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Exam Ref AI-900 Microsoft Azure AI Fundamentals [Pehme köide]

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  • Formaat: Paperback / softback, 208 pages, kõrgus x laius x paksus: 230x190x10 mm, kaal: 380 g
  • Sari: Exam Ref
  • Ilmumisaeg: 23-Feb-2022
  • Kirjastus: Addison Wesley
  • ISBN-10: 0137358032
  • ISBN-13: 9780137358038
Teised raamatud teemal:
Exam Ref AI-900 Microsoft Azure AI FundamentalsSuurem pilt
  • Formaat: Paperback / softback, 208 pages, kõrgus x laius x paksus: 230x190x10 mm, kaal: 380 g
  • Sari: Exam Ref
  • Ilmumisaeg: 23-Feb-2022
  • Kirjastus: Addison Wesley
  • ISBN-10: 0137358032
  • ISBN-13: 9780137358038
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780137358038.html
Märksõnad:
Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft AI-900 Microsoft Azure AI Fundamentals certification exam.


Exam Ref AI-900 Microsoft Azure AI Fundamentals offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to demonstrate real-world mastery of common machine learning (ML) and artificial intelligence (AI) workloads and how to use them in Azure. Coverage includes:

  • AI workloads and considerations
  • Fundamental principles of ML on Azure
  • Features of computer vision workloads on Azure
  • Features of Natural Language Processing (NLP) workloads on Azure
  • Features of conversational AI workloads on Azure
  • Step-by-step guidance for implementing ML/AI workloads on Azure

Microsoft Exam Ref publications stand apart from third-party study guides because they:

  • Provide guidance from Microsoft, the creator of Microsoft certification exams
  • Target professional-level exam candidates with content focused on their needs, not one-size-fits-all content
  • Streamline study by organizing material according to the exam’s objective domain (OD), covering one functional group and its objectives in each chapter
  • Feature Thought Experiments to guide candidates through a set of what if? scenarios, and prepare them more effectively for Pro-level style exam questions
  • Explore big picture thinking around the professional’s job role

For more information on Exam AI-900 and the Microsoft Certified Azure AI Fundamentals credential, visit https://docs.microsoft.com/en-us/learn/certifications/exams/AI-900.
 



Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft AI-900 Microsoft Azure AI Fundamentals certification exam. Exam Ref AI-900 Microsoft Azure AI Fundamentals offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to demonstrate real-world mastery of common machine learning (ML) and artificial intelligence (AI) workloads and how to use them in Azure. 
Introduction xv
Organization of this book xv
Preparing for the exam xv
Microsoft certifications xvi
Quick access to online references xvi
Errata, updates, & book support xvii
Stay in touch xvii
Chapter 1 Describe Artificial Intelligence workloads and considerations
1(16)
Skill 1.1 Identify features of common Al workloads
1(8)
Describe Azure services for Al and ML
2(1)
Understand Azure Machine Learning
3(1)
Understand Azure Cognitive Services
4(1)
Describe the Azure Bot Service
5(1)
Identify common Al workloads
5(4)
Skill 1.2 Identify guiding principles for Responsible Al
9(8)
Describe the Fairness principle
10(1)
Describe the Reliability & Safety principle
11(1)
Describe the Privacy & Security principle
11(1)
Describe the Inclusiveness principle
11(1)
Describe the Transparency principle
12(1)
Describe the Accountability principle
12(1)
Understand Responsible Al for Bots
12(1)
Understand Microsoft's Al for Good program
13(1)
Chapter summary
13(2)
Thought experiment
15(1)
Thought experiment answers
16(1)
Chapter 2 Describe fundamental principles of machine learning on Azure
17(60)
Skill 2.1 Identify common machine learning types
17(7)
Understand machine learning model types
18(1)
Describe regression models
19(2)
Describe classification models
21(1)
Describe clustering models
22(2)
Skill 2.2 Describe core machine learning concepts
24(9)
Understand the machine learning workflow
24(1)
Identify the features and labels in a dataset for machine learning
25(3)
Describe how training and validation datasets are used in machine learning
28(2)
Describe how machine learning algorithms are used for model training
30(1)
Select and interpret model evaluation metrics
30(3)
Skill 2.3 Identify core tasks in creating a machine learning solution
33(25)
Understand machine learning on Azure
33(6)
Understand Azure Machine Learning studio
39(3)
Describe data ingestion and preparation
42(8)
Describe feature selection and engineering
50(1)
Describe model training and evaluation
51(6)
Describe model deployment and management
57(1)
Skill 2.4 Describe capabilities of no-code machine learning with Azure Machine Learning
58(19)
Describe Azure Automated Machine Learning
58(5)
Describe Azure Machine Learning designer
63(9)
Chapter summary
72(1)
Thought experiment
73(1)
Thought experiment answers
74(3)
Chapter 3 Describe features of computer vision workloads on Azure
77(38)
Skill 3.1 Identify common types of computer vision solution
77(12)
Introduce Cognitive Services
78(6)
Understand computer vision
84(1)
Describe image classification
85(1)
Describe object detection
86(1)
Describe optical character recognition
87(1)
Describe facial detection, recognition, and analysis
88(1)
Skill 3.2 Identify Azure tools and services for computer vision tasks
89(26)
Understand the capabilities of the Computer Vision service
89(7)
Understand the Custom Vision service
96(8)
Understand the Face service
104(4)
Understand the Form Recognizer service
108(3)
Chapter summary
111(1)
Thought experiment
112(1)
Thought experiment answers
113(2)
Chapter 4 Describe features of Natural Language Processing (NLP) workloads on Azure
115(34)
Skill 4.1 Identify features of common NLP workload scenarios
116(6)
Describe Natural Language Processing
116(2)
Describe language modeling
118(1)
Describe key phrase extraction
119(1)
Describe named entity recognition
119(1)
Describe sentiment analysis
120(1)
Describe speech recognition and synthesis
120(1)
Describe translation
121(1)
Skill 4.2 Identify Azure tools and services for NLP workloads
122(27)
Identify the capabilities of the Text Analytics service
123(5)
Identify the capabilities of the Language Understanding service (LUIS)
128(12)
Identify the capabilities of the Speech service
140(4)
Identify the capabilities of the Translator service
144(1)
Chapter summary
145(2)
Thought experiment
147(1)
Thought experiment answers
148(1)
Chapter 5 Describe features of conversational Al workloads on Azure
149(26)
Skill 5.1 Identify common use cases for conversational Al
149(7)
Identify features and uses for webchat bots
151(2)
Identify common characteristics of conversational Al solutions
153(3)
Skill 5.2 Identify Azure services for conversational Al
156(19)
Identify capabilities of the QnA Maker service
157(8)
Identify capabilities of the Azure Bot Service
165(8)
Chapter summary
173(1)
Thought experiment
174(1)
Thought experiment answers 175(2)
Index 177
JULIAN SHARP is a solutions architect, trainer, and Microsoft Business Applications MVP with over 30 years of experience in IT. He completed his MA in Mathematics at the University of Cambridge. Julian has spoken at Microsoft Ignite and many other community events. For the past 15 years, he has been a Microsoft Certified Trainer delivering certification training around Dynamics 365, Azure, and the Power Platform. He has taught thousands of students with a high pass rate. Julian has a passion for Artificial Intelligence to enhance user experience and customer data in the solutions that he designs.