Book Image

AWS Certified Cloud Practitioner Exam Guide

By : Rajesh Daswani
3 (1)
Book Image

AWS Certified Cloud Practitioner Exam Guide

3 (1)
By: Rajesh Daswani

Overview of this book

Amazon Web Services is the largest cloud computing service provider in the world. Its foundational certification, AWS Certified Cloud Practitioner (CLF-C01), is the first step to fast-tracking your career in cloud computing. This certification will add value even to those in non-IT roles, including professionals from sales, legal, and finance who may be working with cloud computing or AWS projects. If you are a seasoned IT professional, this certification will make it easier for you to prepare for more technical certifications to progress up the AWS ladder and improve your career prospects. The book is divided into four parts. The first part focuses on the fundamentals of cloud computing and the AWS global infrastructure. The second part examines key AWS technology services, including compute, network, storage, and database services. The third part covers AWS security, the shared responsibility model, and several security tools. In the final part, you'll study the fundamentals of cloud economics and AWS pricing models and billing practices. Complete with exercises that highlight best practices for designing solutions, detailed use cases for each of the AWS services, quizzes, and two complete practice tests, this CLF-C01 exam study guide will help you gain the knowledge and hands-on experience necessary to ace the AWS Certified Cloud Practitioner exam.
Table of Contents (23 chapters)
1
Section 1: Cloud Concepts
5
Section 2: AWS Technologies
16
Section 3: AWS Security
18
Section 4: Billing and Pricing
20
Chapter 16: Mock Tests

Additional analytics services

In this section, we will take a very quick look at some other AWS analytics services that you need to be aware of. Specifically, we will look at the Elastic Map Reduce (EMR) service, CloudSearch, and Data Pipeline:

  • AWS EMR: This provides a managed Hadoop framework to enable you to process vast amounts of big data. You can use open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR comes with an integrated development environment (IDE) called EMR Studio to help you develop, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. You can run your EMR workloads on EC2 Instances, Amazon Elastic Kubernetes Service (EKS) clusters, and on-premises using the AWS Outpost service. In terms of pricing, you are charged at a per-instance rate for every second used, with a 1-minute minimum charge.
  • AWS Data Pipeline: This is a web service that...