Speech synthesis, also known as text-to-speech (TTS), is the AI workload that converts written text into spoken words. In this case, the task is to generate narration audio from provided scripts for silent instructional videos.
Speech recognition performs the opposite function - it converts speech into text. Language modeling is for text understanding and prediction (e.g., GPT). Translation converts text between languages, not from text to audio.
Therefore, the most appropriate workload, according to Microsoft's AI-900 study material under the "Speech AI capabilities" section, is speech synthesis, which enables natural voice narration generation.

When interacting with the Azure OpenAI REST API, requests and responses are structured using the JSON format. According to the Microsoft Learn documentation on the Azure OpenAI REST API, JSON is the required format for sending prompts, configuration parameters (such as temperature, max_tokens, or top_p), and metadata to the API endpoint.
A typical request to an Azure OpenAI endpoint (for example, /openai/deployments/{model-name}/chat
/completions?api-version=2024-02-01) includes a JSON body similar to the following:
{
"messages": [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "Write a JavaScript function to add two numbers."}
],
"max_tokens": 200,
"temperature": 0.7
}
This JSON format allows for structured data exchange between the client and the server, ensuring that key- value pairs are properly parsed by the API.
* Option A (CSV): Used for storing tabular data, not suitable for API communication.
* Option C (XML): Though historically used in web services, Microsoft's modern APIs, including Azure OpenAI, rely on JSON.
* Option D (YAML): Common in configuration files but not for REST API payloads.
Therefore, per official Microsoft documentation and the AI-900 study guide, the correct and verified answer is B. JSON, as it is the standard format required when sending requests to the Azure OpenAI REST API endpoint.

According to Microsoft's AI-900 study guide and the Microsoft Learn module "Identify common types of AI workloads", computer vision is the branch of AI that enables machines to interpret and act upon visual inputs such as photos, videos, or camera feeds.
In this scenario, the recycling machine must identify bottles based on their shape and reject other objects. This requires the AI system to visually analyze an image or a video stream and make a decision based on shape recognition-a classic computer vision application.
Computer vision models are trained with labeled image data (e.g., images of correct bottle shapes vs. incorrect items). Using techniques such as object detection or image classification, the model learns to detect patterns and identify whether an image matches the desired object.
The other options are not suitable:
* A. Anomaly detection identifies unusual data patterns, not visual features.
* B. Conversational AI handles human-computer interactions via text or voice.
* D. Natural language processing (NLP) deals with understanding and generating human language.
Therefore, because the task requires visual recognition of bottle shapes, the appropriate AI workload is Computer Vision.
Reference:Microsoft Learn - Identify common types of AI workloads: Computer Vision, NLP, Conversational AI, and Anomaly Detection (AI-900 Learning Path)

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of computer vision workloads on Azure", computer vision models can perform different types of image analysis depending on the goal of the task. The main types include image classification, object detection, and semantic segmentation. Each method analyzes images at a different level of granularity.
* Image Classification # Separate images of polar bears and brown bearsImage classification assigns an entire image to a specific category or label. The model analyzes the image as a whole and determines which predefined class it belongs to. For example, in this case, the model would look at the features of each image and decide whether it shows a polar bear or a brown bear. The Microsoft Learn materials define classification as "assigning an image to a specific category."
* Object Detection # Determine the location of a bear in a photoObject detection identifies where objects appear within an image by drawing bounding boxes around them. This type of model not only classifies what object is present but also provides its location. Microsoft Learn explains that object detection
"detects and locates individual objects within an image." For instance, the model can detect a bear in a forest scene and highlight its position.
* Semantic Segmentation # Determine which pixels in an image are part of a bearSemantic segmentation is the most detailed form of image analysis. It classifies each pixel in an image according to the object it belongs to. In this scenario, the model identifies every pixel corresponding to the bear's body. The AI-
900 content defines this as "classifying every pixel in an image into a category." To summarize:
* Image classification # Categorizes entire images.
* Object detection # Locates and labels objects within images.
* Semantic segmentation # Labels each pixel for precise object boundaries.
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/