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Distributed Computing with Spark SQL Coursera Quiz Answer

Team Networking Funda

  • In Uncategorized
  • On August 22, 2023

All Weeks Distributed Computing with Spark SQL Coursera Quiz Answer

Week 01 : distributed computing with spark sql coursera quiz answer, quiz 01: assignment #1 quiz – queries in spark sql.

Q 1. What is the first value for “Incident Number”?

Answer: comment the answer

Q 2. What is the first value for “Incident Number” on April 4th, 2016?

Q 3. Is the first fire call in this table on Brooke or Conor’s birthday? Conor’s birthday is 4/4 and Brooke’s is 9/27 (in MM/DD format).

  • B​rooke’s birthday
  • C​onor’s birthday

Q 4. W​hat is the “Station Area” for the first fire call in this table? Note that this table is a subset of the dataset.

Q 5. H​ow many incidents were on Conor’s birthday in 2016?

Q 6. H​ow many fire calls had an “Ignition Cause” of “4 act of nature”?

Q 7. W​hat is the most common “Ignition Cause”?

Hint: Put the entire string.

Q 8. W​hat is the total incidents from the two joined tables?

Quiz 02: Module 1 Quiz

Q 1. Which of the following are true when it comes to the business value of big data? (Select all that apply.)

  • Businesses are increasingly making data-driven decisions
  • The size of the data businesses collect is growing

Spark uses…

(Select all that apply.)

  • Your database technology (e.g., Postgres or SQL Server) to run Spark queries
  • One very large computer that is able to run computation against large databases
  • A distributed cluster of networked computers made of a driver node and many executor nodes
  • A driver node to distribute work across a number of executor nodes

Q 3. How does Spark execute code backed by DataFrames? (Select all that apply.)

  • It optimizes your query by figuring out the best “how” to execute what you want
  • It iterates over all of the source data to exhaustively evaluate queries
  • It executes code determined in advance

Q 4. What are the properties of Spark DataFrames? (Select all that apply.)

  • Distributed: Computed across multiple nodes
  • Resilient: Fault-tolerant
  • Dataset: Collection of partitioned data
  • Tables: Operates as any table in SQL environments

Q 5. What is the difference between Spark and database technologies? (Select all that apply.)

  • Spark does not interact with databases but uses its proprietary DataFrame technology instead
  • Spark is a computation engine and is not for data storage
  • Spark is a highly optimized compute engine and is not a database

Q 6. What is Amdahl’s law of scalability? (Select all that apply.)

  • A formula that gives the number of processors (or other unit of parallelism) needed to complete a task
  • A formula that gives the theoretical speedup as a function of the size of a partition (or subset) of data
  • A formula that gives the expected speed of a single processor performing a computation
  • Amdahl’s law states that the speedup of a task is a function of how much of that task can be parallelized
  • A formula that gives the theoretical speedup as a function of the percentage of a computation that can be parallelized

Q 7. Spark offers a unified approach to analytics. What does this include? (Select all that apply.)

  • Spark is able to connect to data where it lives in any number of sources, unifying the components of a data application
  • Spark allows analysts, data scientists, and data engineers to all use the same core technology
  • Spark code can be written in the following languages: SQL, Scala, Java, Python, and R
  • Spark unifies applications such as SQL queries, streaming, and machine learning
  • Spark unifies databases with optimized computation allowing for faster computation against the data it stores

Q 8. What is a Databricks notebook?

  • A single Spark query
  • A collaborative, interactive workspace that allows you to execute Spark queries at scale
  • A cluster that executes Spark code
  • A Spark instance that executes queries

Q 9. How can you get data into Databricks? (Select all that apply.)

  • By connecting to Dropbox or Google Drive
  • By registering the data as a table
  • By uploading it through the user interface
  • By “mounting” data backed by cloud storage

Q 10. What are the qualities of big data? (Select all that apply.)

  • Variety: the diversity of data
  • Volume: the amount of data
  • Valorous: the positives impact of data
  • Veracity: the reliability of data
  • Velocity: the speed of data

Week 02 : Distributed Computing with Spark SQL Coursera Quiz Answer

Quiz 01 : assignment #2 quiz – spark internals.

Q 1. H​ow many fire calls are in our table?

Answer: Comment the answer

Q 2. How large is our fireCalls dataset in memory? Input just the numeric value (e.g.  51.2 )

Q 3. Which Unit Type is most common?

  • RESCUE CAPTAIN

Q 4. W​hat type of transformation, wide or narrow, did the GROUP BY and ORDER BY queries result in?

