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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
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NEW QUESTION # 211
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 212
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:
This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore natural language processing." NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language - both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.
* Monitoring online service reviews for profanities # YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: "Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content." Hence, this task is correctly classified as NLP.
* Identifying brand logos in an image # NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.
* Monitoring public news sites for negative mentions of a product # YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.
NEW QUESTION # 213
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 214
For a machine learning progress, how should you split data for training and evaluation?
Answer: C
Explanation:
Explanation
In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/
NEW QUESTION # 215
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Features
NEW QUESTION # 216
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