Correct Answer:

Explanation:
Classification
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of common AI workloads", classification is a supervised machine learning technique used when the goal is to predict which category or class an item belongs to. In supervised learning, the model is trained with labeled data-data that already contains known outcomes. The system learns patterns and relationships between input features and their corresponding labels so it can predict future classifications accurately.
In the scenario provided - "A banking system that predicts whether a loan will be repaid" - the model's output is a binary decision, meaning there are two possible outcomes:
* The loan will be repaid (positive class)
* The loan will not be repaid (negative class)
This kind of problem involves predicting a discrete value (a label or category), not a continuous numeric output. Therefore, it perfectly fits the classification type of machine learning.
The AI-900 learning materials describe classification as being used in many real-world examples, including:
* Determining whether an email is spam or not spam.
* Predicting whether a customer will churn (leave) or stay.
* Detecting fraudulent transactions.
* Assessing medical test results as positive or negative.
By contrast:
* Regression predicts continuous numeric values, such as predicting house prices, temperatures, or sales revenue. It would not apply here because repayment prediction is not a numeric value but a categorical decision.
* Clustering is an unsupervised learning method that groups similar data points without predefined categories, such as segmenting customers by purchasing behavior.
Thus, based on Microsoft's Responsible AI and AI-900 study guide concepts, a banking system that predicts whether a loan will be repaid uses the Classification type of machine learning.

Correct Answer:

Explanation:

This question evaluates your understanding of how different Azure AI workloads correspond to specific tasks in image, text, and content generation scenarios, as explained in the Microsoft Azure AI Fundamentals (AI-
900) study guide and Microsoft Learn modules covering common AI workloads and Azure services.
* Generate a caption from a given image # Computer VisionThis is a computer vision task because it involves analyzing the visual elements of an image and producing descriptive text (a caption). Azure AI Vision provides image analysis and captioning capabilities through its Describe Image API, which uses deep learning models to recognize objects, scenes, and actions in an image and automatically generate natural-language descriptions (e.g., "A cat sitting on a sofa").
* Generate an image from a given caption # Generative AIThis task belongs to Generative AI, which focuses on creating new content such as text, code, or images based on prompts. Tools like Azure OpenAI Service with DALL-E can interpret text descriptions and generate realistic images that match the given caption. Generative AI is capable of creative synthesis, not just analysis, making it the appropriate category.
* Generate a 200-word summary from a 2,000-word article # Text AnalyticsText analytics (a subset of natural language processing) allows summarization, sentiment analysis, and entity recognition from large text corpora. Azure AI Language includes text summarization capabilities that condense long documents into concise summaries while preserving meaning and key information.

Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads," an anomaly detection workload is designed to identify data points or patterns that deviate significantly from what is expected or normal. These anomalies often indicate irregularities, faults, or potential issues that require attention.
In this scenario, the AI system monitors temperature data from a large machine. Normally, the machine operates within a predictable temperature range. When the AI detects sudden or unexpected temperature spikes or drops - behavior that does not match the historical pattern - it flags these occurrences as anomalies. This type of workload is fundamental in predictive maintenance and industrial monitoring, where it helps detect equipment failures, safety hazards, or energy inefficiencies before they escalate.
Microsoft's AI-900 curriculum emphasizes that anomaly detection workloads are often used in:
* Industrial IoT systems (detecting abnormal sensor readings or machine behavior)
* Finance (fraud detection or unusual transaction monitoring)
* Cybersecurity (detecting irregular network traffic or access patterns)
* Operations (identifying abnormal variations in production data)
The Azure service used for this purpose is Azure Anomaly Detector, part of Azure Cognitive Services, which uses advanced statistical and machine learning models to automatically detect outliers in time-series data such as temperature, pressure, or transaction logs.
By comparison:
* Computer vision handles image or video analysis.
* Knowledge mining extracts insights from large document collections.
* Natural Language Processing (NLP) interprets human language.
Thus, based on the official Microsoft AI-900 study guide and Microsoft Learn, the correct and verified answer is An anomaly detection workload, since detecting unusual temperature fluctuations precisely fits this AI workload type.

Correct Answer: B  🗳️

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Correct Answer: A,D  🗳️

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