Q 5.Looking at the query below, how many tasks are in the last stage of the last job?

Answer: Comment the Answer

Quiz 02:Module 2 Quiz

Q 1. What are the different units of parallelism? (Select all that apply.)

Q 2. What is a partition?

  • A division of computation that executes a query
  • A synonym with “task”
  • A portion of a large distributed set of data
  • The result of data filtered by a WHERE clause

Q 3. What is the difference between in-memory computing and other technologies? (Select all that apply.)

  • In-memory operates from RAM while other technologies operate from disk
  • In-memory computing is slower than other types of computing
  • In-memory operations were not realistic in older technologies when memory was more expensive

Q 4. Why is caching important?

  • It reformats data already stored in RAM for faster access
  • It improves queries against data read one or more times
  • It stores data on the cluster to improve query performance
  • It always stores data in-memory to improve performance

Q 5. Which of the following is a wide transformation? (Select all that apply.)

Q 6. Broadcast joins…

  • Shuffle both of the tables, minimizing computational resources
  • Shuffle both of the tables, minimizing data transfer by transferring data in parallel
  • Transfer the smaller of two tables to the larger, increasing data transfer requirements
  • Transfer the smaller of two tables to the larger, minimizing data transfer

Q 7. Adaptive Query Execution uses runtime statistics to:

  • Dynamically coalesce shuffle partitions
  • Dynamically switch join strategies
  • Dynamically optimize skew joins
  • Dynamically cache data

Q 8. Which of the following are bottlenecks you can detect with the Spark UI? (Select all that apply.)

  • Incompatible data formats

Q 9. What is a stage boundary?

  • Any transition between Spark tasks
  • An action caused by a SQL query is predicate
  • When all of the slots or available units of processing have to sync with one another
  • A narrow transformation

Q 10. What happens when Spark code is executed in local mode?

  • The executor and driver are on the same machine
  • The code is executed against a local cluster
  • The code is executed in the cloud
  • A cluster of virtual machines is used rather than physical machines

Week 03 : Distributed Computing with Spark SQL Coursera Quiz Answer

Quiz 01:assignment #3 quiz – engineering data pipelines.

Q 1. W​hat type of table is “newTable”?

Q 2. H​ow many rows are in “newTable”?

Answer: Comment the Answer.

Q 3.W​hat is the “Battalion” of the first entry in the sorted table?

Q 4. W​as this query faster or slower on the table with increased partitions?

Q 5. D​oes the data stored within the table still exist at the original location (‘dbfs:/tmp/newTableLoc’) after you dropped the table?

Quiz 02: Module 3 Quiz

Q 1. Decoupling storage and compute means storing data in one location and processing it using a separate resource. What are the benefits of this design principle? (Select all that apply.)

  • Resources are isolated and therefore more manageable and debuggable
  • It results in copies of the data in case of a data center outage
  • It allows for elastic resources so larger storage or compute resources are used only when needed
  • It makes updates to new software versions easier

Q 2. You want to run a report entailing summary statistics on a large dataset sitting in a database. What is the main resource limitation of this task?

  • IO: the transfer of data is more demanding than the computation
  • IO: computation is more demanding that the data transfer
  • CPU: the transfer of data is more demanding than the computation
  • CPU: computation is more demanding than the data transfer

Q 3. Processing virtual shopping cart orders in real time is an example of

  • Online Transaction Processing (OLTP)
  • Online Analytical Processing (OLAP)

Q 4. When are BLOB stores an appropriate place to store data? (Select all that apply.)

  • For cheap storage
  • For storing large files
  • For a “data lake” of largely unstructured data
  • For online transaction processing on a website

Q 5. JDBC is the standard protocol for interacting with databases in the Java environment. How do parallel connections work between Spark and a database using JDBC?

  • Specify a column, number of partitions, and the column’s minimum and maximum values. Spark then divides that range of values between parallel connections.
  • Specify the numPartitions configuration setting. Spark then creates one parallel connection for each partition.
  • Specify the number of partitions using COALESCE. Spark then creates one parallel connection for each partition.
  • Specify the number of partitions using REPARTITION. Spark then creates one parallel connection for each partition.

Q 6. What are some of the advantages of the file format Parquet over CSV? (Select all that apply.)

  • Corruptible
  • Compression
  • Parallelism

Q 7. SQL is normally used to query tabular (or “structured”) data. Semi-structured data like JSON is common in big data environments. Why? (Select all that apply.)

  • It does not need a formal structure
  • It allows for easy joins between relational JSON tables
  • It allows for missing data
  • It allows for complex data types
  • It allows for data change over time

Q 8. Data writes in Spark can happen in serial or in parallel. What controls this parallelism?

