According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," object detection is a computer vision technique used to locate and identify objects within an image. It not only determines what objects are present but also where they appear in the image by returning bounding box coordinates around each detected item.
In this scenario, the goal is to identify the location of widgets within digital photos. This requires both recognition (knowing that the object is a widget) and localization (determining its position). The Custom Vision service in Azure allows you to train a model specifically for your own images, making it ideal for recognizing company-specific products such as widgets. By selecting the Object Detection domain in Custom Vision, you can label regions of interest in your training images. The model then learns to detect and locate those objects in new photos.
Let's examine the other options:
* A. Computer Vision Spatial Analysis: Used for people tracking, movement detection, and occupancy analytics in video streams - not for locating products in still images.
* C. Custom Vision classification: This model categorizes an image as a whole (e.g., "contains a widget" or "does not contain a widget") but does not locate objects within the image.
* D. Computer Vision Image Analysis: Provides general image tagging, description, and OCR capabilities but does not pinpoint object locations.

The Translator Text service (part of Azure Cognitive Services) provides real-time text translation across multiple languages. According to Microsoft Learn's AI-900 module on "Identify features of Natural Language Processing (NLP) workloads", translation is one of the four main NLP tasks, alongside key phrase extraction, sentiment analysis, and language understanding.
In this scenario, the company wants to make press releases available in a range of languages, which requires converting text from one language to another while preserving meaning and tone. The Translator Text API supports more than 100 languages and can be integrated into web apps, chatbots, or content management systems for automatic multilingual publishing.
The other options perform different functions:
* Text Analytics (B) extracts insights such as key phrases or sentiment but does not translate.
* Speech (C) focuses on converting between speech and text, not text translation.
* Language Understanding (LUIS) (D) identifies user intent but does not perform translation.
Therefore, to provide multilingual press releases, the appropriate service is A. Translator Text, which ensures accurate, fast, and scalable translation across global audiences.

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

The Azure AI Language service provides several NLP features, including language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).
When you need to extract specific data points such as dates, names, organizations, or locations from unstructured text (for example, social media posts), the correct feature is Entity Recognition.
Entity Recognition identifies and classifies information in text into predefined categories like:
* Person names (e.g., "John Smith")
* Organizations (e.g., "Contoso Ltd.")
* Dates and times (e.g., "October 22, 2025")
* Locations, events, and quantities
This capability helps transform unstructured textual data into structured data that can be analyzed or stored.
Option analysis:
* A (Language detection): Determines the language of a text (e.g., English, French).
* B (Speech recognition): Converts spoken audio to text; not applicable here.
* C (Key phrase extraction): Identifies important phrases or topics but not specific entities like names or dates.
* D (Entity recognition): Correctly extracts names, dates, and other specific data from text.
Hence, the accurate feature for this scenario is D. Entity Recognition.