Free Practice Questions for Microsoft AI-102 Exam

Azure AI Video Indexer (VI) imposes limits on both file duration and file size:

Duration: Up to 6 hours for all presets; Basic Audio preset supports up to 12 hours.

Size: When uploading from your device, the limit is 2 GB (uploading by URL allows up to 30 GB).

Formats: MP4, AVI (video) and MP3 (audio codec/file) are supported.

Evaluating each file:

File1 – MP4, 34 mins, 1,500 MB: Within 6-hour duration and under 2 GB → Supported.

File2 – AVI, 500 mins (≈8h20m), 1,700 MB: Exceeds the 6-hour limit → Not supported.

File3 – MP3, 300 mins (5h), 980 MB: Under 6 hours (and also under the 12-hour Basic Audio limit) and under 2 GB → Supported.

File4 – MP4, 350 mins (≈5h50m), 2,800 MB: Duration is under 6 hours but size exceeds 2 GB for device uploads → Not supported (unless uploaded by URL, which the question doesn’t state).

Therefore, the files you can upload and analyze (under standard device upload constraints) are File1 and File3 only.

Microsoft References

Support matrix & limits — file duration (6h for all presets; 12h for Basic Audio), device upload size limit 2 GB; supported formats (MP4, AVI, WAV, etc.) and audio codecs including MP3. Microsoft Learn

Release notes — increase of duration limit from 4h to 6h (and 12h for Basic Audio). Microsoft Learn

Upload guidance — device upload 2 GB vs 30 GB via URL.

Inclusiveness: AI systems should empower everyone and engage people.

Direct Line Speech is a robust, end-to-end solution for creating a flexible, extensible voice assistant. It is powered by the Bot Framework and its Direct Line Speech channel, that is optimized for voice-in, voice-out interaction with bots.

A streaming workload means data is generated continuously and ingested in near real time.

Telemetry from edge/IoT devices is a classic example of streaming workloads because the devices constantly push data (sensor readings, logs, metrics).

A. sending transactions daily → batch workload (not streaming).

B. sending metadata every 30 minutes → periodic batch, not continuous streaming.

C. sending old transactions to archive → cold data archival, not streaming.

Correct Answer: D

Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an “entity for price” would not make the chatbot correctly answer “How much does < product > cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).

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

CLU entities vs. QnA authoring—separate features and workflows.

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

Comprehensive Detailed Explanation

To move a Question Answering project to a different Azure region, you migrate the project data and then re-deploy it in the target region:

Export the existing project (source region).

In Language Studio (Question Answering), you first export the project. This produces a package (JSON/ZIP) containing your knowledge bases, sources, synonyms, and settings. Exporting is the supported way to move a project between resources/regions.

Import the project into the new Language service instance (target region).

In the target region’s Language service instance, use Language Studio to import the previously exported file. This recreates the project (KBs and configuration) under the new resource.

Train and publish from the new instance.

After import, you must train (build) and then publish the project so that an endpoint is created under the new regional resource. Clients can then point to the new endpoint and key.

Actions like “enable custom text classification” or “regenerate the keys” are not required for moving a Question Answering project, and training/publishing from the original instance won’t deploy to the target region.

Microsoft Azure AI Solution References

Export and import Question Answering projects in Language Studio (move between resources/regions):

Microsoft Learn – Export and import projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/export-import-project

Train and publish a Question Answering project:

Microsoft Learn – Create, train, and publish projects (Question Answering) .

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/create-test-deploy

Language service overview and regional deployment concepts:

Microsoft Learn – What is Azure AI Language?

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

The question requires enabling the bot to respond to spoken requests with minimal effort. Among the options:

Cortana integration automatically adds speech input/output capabilities to the bot.

Direct Line Speech is another option but requires additional setup and coding, so not the minimal effort solution.

Microsoft Teams is primarily for text-based interactions (though can do voice calls, but not direct bot speech input).

Azure function + Speech would require more development overhead.

Thus, Cortana integration provides the simplest path.

To quickly prototype harmful-content detection (e.g., discriminatory language) with minimal development, you should provision an Azure AI Content Safety resource in the Azure portal and then try it in Content Safety Studio using “Moderate text content.” Content Safety Studio lets you test text moderation, tune thresholds, and export sample code without writing much code up front. Creating an Azure OpenAI resource or using “Protected material detection for text” isn’t required for abusive/hate moderation; “protected material” targets copyrighted/credential leakage scenarios, not general toxicity.

Microsoft References:

Quickstart: Analyze text content (use Moderate text content in the studio; prerequisite is to create a Content Safety resource). Microsoft Learn

Azure AI Content Safety overview

Box 1: Yes

http://localhost:5000/status : Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.

Box 2: Yes

The command saves container and LUIS logs to output mount at C:\output, located on container host

Box 3: Yes

http://localhost:5000/swagger : The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that ' s required.

According to the course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution1, the correct sequence of actions to run model1 on Host1 is:

Export model1 to Host1

Run the container

Configure disk logging

The explanation and references are as follows:

Export model1 to Host1: This step is required to deploy the custom speech-to-text model to the Docker host. You can use the Azure portal or the Azure CLI to export the model as a container image2.

Run the container: This step is required to start the container and run the model on the Docker host. You can use the Docker CLI to run the container image3.

Configure disk logging: This step is optional but recommended to monitor the performance and health of the container. You can use the Docker CLI to configure disk logging for the container4.