  • The number of stages in a write operation
  • The number of data partitions in a DataFrame
  • The numPartitions setting in the Spark configuration
  • The number of jobs in a write operation

Q 9. Fill in the blanks with the appropriate response below:

A _______ table manages _______and a DROP TABLE command will result in data loss.

  • Managed, both the data and metadata such as the schema and data location
  • Unmanaged, only the metadata such as the schema and data location
  • Unmanaged, both the data and metadata such as the schema and data location
  • Managed, only the metadata such as the schema and data location

Week 04 : Distributed Computing with Spark SQL Coursera Quiz Answer

Assignment #4 quiz – lakehouse.

Q 1. How many folders were created? Enter the number of records you see from the output below (include the _delta_log in your count)

Q 2. Delete all the records where City is null. How many records are left in the delta table?

Answer: 416869

Q 3. After you deleted all records where the City is null, how many files were removed? Hint: Look at operationsMetrics in the transaction log using the DESCRIBE HISTORY command.

Q 4. There are quite a few missing  Call_Type_Group  values. Use the  UPDATE  command to replace any null values with  Non Life-threatening .

After you replace the null values, how many  Non Life-threatening  call types are the

Answer: 302506

Q 5. Travel back in time to the earliest version of the Delta table (version 0). How many records were there?

Answer: 417419

Module 4 Quiz

Q 1. What are the ACID properties?

  • Atomicity, Consistency, Isolation, and Durability
  • Atomicity, Consistency, Idempotent, and Durability
  • Atomicity, Consistency, Isolation, and Duration
  • Atomicity, Congruency, Isolation, and Durability

Q 2. Which of the following are true statements about data warehouses?

  • T hey use closed protocols and proprietary software
  • They enable machine learning workloads
  • They provide the structure needed for BI applications
  • They have a high degree of flexibility

Q 3. Which of these features does Delta Lake support? (Select all that apply.)

  • Cluster Creation
  • Time Travel
  • Schema Evolution
  • Space Travel

Q 4. Which of the following are true statements about data lakes?

  • They use closed protocols and proprietary software

Q 5. Which of the following are valid data models?

  • Non-relational
  • Query-oriented

Q 6. What are the benefits a lakehouse architecture provides?

  • Combine scalability and low-cost storage of data lakes with the speed and ACID transactional guarantees of data warehouses
  • Combine scalability and ACID transactional guarantees of data lakes with the speed and low-cost storage of data warehouses
  • Combine scalability and low-cost storage of data warehouses with the speed and ACID transactional guarantees of data lakes
  • Combine speed and low-cost storage of data lakes with the scalability and ACID transactional guarantees of data warehouses

Q 7. Machine learning is suited to solve which of the following tasks? (Select all that apply.)

  • Image Recognition
  • Financial Forecasting
  • Fraud Detection
  • Natural Language Processing
  • A/B Testing
  • Churn Analysis

Q 8. What is Machine Learning? (Select all that apply.)

  • A function that maps features to an output
  • Learning patterns in your data without being explicitly programmed
  • Hand-coded logic
  • Statistical moments calculated against a dataset

Q 9. Fill in the blanks with the appropriate answer below.)

Predicting whether a website user is fraudulent or not is an example of _________ machine learning. It is a __________ task

  • unsupervised, regression
  • supervised, classification
  • unsupervised, classification
  • supervised, regression

Q 10. Linear regression is one algorithm used for machine learning. What is this algorithm learning?

  • It learns the line of best fit through the data
  • It learns the average of the label you’re trying to predict
  • It learns the median of the label you’re trying to predict
  • It learns the most similar other datapoints in that dataset to the ones you provide

Get All Course Quiz Answers of Learn SQL Basics for Data Science Specialization

SQL for Data Science Coursera Quiz Answers

Data Wrangling, Analysis and AB Testing with SQL Coursera Quiz Answers

Distributed Computing with Spark SQL Coursera Quiz Answers

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Distributed computing with spark sql at uc davis.

Learn more advanced concepts of SQL and distributed computing with the Distributed Computing with Spark SQL course by Coursera.

Quick Facts

Course overview.

The Distributed Computing with Spark SQL certification course is a 14 hours course. This course is available on the educational course platform Coursera, and the syllabus is curated by the University of California. Also, this course is part of the main programme, Learn SQL Basics for Data Science Specialization. 

