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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision |
| Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
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103. Frage
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.
Antwort:
Begründung:
Explanation:
104. Frage
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
105. Frage
Match the Al solution to the appropriate task.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Antwort:
Begründung:
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.
106. Frage
Which two resources can you use to analyze code and generate explanations of code function and code comments? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Antwort: A,B
Begründung:
The correct answers are C. the Azure OpenAI GPT-4 model and D. the GitHub Copilot service.
According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn documentation on Azure OpenAI and GitHub Copilot, both GPT-4 and GitHub Copilot can be used to analyze and generate explanations for code functionality, as well as produce or refine code comments.
* Azure OpenAI GPT-4 model (C):The GPT-4 model is a large language model (LLM) developed by OpenAI and available through the Azure OpenAI Service. It is trained on vast amounts of text, including programming languages, documentation, and natural language instructions. This enables it to interpret source code, explain what it does, suggest optimizations, and automatically generate detailed code comments. When prompted with code snippets, GPT-4 can provide structured natural language explanations describing the logic and intent of the code. In enterprise scenarios, developers use Azure OpenAI GPT models for code understanding, review automation, and documentation generation.
* GitHub Copilot service (D):GitHub Copilot, powered by OpenAI Codex, is an AI coding assistant integrated into IDEs such as Visual Studio Code. It can analyze code context and generate inline comments and explanations in real time. GitHub Copilot understands the syntax and intent of numerous programming languages and provides intelligent suggestions or explanations directly in the developer's environment.
The other options are not suitable:
* A. DALL-E is a generative image model for creating visual content, not text or code analysis.
* B. Whisper is an automatic speech recognition (ASR) model used for converting speech to text, unrelated to code interpretation.
Therefore, based on the official Azure AI and GitHub documentation, the correct and verified answers are C.
Azure OpenAI GPT-4 model and D. GitHub Copilot service.
107. Frage
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
Antwort: C
Begründung:
Section: Describe fundamental principles of machine learning on Azure
Explanation/Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/ introduction
108. Frage
......
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