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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
| Topic 2: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Describe generative AI concepts - Identify responsible AI considerations for generative AI |
| Topic 3: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 5: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe core machine learning concepts - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities |
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NEW QUESTION # 57
Which Azure Cognitive Services service can be used to identify documents that contain sensitive information?
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common AI workloads," the Azure Form Recognizer service is part of Azure Cognitive Services for Document Intelligence. It enables organizations to extract, analyze, and identify information from structured and unstructured documents, including sensitive or confidential data such as names, addresses, financial figures, and identification numbers.
Form Recognizer uses optical character recognition (OCR) combined with machine learning to automatically extract key-value pairs, tables, and text fields from documents like invoices, receipts, contracts, and forms. It can be customized to identify and classify documents that contain specific sensitive data, allowing businesses to automate compliance and data governance tasks.
By contrast:
* A. Custom Vision is used for image classification and object detection - it analyzes visual data, not document content.
* B. Conversational Language Understanding (formerly LUIS) identifies intent and entities in text conversations, not document structure or sensitive data.
Form Recognizer is explicitly mentioned in the AI-900 course as the tool for document analysis and extraction. It can even integrate with Azure Cognitive Search or Azure Purview for further data management and compliance workflows.
Therefore, the verified and correct answer, aligned with Microsoft's official training content, is C. Form Recognizer, as it is the Azure Cognitive Service capable of identifying and processing documents containing sensitive information.
NEW QUESTION # 58
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 59
Which metric can you use to evaluate a classification model?
Answer: C
Explanation:
Explanation
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner.
Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
NEW QUESTION # 60
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and official Microsoft Learn modules under "Describe features of common AI workloads", Conversational AI refers to technology that enables computers to engage in dialogue or conversation with users through natural language, whether by text or speech. The interactive answering of user-entered questions through a chat interface or virtual assistant is a direct example of a conversational AI workload.
Microsoft defines Conversational AI as systems that use natural language processing (NLP) and language understanding models to interpret what users are asking and respond appropriately. This includes chatbots, virtual assistants (like Cortana or Azure Bot Service), and automated customer service systems that simulate a human-like conversation. In this case, when an application answers questions that a user types interactively, the AI model is processing human language inputs, deriving intent, and generating meaningful replies - precisely what conversational AI is designed to do.
By contrast:
* Anomaly detection identifies unusual patterns in data, typically used for fraud detection or equipment monitoring - not interactive dialogue.
* Computer vision deals with interpreting images or video (e.g., object detection, facial recognition), unrelated to answering text-based questions.
* Forecasting uses historical data to predict future trends or outcomes, often in sales or demand prediction scenarios.
The AI-900 guide emphasizes that Conversational AI helps businesses improve customer interaction efficiency by offering instant, automated, and consistent responses. It enables real-time engagement 24/7 and integrates with tools such as Azure Bot Service, Azure Cognitive Service for Language, and QnA Maker (now part of Azure AI Language Service).
Therefore, based on the Microsoft Learn objectives and definitions from the official AI-900 curriculum, the interactive answering of user questions in an application is best categorized as Conversational AI.
NEW QUESTION # 61
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
For each of the following statements select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
NEW QUESTION # 62
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