This Distributed Computing with Spark SQL training course is made for candidates with some ideas, and information about SQL . This Coursera programme will be great for students who want to step ahead in their data journey. All 4 modules developed for this course are intertwined among themselves. In the end, when all 4 modules are learnt, the candidates will have learned many ways related to Spark SQL, and ways to construct reliable data points.

The Highlights

  • Online course
  • Shareable certificate
  • 14 hours for completion
  • English course title available 

Programme Offerings

Courses and certificate fees.

The Distributed Computing with Spark SQL certification fee is based on the monthly plans mainly for 1 month, 3 months, or 6 months. All these monthly plans have the number of hours to be learned mentioned and also a certification at the course end to be shared with the candidates.

Distributed Computing with Spark SQL Fee Details

Eligibility Criteria

Certification Qualifying Details

The Distributed Computing with Spark SQL certification by Coursera is offered when the candidates are done with every course specialization.

What you will learn

Here are some things that will be learnt from the Distributed Computing with Spark SQL certification syllabus:

  • Using a collaborative workspace that can help in writing Spark SQL that can be easily executed.
  • Learning to inspect the Spark Up that shall be used for analyzing the query performance that helps in ultimately identifying bottlenecks.
  • Curating end-to-end pipelines that will help read the data by transforming it and ultimately saving the result.
  • Help in building a medallion either gold, bronze, or silver to ensure performance, scalability, and reliability.

Who it is for

Distributed Computing with Spark SQL shall become ideal for people like  data scientists , and  computer programmers . 

Admission Details

To get admission to the Distributed Computing with Spark SQL classes, the students can follow these steps: 

Step 1: Follow the official URL: https://www.coursera.org/learn/spark-sql#.

Step 2: During step 2, get to the ‘Enroll Now’ button and then click on it

Step 3: After the account creation is done, then log in must be done which will then lead the students to choose either the free mode or the paid mode.

Step 4: The above decision will be the deciding factor for admission to this course.

The Syllabus

  • Course Introduction
  • Why Distributed Computing?
  • Spark DataFrames
  • The Databricks Environment
  • SQL in Notebooks
  • Import Data
  • A Note From UC Davis
  • Readings and Resources
  • Assignment #1 - Queries in Spark SQL

Practice Exercises

  • Assignment #1 Quiz - Queries in Spark SQL
  • Module 1 Quiz
  • Module Introduction
  • Spark Terminology
  • Shuffle Partitions
  • Adaptive Query Execution (AQE)
  • Assignment #2 - Spark Internals
  • Assignment #2 Quiz - Spark Internals
  • Module 2 Quiz
  • Spark as a Connector
  • Accessing Data
  • File Formats
  • JSON, Schemas and Types
  • Writing Data
  • Tables and Views
  • Assignment #3 - Engineering Data Pipelines
  • Assignment #3 Quiz - Engineering Data Pipelines30m
  • Module 3 Quiz
  • Data Lakes vs. Data Warehouses
  • What is a Lakehouse?
  • Delta Lake (Demo)
  • Delta Advanced Features (Demo)
  • Continuing with Spark and Data Science
  • Course Summary
  • Assignment #4 - Lakehouse
  • Assignment #4 Quiz - Lakehouse
  • Module 4 Quiz

Instructors

Uc davis frequently asked questions (faq's).

The name of the main course is ‘Learn SQL Basics for Data Science Specialization'.

Yes, the deadlines can be adjusted on the Coursera platform.

The level is intermediate.

There are 2 tutors namely Brooke Wenig, and Conor Murphy.

UC Davis is a partnering institution.

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SQL for Data Science - University of California, Davis

sophiasagan/SQL_for_Data_Science

Folders and files, repository files navigation, sql for data science.

University of California, Davis

Taught by: Sadie St. Lawrence, Data Scientist at VSP Global Founder and Executive Director, Women in Data (WID)

About this Course

As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part computer scientist, and part trend spotter” (SAS Institute, Inc.). According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. The skills necessary to be a good data scientist include being able to retrieve and work with data, and to do that you need to be well versed in SQL, the standard language for communicating with database systems. This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes. Although we do not have any specific prerequisites or software requirements to take this course, a simple text editor is recommended for the final project. So what are you waiting for? This is your first step in landing a job in the best occupation in the US and soon the world!

Getting Started and Selecting & Retrieving Data with SQL

In this module, you will be able to define SQL and discuss how SQL differs from other computer languages. You will be able to compare and contrast the roles of a database administrator and a data scientist, and explain the differences between one-to-one, one-to-many, and many-to-many relationships with databases. You will be able to use the SELECT statement and talk about some basic syntax rules. You will be able to add comments in your code and synthesize its importance.

