The correct answer is B. Optical Character Recognition (OCR).
Optical Character Recognition (OCR) is a feature of Azure AI Vision that converts printed or handwritten text within images into machine-readable text. In this scenario, the goal is to read runner numbers on shirts from race photos. OCR can identify and extract these numbers, allowing them to be associated with specific participants.
Option analysis:
* A. Image classification: Categorizes entire images (e.g., "runner," "crowd"), not text.
* B. Optical Character Recognition (OCR) - # Correct. Extracts alphanumeric text from images.
* C. Object detection: Identifies and locates objects (e.g., shoes, cars) but doesn't read text.
* D. Facial recognition: Identifies individuals by matching facial features to known identities, not by reading numbers.
Therefore, to read and extract runner numbers from photos, the correct computer vision technique is Optical Character Recognition (OCR).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under the topic "Describe features of common AI workloads" and "Identify capabilities of Azure AI services", QnA Maker is the service designed specifically to create a knowledge base (KB) or question-and- answer bot from existing content such as FAQ documents, product manuals, support pages, or structured knowledge sources.
QnA Maker enables developers to take semi-structured text (for example, an FAQ document or webpage) and automatically generate a knowledge base of pairs of questions and corresponding answers. This knowledge base can then be connected to a chatbot, typically through the Azure Bot Service, so that users can interact with it conversationally. The key advantage is that the process does not require deep machine learning or programming expertise. The service uses natural language processing (NLP) to match user queries with the most relevant pre-defined answers in the knowledge base.
In the AI-900 curriculum, this falls under the Conversational AI workload-creating intelligent bots that can respond naturally to user questions. Microsoft's training content explains that "QnA Maker extracts pairs of question and answer from your content and builds a knowledge base that can be queried by bots and other applications." The output, as shown in the example diagram, demonstrates how user input (the question) triggers a request to the QnA Maker API, which returns a JSON response containing the best-matched answer.
The other options are not correct because:
* B. Language Understanding (LUIS) is used to interpret user intent and extract entities, not to create FAQs.
* C. Text Analytics performs text extraction, sentiment analysis, and key-phrase detection but does not build a Q&A knowledge base.
* D. Speech handles speech-to-text or text-to-speech, not Q&A matching.
Therefore, per the AI-900 study guide and Microsoft Learn, the verified and correct answer is A. QnA Maker.

Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of common AI workloads", there are three primary supervised and unsupervised machine learning types: Regression, Classification, and Clustering. Each type of learning addresses a different kind of problem depending on the data and desired prediction output.
* Regression - Regression models are used to predict numeric, continuous values. The study guide specifies that "regression predicts a number." In the scenario "Predict how many minutes late a flight will arrive based on the amount of snowfall," the output (minutes late) is a continuous numeric value.
Therefore, this is a regression problem. Regression algorithms like linear regression or decision tree regression estimate relationships between variables and predict measurable quantities.
* Clustering - Clustering falls under unsupervised learning, where the model identifies natural groupings or patterns in unlabeled data. The official AI-900 training material states that "clustering is used to find groups or segments of data that share similar characteristics." The scenario "Segment customers into different groups to support a marketing department" fits this description because the goal is to group customers based on behavior or demographics without predefined labels. Thus, it is a clustering problem.
* Classification - Classification is a supervised learning method used to predict discrete categories or labels. The AI-900 content defines classification as "predicting which category an item belongs to." The scenario "Predict whether a student will complete a university course" requires a yes/no (binary) outcome, which is a classic classification problem. Examples include logistic regression, decision trees, or neural networks trained for categorical prediction.
In summary:
* Regression # Predicts continuous numeric outcomes.
* Clustering # Groups data by similarities without predefined labels.
* Classification # Predicts discrete or categorical outcomes.
Hence, the correct and verified mappings based on the official AI-900 study material are:
* Regression # Flight delay prediction
* Clustering # Customer segmentation
* Classification # Course completion prediction

QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semistructured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base-automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

Generative AI focuses on creating new content rather than just analyzing existing data. As per Microsoft's AI-
900 curriculum and Azure OpenAI documentation, typical use cases include generating text, images, code, or other creative outputs based on input prompts.
* A. Generating draft responses for customer service agents - # Correct. GPT-based models can automatically generate draft replies to customer queries, enabling agents to refine responses and increase efficiency.
* B. Creating original artwork from textual descriptions - # Correct. DALL-E, available through Azure OpenAI, can produce unique images based on natural language prompts.
Options C and D are incorrect because they involve predictive or classification models, not generative ones:
* C. Predicting sales revenue # Regression (machine learning).
* D. Classifying email messages # Classification (machine learning).
Correct answers: A and B.