Book Image

The Self-Taught Cloud Computing Engineer

By : Dr. Logan Song
Book Image

The Self-Taught Cloud Computing Engineer

By: Dr. Logan Song

Overview of this book

The Self-Taught Cloud Computing Engineer is a comprehensive guide to mastering cloud computing concepts by building a broad and deep cloud knowledge base, developing hands-on cloud skills, and achieving professional cloud certifications. Even if you’re a beginner with a basic understanding of computer hardware and software, this book serves as the means to transition into a cloud computing career. Starting with the Amazon cloud, you’ll explore the fundamental AWS cloud services, then progress to advanced AWS cloud services in the domains of data, machine learning, and security. Next, you’ll build proficiency in Microsoft Azure Cloud and Google Cloud Platform (GCP) by examining the common attributes of the three clouds while distinguishing their unique features. You’ll further enhance your skills through practical experience on these platforms with real-life cloud project implementations. Finally, you’ll find expert guidance on cloud certifications and career development. By the end of this cloud computing book, you’ll have become a cloud-savvy professional well-versed in AWS, Azure, and GCP, ready to pursue cloud certifications to validate your skills.
Table of Contents (24 chapters)
1
Part 1: Learning about the Amazon Cloud
9
Part 2:Comprehending GCP Cloud Services
14
Part 3:Mastering Azure Cloud Services
19
Part 4:Developing a Successful Cloud Career

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...