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

You might want to manage the translation and localization for the language understanding content for your bot independently.

Translate command in the @microsoft/bf-lu library takes advantage of the Microsoft text translation API to automatically machine translate .lu files to one or more than 60+ languages supported by the Microsoft text translation cognitive service.

What is translated?

An .lu file and optionally translate Comments in the lu file

LU reference link texts

List of .lu files under a specific path.

To deploy an agent and connect it to an existing Azure AI Search index (which already contains vectors), you first need the connection ID for the search resource. Using the Azure AI Projects/Agents client, you obtain that connection ID from your project’s connections. Next, you configure the Azure AI Search tool by specifying the index_connection_id , index_name , and (optionally) the query type (simple, semantic, vector, or hybrid), which supports vectorized indexes. After the tool is configured, you create the agent with the tool enabled (passing tools and tool_resources ). Finally, you validate the integration by starting a thread and asking questions grounded in the Azure AI Search index. This sequence matches the Microsoft guidance and minimizes effort because it reuses the existing index and only adds a configured tool to a new agent.

Microsoft References

Use an existing index with the Azure AI Search tool (sequence: create client → configure tool → create agent with tool → ask questions). Microsoft Learn

Azure AI Search tool overview (add the tool to an agent; supported search types including vector and hybrid). Microsoft Learn

AzureAISearchTool and AzureAISearchQueryType (parameters, including connection ID and query types: simple, semantic, vector, hybrid).

Vector and hybrid search in Azure AI Search (background for vector-enabled indexes and hybrid queries).

On the Azure AI Video Indexer website, uploading directly from your device is limited to 2 GB —your file is 20 GB , so this won’t work. Instead, the recommended approach is to upload the video to an online location and use the “Enter URL” option in the Video Indexer site. The service accepts a direct media-file URL (not a web page like YouTube) and supports uploads up to 30 GB via URL. Given the options provided, OneDrive is the only viable online storage choice listed to host the file before submitting its direct URL to Video Indexer. (Note: the URL must resolve directly to the media file; YouTube links are explicitly not supported, and an Azure Storage queue is not for storing video files.)

Microsoft References

Azure AI Video Indexer support matrix: 2-GB limit from device; 30-GB limit via URL ; URL must be a direct media file, not a webpage (e.g., YouTube). Microsoft Learn

Upload & index media in the Video Indexer website (“Enter URL” flow). Microsoft Learn

Considerations at scale: prefer URL upload over byte array; reiterates 2-GB vs 30-GB limits. Microsoft Learn

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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.

The requirement: A chatbot that answers based on a product knowledgebase (multiple PDFs) .

Best choice: Custom Question Answering in Azure AI Language .

It allows you to ingest documents like PDFs, FAQs, manuals.

Minimal development effort since you just upload documents and the service automatically indexes them.

Other options:

CLU → for intent/intent-based classification, not for knowledgebases.

Language detection → only detects language, not Q & A.

Azure OpenAI → could work, but more costly and requires prompt engineering (not the “minimize effort/cost” requirement).

Microsoft Reference:

Custom question answering in Azure AI Language

https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview#how-does-it-work

With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.

- Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data.

- Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products.

- Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended.

- Deploy a model. Once you ' re satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.

You need to identify industry-specific technical terms in documents. That is a classic use case for Custom Named Entity Recognition (custom NER) , which lets you build a model to extract domain-specific entities (for example, chemical names, part numbers, contract IDs) from unstructured text. Among the options, only custom NER is designed to recognize entities unique to your domain.

Why not Key Phrase Extraction? It returns the main concepts/talking points of text, not typed, domain-specific entities.

Why not CLU? Conversational Language Understanding is for intent and entity extraction from user utterances in conversations , not for mining technical terms from documents. Microsoft Learn

Why not Language Detection? It detects the language of the text only.

Custom NER fits: Purpose-built to extract domain-specific entities with minimal code; you label a small dataset and train in Language Studio.

Microsoft References:

Custom Named Entity Recognition overview – purpose is extracting domain-specific entities . Microsoft Learn

Transparency note for custom NER – emphasizes domain-specific extraction. Microsoft Learn

Key Phrase Extraction overview – identifies main concepts , not domain entities. Microsoft Learn

CLU overview – intent/entity for conversational scenarios. Microsoft Learn