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Databricks Certified Associate Developer for Apache Spark Using Python

You're reading from   Databricks Certified Associate Developer for Apache Spark Using Python The ultimate guide to getting certified in Apache Spark using practical examples with Python

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781804619780
Length 274 pages
Edition 1st Edition
Languages
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Author (1):
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Saba Shah Saba Shah
Author Profile Icon Saba Shah
Saba Shah
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Exam Overview
2. Chapter 1: Overview of the Certification Guide and Exam FREE CHAPTER 3. Part 2: Introducing Spark
4. Chapter 2: Understanding Apache Spark and Its Applications 5. Chapter 3: Spark Architecture and Transformations 6. Part 3: Spark Operations
7. Chapter 4: Spark DataFrames and their Operations 8. Chapter 5: Advanced Operations and Optimizations in Spark 9. Chapter 6: SQL Queries in Spark 10. Part 4: Spark Applications
11. Chapter 7: Structured Streaming in Spark 12. Chapter 8: Machine Learning with Spark ML 13. Part 5: Mock Papers
14. Chapter 9: Mock Test 1
15. Chapter 10: Mock Test 2
16. Index 17. Other Books You May Enjoy

Sample questions

Question 1:

What’s true about Spark’s execution hierarchy?

  1. In Spark’s execution hierarchy, a job may reach multiple stage boundaries.
  2. In Spark’s execution hierarchy, manifests are one layer above jobs.
  3. In Spark’s execution hierarchy, a stage comprises multiple jobs.
  4. In Spark’s execution hierarchy, executors are the smallest unit.
  5. In Spark’s execution hierarchy, tasks are one layer above slots.

Question 2:

What do executors do?

  1. Executors host the Spark driver on a worker-node basis.
  2. Executors are responsible for carrying out work that they get assigned by the driver.
  3. After the start of the Spark application, executors are launched on a per-task basis.
  4. Executors are located in slots inside worker nodes.
  5. The executors’ storage is ephemeral and as such it defers the task of caching data directly to the worker node thread.

Answers

  1. A
  2. B
  3. ...
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