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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| 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 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 |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
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질문 # 220
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
Box 1: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
질문 # 221
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point
정답：
설명：
Explanation:
Graphical user interface, application Description automatically generated.
질문 # 222
Which Azure service can use the prebuilt receipt model in Azure Document Intelligence in Foundry Tools?
정답：A
질문 # 223
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
질문 # 224
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
This question evaluates understanding of clustering-an unsupervised learning technique explained in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore fundamental principles of machine learning." Clustering involves finding natural groupings within data without prior knowledge of output labels. The algorithm identifies similarities among data points and groups them accordingly, with each group (or cluster) containing items that are more similar to each other than to those in other groups.
* Organizing documents into groups based on similarities of the text contained in the documents # YesThis is a classic clustering application. In text analytics or natural language processing (NLP), clustering algorithms such as K-means or hierarchical clustering are used to group documents with similar content or topics. According to Microsoft Learn, "clustering identifies relationships in data and groups items that share common characteristics." Therefore, organizing text documents based on content similarity is a textbook example of clustering.
* Grouping similar patients based on symptoms and diagnostic test results # YesThis is another example of clustering. In healthcare analytics, clustering can be used to segment patients with similar health patterns or risks. The study guide emphasizes that clustering can "discover natural groupings in data such as customers with similar buying patterns or patients with similar clinical results." Thus, this task correctly describes unsupervised clustering because it does not involve predicting a known outcome but grouping based on similarity.
* Predicting whether a person will develop mild, moderate, or severe allergy symptoms based on pollen count # NoThis is a classification problem, not clustering. Classification is a supervised learning technique where the model is trained with labeled data to predict predefined categories (in this case, mild, moderate, or severe). Microsoft Learn clearly distinguishes between clustering (discovering hidden patterns) and classification (predicting predefined categories).
질문 # 225
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