11 videos, 2 readings, 2 practice quizzes

Discussion Prompt: Your Goals For This Course...

Video: Course Introduction

Video: Module Introduction

Video: What is SQL Anyway?

Video: Data Models, Part 1: Thinking About Your Data

Video: Data Models, Part 2: The Evolution of Data Models

Video: Data Models, Part 3: Relational vs. Transactional Models

Video: Retrieving Data with a SELECT Statement

Video: Creating Tables

Video: Creating Temporary Tables

Video: Adding Comments to SQL

Practice Quiz: Let's Practice!

Practice Quiz: Practice Simple Select Queries

Video: Summary

Reading: SQL Overview

Reading: Data Modeling and ER Diagrams

Discussion Prompt: Comparing NoSQL and SQL

Graded: Module 1 Quiz

Graded: Module 1 Coding Questions

Filtering, Sorting, and Calculating Data with SQL

In this module, you will be able to use several more new clauses and operators including WHERE, BETWEEN, IN, OR, NOT, LIKE, ORDER BY, and GROUP BY. You will be able to use the wildcard function to search for more specific or parts of records, including their advantages and disadvantages, and how best to use them. You will be able to discuss how to use basic math operators, as well as aggregate functions like AVERAGE, COUNT, MAX, MIN, and others to begin analyzing our data.

9 videos, 1 reading

Video: Basics of Filtering with SQL

Video: Advanced Filtering: IN, OR, and NOT

Video: Using Wildcards in SQL

Video: Sorting with ORDER BY

Video: Math Operations

Video: Aggregate Functions

Video: Grouping Data with SQL

Video: Putting it All Together

Reading: SQL for Various Data Science Languages

Graded: Module 2 Quiz

Graded: Module 2 Coding Assignment

Subqueries and Joins in SQL

In this module, you will be able to discuss subqueries, including their advantages and disadvantages, and when to use them. You will be able to recall the concept of a key field and discuss how these help us link data together with JOINs. You will be able to identify and define several types of JOINs, including the Cartesian join, an inner join, left and right joins, full outer joins, and a self join. You will be able to use aliases and pre-qualifiers to make your SQL code cleaner and efficient.

10 videos, 2 readings

Video: Using Subqueries

Video: Subquery Best Practices and Considerations

Video: Joining Tables: An Introduction

Video: Cartesian (Cross) Joins

Video: Inner Joins

Video: Aliases and Self Joins

Video: Advanced Joins: Left, Right, and Full Outer Joins

Video: Unions

Reading: SQL and Python

Reading: Union and Union All

Discussion Prompt: What do you think you'll use?

Graded: Module 3 Quiz

Graded: Module 3 Coding Assignment

Modifying and Analyzing Data with SQL

In this module, you will be able to discuss how to modify strings by concatenating, trimming, changing the case, and using the substring function. You will be able to discuss the date and time strings specifically. You will be able to use case statements and finish this module by discussing data governance and profiling. You will also be able to apply fundamental principles when using SQL for data science. You'll be able to use tips and tricks to apply SQL in a data science context.

10 videos, 3 readings

Video: Working with Text Strings

Video: Working with Date and Time Strings

Video: Date and Time Strings Examples

Video: Case Statements

Video: Views

Video: Data Governance and Profiling

Video: Using SQL for Data Science, Part 1

Video: Using SQL for Data Science, Part 2

Reading: Additional SQL Resources to Explore

Reading: Welcome to Peer Review Assignments!

Reading: Yelp Dataset SQL Lookup

Video: Course Summary

Discussion Prompt: How do you plan on using SQL in the future?

Graded: Module 4 Quiz

Graded: Module 4 Coding Questions

Graded: Data Scientist Role Play: Profiling and Analyzing the Yelp Dataset

IMAGES

  1. Databricks Lakehouse Fundamentals

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  2. Pin on ACADEMIC PAPERS

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  3. Databricks Accredited Lakehouse Platform Fundamentals

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  4. SOLUTION: Fundamentals of the databricks lakehouse platform

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  5. Chapter 5 Quiz

    assignment #4 quiz lakehouse answers

  6. Quiz 4 Questions and Answers

    assignment #4 quiz lakehouse answers

VIDEO

  1. 2. Lakehouse Architecture and Procedural ETL vs Declarative ETL. How Delta Live tables executes jobs

  2. Compiler Design

  3. Module 4 Quiz Tips

  4. Behind the Hype

  5. End of Module Quiz

  6. Effective Writing

COMMENTS

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  25. SQL for Data science Module quiz 4 answer

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