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The Self-Taught Cloud Computing Engineer

You're reading from   The Self-Taught Cloud Computing Engineer A comprehensive professional study guide to AWS, Azure, and GCP

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781805123705
Length 472 pages
Edition 1st Edition
Tools
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Author (1):
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Dr. Logan Song Dr. Logan Song
Author Profile Icon Dr. Logan Song
Dr. Logan Song
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1: Learning about the Amazon Cloud
2. Chapter 1: Amazon EC2 and Compute Services FREE CHAPTER 3. Chapter 2: Amazon Cloud Storage Services 4. Chapter 3: Amazon Networking Services 5. Chapter 4: Amazon Database Services 6. Chapter 5: Amazon Data Analytics Services 7. Chapter 6: Amazon Machine Learning Services 8. Chapter 7: Amazon Cloud Security Services 9. Part 2:Comprehending GCP Cloud Services
10. Chapter 8: Google Cloud Foundation Services 11. Chapter 9: Google Cloud’s Database and Big Data Services 12. Chapter 10: Google Cloud AI Services 13. Chapter 11: Google Cloud Security Services 14. Part 3:Mastering Azure Cloud Services
15. Chapter 12: Microsoft Azure Cloud Foundation Services 16. Chapter 13: Azure Cloud Database and Big Data Services 17. Chapter 14: Azure Cloud AI Services 18. Chapter 15: Azure Cloud Security Services 19. Part 4:Developing a Successful Cloud Career
20. Chapter 16: Achieving Cloud Certifications 21. Chapter 17: Building a Successful Cloud Computing Career 22. Index 23. Other Books You May Enjoy

Practice questions

Questions 1-4 are based on the following use case.

ML case #1

An engineer is training an Amazon SageMaker model to detect as many true malignant tumors (MTs) from MRI images as possible. The model features are shown in Figure 6.18.

Figure 6.18 – Model features: x and y

Figure 6.18 – Model features: x and y

The initial models were underfitting, so they put in a lot of effort and finally got two models working. Their confusion matrixes are shown in Figure 6.19:

Figure 6.19 – Confusion matrixes for models A and B

Figure 6.19 – Confusion matrixes for models A and B

1. How should they synthesize the two features, x and y?

A. x*x + y*y

B. x+y

C. x*y

D. x*10 + y*10

2. What is the precision for model B?

A. 74%

B. 84%

C. 18%

D. 50%

3. What may have helped them improve the initial model?

A. Add more features to the model

B. Add L1 regularization

C. Add L2 regularization

D. Increase the learning rate

4. Which of the following statements is...

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