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Analyzing Big Data in R using Apache Spark Cognitive Class Exam Quiz Answers

Analyzing Big Data in R using Apache Spark Cognitive Class Certification Answers

Analyzing Big Data in R using Apache Spark Cognitive Class Exam Quiz Answers

Question 1: What shells are available for running SparkR?

  • Spark-shell
  • SparkSQL shell
  • SparkR shell
  • RSpark shell
  • None of the options is correct

Question 2: What is the entry point into SparkR?

  • SRContext
  • SparkContext
  • RContext
  • SQLContext

Question 3: When would you need to call sparkR.init?

  • using the R shell
  • using the SR-shell
  • using the SparkR shell
  • using the Spark-shell

Question 1: dataframes make use of Spark RDDs

  • False
  • True

Question 2: You need read.df to create dataframes from data sources?

  • True
  • False

Question 3: What does the groupBy function output?

  • An Aggregate Order object
  • A Grouped Data object
  • An Order By object
  • A Group By object

Question 1: What is the goal of MLlib?

  • Integration of machine learning into SparkSQL
  • To make practical machine learning scalable and easy
  • Visualization of Machine Learning in SparkR
  • Provide a development workbench for machine learning
  • All of the options are correct

Question 2: What would you use to create plots? check all that apply

  • pandas
  • Multiplot
  • Ggplot2
  • matplotlib
  • all of the above are correct

Question 3: Spark MLlib is a module of Apache Spark

  • False
  • True

Question 1: Which of these are NOT characteristics of Spark R?

  • it supports distributed machine learning
  • it provides a distributed data frame implementation
  • is a cluster computing framework
  • a light-weight front end to use Apache Spark from R
  • None of the options is correct

Question 2: True or false? The client connection to the Spark execution environment is created by the shell for users using Spark:

  • True
  • False

Question 3: Which of the following are not features of Spark SQL?

  • performs extra optimizations
  • works with RDDs
  • is a distributed SQL engine
  • is a Spark module for structured data processing
  • None of the options is correct

Question 4: True or false? Select returns a SparkR dataframe:

  • False
  • True

Question 5: SparkR defines the following aggregation functions:

  • sumDistinct
  • Sum
  • count
  • min
  • All of the options are correct

Question 6: We can use SparkR sql function using the sqlContext as follows:

  • head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
  • SparkR:head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
  • SparkR::head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
  • SparkR(head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”)))
  • None of the options is correct

Question 7: Which of the following are pipeline components?

  • Transformers
  • Estimators
  • Pipeline
  • Parameter
  • All of the options are correct

Question 8: Which of the following is NOT one of the steps in implementing a GLM in SparkR:

  • Evaluate the model
  • Train the model
  • Implement model
  • Prepare and load data
  • All of the options are correct

Question 9: True or false? Spark MLlib is a module SparkR to provide distributed machine learning algorithms.

  • True
  • False

Introduction to Analyzing Big Data in R using Apache Spark

Analyzing Big Data in R using Apache Spark typically involves leveraging the SparkR package, which provides an R API for Apache Spark. This allows R users to work with large-scale data processing and analytics on a Spark cluster. Here are the key steps to analyze Big Data in R using Apache Spark:

1. Install Apache Spark:

  • Install Apache Spark on your cluster or local machine. Make sure to set up the necessary configurations.

2. Install SparkR:

  • Install the SparkR package in R, which provides the R API for Apache Spark.

3. Initialize SparkR:

  • Load the SparkR library and initialize a Spark context in R.

4. Load Data into Spark DataFrame:

  • Use SparkR to load your Big Data into a Spark DataFrame. Spark DataFrames are distributed collections of data organized into named columns.


Analyzing Big Data in R using Apache Spark typically involves leveraging the SparkR package, which provides an R API for Apache Spark. This allows R users to work with large-scale data processing and analytics on a Spark cluster. Here are the key steps to analyze Big Data in R using Apache Spark:

1. Install Apache Spark:

  • Install Apache Spark on your cluster or local machine. Make sure to set up the necessary configurations.

2. Install SparkR:

  • Install the SparkR package in R, which provides the R API for Apache Spark.

RCopy code

install.packages("SparkR")

3. Initialize SparkR:

  • Load the SparkR library and initialize a Spark context in R.

RCopy code

library(SparkR) sparkR.session(master = "local[*]", appName = "SparkR Example")

4. Load Data into Spark DataFrame:

  • Use SparkR to load your Big Data into a Spark DataFrame. Spark DataFrames are distributed collections of data organized into named columns.

RCopy code

# Example: Load data from a CSV file into a Spark DataFrame df <- read.df("path/to/your/data.csv", source = "csv", header = "true", inferSchema = "true")

5. Data Exploration and Transformation:

  • Use SparkR functions for data exploration and transformation. SparkR provides a variety of functions similar to those in base R for data manipulation.

6. Data Analysis and Machine Learning:

  • Utilize SparkR’s machine learning functions for analysis. SparkR supports various machine learning algorithms and tools for regression, classification, clustering, and more.

7. Aggregation and Summary Statistics:

  • Perform aggregation and compute summary statistics on your data using SparkR.

8. Visualizations:

  • Generate visualizations using R’s plotting libraries or export data to be visualized in other tools.

9. Optimization and Parallel Processing:

  • Leverage Spark’s distributed and parallel processing capabilities. SparkR automatically distributes computations across the Spark cluster.

10. Integration with Spark Ecosystem:

  • SparkR integrates seamlessly with other Spark components, such as Spark SQL, Spark Streaming, and MLlib. This allows you to leverage the full power of the Spark ecosystem.

11. Model Deployment:

  • After building and evaluating models, deploy them for production use within your Spark environment.

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