Free Microsoft AI-102 Practice Exam with Questions & Answers | Set: 3

The Computer Vision API uses text recognition APIs to extract and recognize text information from images. Read uses the latest recognition models, and is optimized for large, text-heavy documents and noisy images.

When creating a voice talent profile for Custom Neural Voice in Azure Cognitive Services Speech, you must upload a consent recording .

The consent recording is a .wav or .mp3 file where the voice talent explicitly states that they consent to the creation of a synthetic version of their voice.

Audio samples and transcripts (training data) are uploaded later for training the model.

The profile itself requires the consent recording to comply with Microsoft’s responsible AI principles.

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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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

Comprehensive Detailed Explanation

Active learning in Language Understanding (LUIS / Conversational Language Understanding) works by collecting real user queries sent to your prediction endpoint.

By setting log=true in the prediction endpoint query, these user utterances are stored in the LUIS service.

The logged utterances can then be reviewed in the Review endpoint utterances page of the LUIS portal, where you can relabel and retrain the model. This enables the active learning loop.

Why not the others?

A. show-all-intents=true: This simply forces the endpoint to return all intents, not related to active learning.

B. Enable speech priming: Used for improving speech-to-text accuracy, unrelated to LUIS active learning.

D. Enable sentiment analysis: Adds sentiment scores to prediction output, not related to active learning.

- enable active learning

- validate the utterances

- train and republish

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log-user-queries-to-enable-active-learning

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-prebuilt-model

In Azure Stream Analytics, queries are defined using a SQL-like query language specifically designed for real-time analytics over streaming data.

The language is often referred to as Stream Analytics Query Language (SAQL).

It is very similar to T-SQL (Transact-SQL) but extended with additional features for temporal (time-based) operations, stream joins, aggregations over windows, and event handling.

With this language, you can perform operations like filtering, projections, aggregations, joins, and even machine learning model scoring on data streams.

Reviewing the options:

SQL – Correct. Azure Stream Analytics jobs are defined using a SQL-like language.

XML – Incorrect. XML is used for data representation, not defining queries in Stream Analytics.

YAML – Incorrect. YAML is commonly used for configuration files, not streaming queries.

KOL – Incorrect. This is not a relevant query language.

Correct Answer: A. SQL

Microsoft References

Introduction to Azure Stream Analytics query language

Query overview for Stream Analytics