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Pass Guaranteed Quiz 2025 AI-900: Microsoft Azure AI Fundamentals Accurate Test Dumps.zip
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Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that aims to test an individual's knowledge of artificial intelligence (AI) concepts and how they can be applied to Microsoft Azure. AI-900 Exam covers a range of topics, including machine learning, natural language processing, computer vision, and cognitive services. Microsoft Azure AI Fundamentals certification is designed for professionals who are looking to understand the basics of AI and how it can be used to solve business problems.
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The Importance of Microsoft AI-900 Exam
Microsoft AI-900 exam is an associate level certification which is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017. Role of Azure Intelligent Solutions. Windows 2012, Windows 2016, and SQL Server 2017. .NET Framework 4.6.1 (or 4.7), or higher. Resident Deployment of the Solutions.Classify application workloads. Apply Azure monitoring and analytics, Operations Management Suite (OMS) logs, and Event Hubs. Manage the master database. Implement master database failover. Microsoft AI-900 exam dumps aligns with the objectives listed below.Classify application workloads. Deploy, configure, set up, activate, maintain, troubleshoot and support all necessary Azure services. Manage the master database.
Informationidentify the load of a given server. Forums and blogs about Azure and use of its products. Long term planning and development of future plans. Categoriesidentify the Azure deployment models. Forums and blogs about Azure and use of its products. Easy management of the Azure resources. Responsible for managing the database when the system doesn't work normally. Array of Solutionstests the knowledge of the candidate. Backgrounds of the candidate before they start their work. Push when you are in the process of testing.
Microsoft AI-900 Exam Syllabus Topics:
Topic
Details
Describe Artificial Intelligence workloads and considerations (20-25%)
Identify features of common AI workloads
- identify features of anomaly detection workloads
- identify computer vision workloads
- identify natural language processing workloads
- identify knowledge mining workloads
Identify guiding principles for responsible AI
- describe considerations for fairness in an AI solution
- describe considerations for reliability and safety in an AI solution
- describe considerations for privacy and security in an AI solution
- describe considerations for inclusiveness in an AI solution
- describe considerations for transparency in an AI solution
- describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (25-30%)
Identify common machine learning types
- identify regression machine learning scenarios
- identify classification machine learning scenarios
- identify clustering machine learning scenarios
Describe core machine learning concepts
- identify features and labels in a dataset for machine learning
- describe how training and validation datasets are used in machine learning
Describe capabilities of visual tools in Azure Machine Learning studio
- automated machine learning
- azure Machine Learning designer
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution
- identify features of image classification solutions
- identify features of object detection solutions
- identify features of optical character recognition solutions
- identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks
- identify capabilities of the Computer Vision service
- identify capabilities of the Custom Vision service
- identify capabilities of the Face service
- identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)
Identify features of common NLP Workload Scenarios
- identify features and uses for key phrase extraction
- identify features and uses for entity recognition
- identify features and uses for sentiment analysis
- identify features and uses for language modeling
- identify features and uses for speech recognition and synthesis
- identify features and uses for translation
Identify Azure tools and services for NLP workloads
- identify capabilities of the Language service
- identify capabilities of the Speech service
- identify capabilities of the Translator service
Microsoft Azure AI Fundamentals Sample Questions (Q65-Q70):
NEW QUESTION # 65
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 66
Match the types of computer vision 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://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 67
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:
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 # 68
For a machine learning progress, how should you split data for training and evaluation?
- A. Randomly split the data into columns for training and columns for evaluation.
- B. Use features for training and labels for evaluation.
- C. Use labels for training and features for evaluation.
- D. Randomly split the data into rows for training and rows for evaluation.
Answer: A
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 # 69
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:
NEW QUESTION # 70
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