Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of the Computer Vision and Custom Vision services", the Custom Vision service is used to train, deploy, and improve custom image classification and object detection models using your own labeled data.
* Multilabel or Multiclass Selection - NOThe statement is false because the Multilabel or Multiclass choice applies only to image classification models, not object detection models. In image classification,
"Multiclass" means one label per image, while "Multilabel" means multiple labels per image. In contrast, object detection models identify and locate multiple objects in an image using bounding boxes; thus, this classification-type selection does not apply.
* Object Detection Locates Content in an Image - YESThis statement is true. The object detection functionality in Custom Vision is designed to both identify what objects appear in an image and determine their location through bounding box coordinates. For example, a model could detect and locate multiple products on a store shelf. Microsoft documentation describes object detection as
"identifying the presence and location of objects in an image."
* Predefined Domains - YESThis statement is true as well. When you create a new Custom Vision project, you must select a domain, which is a predefined optimization setting tailored to specific use cases such as retail, food, landmarks, or general images. These domains are designed to improve model accuracy by applying specialized transfer learning features based on the type of images you will analyze.
In summary:
* Classification type (Multilabel/Multiclass): No (only for classification models)
* Detect object location: Yes
* Choose predefined domain: Yes

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data.
In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories.
The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:
* Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).
* Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).
* Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation).
Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction.
Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.

According to the AI-900 study guide and Microsoft Learn module "Identify features of natural language processing workloads", Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. Tasks such as extracting key terms, summarizing documents, identifying topics, or determining sentiment fall under NLP workloads.
In this question, you have insurance claim reports stored as text, and you need to extract key terms to generate summaries. This matches the Text Analytics service in Azure Cognitive Services, which uses NLP techniques such as key phrase extraction to identify important concepts within textual data.
The other options are incorrect because:
* A. Conversational AI focuses on chatbots or dialogue systems.
* B. Anomaly detection identifies unusual data patterns, not textual meaning.
* D. Computer vision processes image or video content, not text.
Therefore, extracting key terms from documents is a clear example of Natural Language Processing.
Reference:Microsoft Learn - Identify features of Natural Language Processing (NLP) workloads

Explanation:

The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.
* Convert spoken requests into text # Azure AI SpeechThe Azure AI Speech service provides speech-to- text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.
* Identify the intent of a user's requests # Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents-the goals or actions a user wants to perform-and entities, which are key details within that request. For example, in the command "Book a flight to Paris," the intent is "book a flight," and the entity is "Paris."
* Apply intent to entities and utterances # Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.
In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities-a workflow essential in intelligent conversational applications.

The question describes a smart device that can understand and respond to a spoken or written question such as, "What is the stock price of Contoso, Ltd.?" This scenario directly maps to the Natural Language Processing (NLP) workload in Microsoft Azure AI.
According to the Microsoft AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Describe features of common AI workloads," NLP enables systems to understand, interpret, and generate human language. Azure AI Language and Azure Speech services are examples of NLP-based solutions.
In this case, the smart device performs several NLP tasks:
* Speech recognition - converts spoken input into text.
* Language understanding - interprets the user's intent, i.e., retrieving the stock price of a specific company.
* Response generation - formulates a meaningful answer that can be presented back as text or speech.
This process shows a full pipeline of natural language understanding (NLU) and conversational AI. It does not involve visual data (computer vision), data pattern analysis (anomaly detection), or document search (knowledge mining).
Hence, the correct AI workload is D. Natural Language Processing.