Explanation:

This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.

Explanation:

The correct answer is Azure AI Custom Vision.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore computer vision in Azure", the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.
The Azure AI Custom Vision service extends the capabilities of Azure's general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios-such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images-where general-purpose models may not suffice.
By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.
The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:
* Image Classification - categorizing entire images based on predefined tags.
* Object Detection - identifying and locating multiple objects within an image by drawing bounding boxes.
Therefore, when the question specifies "train an object detection model by using your own images," the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user's dataset.
Hence, the verified correct answer is: Azure AI Custom Vision.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaluate-model

Explanation:

Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
* Which companies were mentioned in a news article?
* Does a tweet contain the name of a person? Does the tweet also provide his current location?
* Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentimen