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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

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 combines the power of R’s statistical and visualization capabilities with the scalability and speed of Apache Spark’s distributed computing framework. This integration allows data scientists and analysts to efficiently handle large volumes of data and perform complex analyses without being limited by the resources of a single machine.

To get started with analyzing Big Data in R using Apache Spark, you’ll typically follow these steps:

  1. Setup Apache Spark: Install Apache Spark on your local machine or set it up on a cluster. You can use a standalone Spark cluster, or integrate with other cluster managers like YARN or Mesos.
  2. Install Required Packages: Install the necessary R packages for interacting with Apache Spark. The primary package for this purpose is sparklyr, which provides an R interface for Spark.
  3. Connect to Spark: Establish a connection to the Spark cluster using sparklyr. You can specify the Spark master URL and other configuration options to customize the connection.
  4. Load Data: Load your Big Data into Spark’s distributed data structures, such as DataFrames or Resilient Distributed Datasets (RDDs). Spark supports various data sources, including CSV, JSON, Parquet, and databases.
  5. Data Manipulation and Analysis: Utilize R’s familiar syntax and functions to perform data manipulation and analysis tasks on the Spark data. You can use dplyr-like operations, SQL queries, or custom R functions to transform and analyze the data.
  6. Machine Learning: Leverage Spark’s machine learning library (MLlib) to build and train machine learning models on large datasets. sparklyr provides R wrappers for MLlib algorithms, allowing you to use R syntax for model training and evaluation.
  7. Visualizations: Use R’s rich ecosystem of visualization libraries, such as ggplot2 and plotly, to create insightful visualizations of your analysis results. You can visualize summary statistics, model predictions, or any other relevant insights.
  8. Optimization and Performance Tuning: Optimize your Spark jobs for performance by leveraging Spark’s built-in optimization techniques, tuning configuration parameters, and optimizing data storage formats and partitioning.
  9. Deployment: Once you’ve developed and tested your analysis pipeline, you can deploy it in production environments. This may involve packaging your code into Spark applications or integrating it with other systems and tools.
  10. Monitoring and Maintenance: Continuously monitor the performance and health of your Spark cluster and analysis jobs. Make adjustments as needed to ensure scalability, reliability, and efficiency.

By following these steps, you can effectively leverage R and Apache Spark to analyze Big Data, uncover valuable insights, and derive actionable recommendations to drive business decisions and innovations.

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