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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines |
| Topic 5: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI |
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NEW QUESTION # 62
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
This question tests understanding of AI workload types, a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) curriculum. Each workload type-Computer Vision, Natural Language Processing, Machine Learning (Regression), and Anomaly Detection-serves a specific function within the AI landscape, as explained in Microsoft Learn's module "Describe features of common AI workloads."
* Computer Vision enables computers to "see" and interpret visual information such as images or videos.
Identifying handwritten letters requires analyzing image patterns, shapes, and strokes, which is a classic image recognition task. Azure's Computer Vision API and Custom Vision services are specifically designed for such tasks.
* Natural Language Processing (NLP) involves interpreting human language, both written and spoken.
Determining the sentiment of a social media post (positive, negative, or neutral) is a typical text analytics use case within NLP, often implemented using Azure's Text Analytics for Sentiment Analysis.
* Anomaly Detection focuses on identifying data points that deviate from normal patterns. Detecting fraudulent credit card payments requires finding transactions that are unusual compared to historical spending behavior. Azure's Anomaly Detector API applies machine learning to identify such irregularities.
* Machine Learning (Regression) is used for predicting continuous numerical outcomes based on historical data. Estimating next month's toy sales is a regression problem-an example of supervised learning where the model predicts future sales values from past sales data.
Thus, based on Microsoft's official AI-900 learning objectives, the correct mapping of workloads to scenarios is:
* Computer Vision # Identify handwritten letters
* NLP # Predict sentiment
* Anomaly Detection # Fraud detection
* Machine Learning (Regression) # Predict toy sales
NEW QUESTION # 63
Match the Azure Al service to the appropriate generative Al capability.
To answer, drag the appropriate service from the column on the left to its capability on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
This question maps each Azure AI service to its correct capability based on the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn documentation on Azure Cognitive Services.
* Classify and label images # Azure AI VisionAzure AI Vision (formerly Computer Vision) provides capabilities to analyze visual content, detect objects, classify images, and extract information from pictures. It includes object detection, image classification, and tagging, which are core vision tasks.
This service enables businesses to build solutions that understand visual input, such as identifying products, reading signs, or detecting faces in images.
* Generate conversational responses # Azure OpenAI ServiceAzure OpenAI Service integrates powerful large language models such as GPT-3.5 and GPT-4, capable of generating human-like text responses, summarizations, translations, and dialogues. These models are designed for natural language generation (NLG) and conversational AI, making them ideal for chatbots, virtual agents, and intelligent assistants that produce dynamic, context-aware replies.
* Convert speech to text in real time # Azure AI SpeechAzure AI Speech provides speech-to-text capabilities (speech recognition) that convert spoken language into written text instantly. It is commonly used in transcription services, voice command systems, and live captioning applications.
Additionally, the Speech service supports text-to-speech (speech synthesis) and speech translation, making it versatile for voice-based AI applications.
By understanding each service's specialization-Vision for visual data, OpenAI for generative text, and Speech for audio processing-you can correctly match the capabilities.
NEW QUESTION # 64
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:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
NEW QUESTION # 65
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
NEW QUESTION # 66
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/
NEW QUESTION # 67
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