Translator

Comprehensive Detailed Explanation

The screenshot shows a Bot Framework Composer dialog that collects user input:

user.name is an entity.

False.

user.name is a property in bot state (a variable), not an entity.

Entities are part of LUIS/NLU models (e.g., PersonName, DateTime).

The dialog asks for a user name and a user age and assigns appropriate values to the user.name and user.age properties.

True.

The dialog explicitly has:

A Bot Asks (Text) step → assigns user’s response to user.name .

A Bot Asks (Number) step → assigns response to user.age .

So the values are stored in those properties.

The chatbot attempts to take the first non-null entity value for userName or personName and assigns the value to user.name.

True.

On the right-hand side, the Value expression is:

=coalesce(@userName, @personName)

coalesce returns the first non-null value.

This means if userName entity is available, it is used; otherwise, personName is used.

That result is assigned to user.name .

Correct Answers:

No

Yes

Yes

Microsoft References

Bot Framework Composer - Manage variables and properties

Bot Framework Composer - Recognizers and entities

Coalesce function in Composer expressions

For an Azure AI Language Custom Question Answering project (QA1), you can import Q & A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft’s guidance explicitly states that when importing question–answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q & A pairs in Language Studio. Microsoft Learn

Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn

Microsoft References:

Move projects and Q & A pairs – “Import question and answers… option to import either an Excel or TSV file.” Microsoft Learn

Format guidelines for Custom Question Answering – structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn

The requirement: Ensure the chatbot blocks questions that attempt to circumvent built-in safety features .

This is known as a jailbreak attempt (e.g., prompt injection, role-play tricking).

Azure AI Content Safety recently added Jailbreak risk detection for precisely this purpose.

Other options:

Protected material detection → for copyright/IP detection.

Monitor online activity → not related to OpenAI safety.

Moderate text content → general harmful content moderation (violence, hate, sexual, self-harm), but not jailbreaks.

Microsoft Reference:

Azure AI Content Safety jailbreak risk detection

The scenario involves storing text files, videos, audio streams, and virtual disk images , along with metadata and a unique ID. This exactly matches Object storage characteristics:

Stores large amounts of unstructured data (blobs).

Each object contains the data itself, metadata, and a unique identifier.

Azure Blob Storage is Microsoft’s object storage solution.

Other options:

Columnar → Used for analytics workloads, optimized for reading columns (e.g., data warehouses).

Key/Value → Good for simple lookups but not optimized for storing large multimedia files.

Document → Stores semi-structured JSON documents, not large binary objects.

Thus, the correct answer is Object storage .

Microsoft Reference: Azure Blob Storage (Object storage) overview

The requirement:

You are using Azure AI Anomaly Detector.

You need to deploy it on Server1, which has intermittent internet access.

This means you need to run the service locally using containers (Docker).

Step 1 – Install Docker Engine

Before pulling or running any containers, Docker must be installed on Server1.

Without Docker Engine, no container can be deployed.

Step 2 – Pull the container image

Use docker pull to download the Anomaly Detector container image from Microsoft Container Registry.

This requires internet access, so it should be done when connectivity is available.

Step 3 – Run the container

After pulling the image, use docker run to start the Anomaly Detector container.

This exposes a local REST API endpoint on Server1.

Step 4 – Query the local endpoint

Once the container is running, you can query the prediction endpoint on Server1.

This allows local inference without requiring continuous internet access.

Correct Sequence:

Install the Docker Engine on Server1.

From Server1, run the docker pull command.

From Server1, run the docker run command.

Query the prediction endpoint on Server1.

Microsoft References

Use Azure AI Anomaly Detector on containers

Deploy Azure AI services in Docker containers

Anomaly Detector container setup

To make the chatbot respond only when the answer’s confidence is ≥ 95%, you must set the request body property confidenceScoreThreshold to 0.95 in the Query knowledge bases operation of Custom question answering. The REST API for querying a Custom question answering project is:

POST { Endpoint }/language/ :query-knowledgebases?projectName= {projectName} & deploymentName={deploymentName} & api-version= 2021 - 10 - 01

Here, projectName identifies the Custom question answering project you’re querying (the screenshot’s placeholder < chatbot-project > ), while deploymentName specifies which deployment (for example, production ) to use. The request body includes confidenceScoreThreshold (0–1) to filter out answers below that score; setting it to 0.95 enforces the requirement.

Example snippet:

curl -X POST " $LANGUAGE_ENDPOINT /language/:query-knowledgebases?projectName= < chatbot-project > & deploymentName=production & api-version=2021-10-01 " \

-H " Ocp-Apim-Subscription-Key: $LANGUAGE_KEY " \

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

-d ' {

" question " : " How much energy does my phone have left? " ,

" confidenceScoreThreshold " : 0.95

} '

References

REST API: Question Answering – Get Answers (shows endpoint with projectName / deploymentName and the confidenceScoreThreshold request property). Microsoft Learn

REST API (newer version) parameter list for projectName / deploymentName . Microsoft Learn