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Big Data 101 Cognitive Class Exam Quiz Answers

Big Data 101 Cognitive Class Certification Answers

Question 1: Name one of the drivers of Volume in the Big Data Era?

  • Scalable infrastructure
  • Cost
  • Competitive advantage
  • FinTech
  • Research and development

Question 2: Value from Big Data can be _______________?

  • Profits
  • Veracity
  • Petabytes
  • Technical ability
  • Infrastructure

Question 3: In the video, 2.5 Quintillion Bytes of data are equivalent to how many blue ray DVDs?

  • 1 Billion
  • 10 million
  • 100 million
  • 5 million
  • 1 Trillion

Question 1: How many petabytes make up an Exabyte

  • 32
  • 2020
  • 64
  • 1024
  • 8

Question 2: What is an example of a source of Semi-Structured Big data?

  • Cameras files
  • Relational databases
  • Satellite files
  • Spreadsheet file
  • JSON files

Question 3: When is it estimated that the data we create and copy will reach around 35 zettabytes?

  • We have already surpassed this mark
  • 2050
  • 2030
  • 2040
  • 2020

Question 1: What is the process of cleaning and analyzing data to derive insights and value from it?

  • Machine Learning
  • Exploratory Research
  • Data Science
  • Predictive Modeling
  • Decision Trees

Question 2: What is the search engine used by Walmart?

  • JSON
  • HBase
  • ZooKeeper
  • Polaris
  • Poisson

Question 3: An example of visualizing Big Data is___________?

  • Hadoop
  • Integration
  • Agile Governance
  • Temperature on a map
  • Closing your eyes and imagining it

Question 1: What is the term used to describe an holistic approach that takes into account all available and meaningful information about a customer to drive better engagement, revenue and long term loyalty?

  • Enhanced 360-degree view
  • Big Data Exploration
  • End to End
  • Operations Analysis
  • Customer Retention

Question 2: What can help organizations to find new associations or uncover patterns and facts to significantly improve intelligence, security and law enforcement?

  • Using local servers
  • Analyzing data in-motion and at rest
  • Satellite data
  • GPS coordinates
  • Using XML

Question 3: In Operations Analysis, we focus on what type of data?

  • Location Data
  • Machine Data
  • Binary Data
  • Social Media Data
  • Structured Data

Question 1: What is a method of storing data to support the analysis of originally disparate sources of data?

  • Data Lakes
  • Data Mining
  • Predictive Analytics
  • Data Analytics
  • Deep Learning

Question 2: Data Warehouses provide online analytic processing: True/False

  • False
  • True

Question 3: What does ‘OLAP’ stand for?

  • Online Analytical Prediction
  • Online Analytical Platform
  • Online Analytical Processing
  • Online Advanced Prediction
  • Online Advanced Programming

Question 1: In Module 1: What is a common use of big data that is used by companies like Netflix, Spotify, Facebook and Amazon?

  • Recommendation Engines
  • Data Lakes
  • Clusters
  • The Cloud
  • Sensors

Question 2: In Module 2: Is one byte binary? True/False

  • False
  • True

Question 3: In Module 2: What has highly contributed to the launch of the Big Data era?

  • Clusters
  • Spark
  • Cloud Computing
  • Zetabytes
  • Data Scientists

Question 4: In Module 3: A data scientist is a person who is qualified to derive insights from data by using skills and experience from computer science, business or science, and statistics. True/False

  • False
  • True

Question 5: In Module 3: ‘HDFS’ stands for ____________________?

  • Hadoop Data Fraud System
  • High Data File System
  • Hadoop Distributed File System
  • High Distribution Frequency System
  • High Definition Frequency Sensors

Question 6: In Module 3: Data privacy is a critical part of the big data era. Businesses and individuals must give great thought to how data is _____________________________.

  • collected, retained, used, and disclosed
  • bought, sold, stored and analyzed
  • secured, sold, downloaded and uploaded
  • aggregated, compiled, saved and stored
  • stored, analyzed, read and written

Question 7: In Module 5: In the Hadoop framework, a rack is a collection of ____________?

  • Yarn
  • Networks
  • Bits
  • Nodes
  • Distributed files

Question 8: In Module 5: What is a method of storing data to support the analysis of originally disparate sources of data?

  • Spark
  • Data Warehouse
  • Yarn
  • Data Repository
  • Data Lake

Question 9: In Module 5: The Hadoop framework is mostly written in the Java programming language. True/False

  • False
  • True

Question 10: In Module 5: What is the term referring to a database that must be processed by means other than just the SQL Query Language.

  • Spark
  • NoSQL
  • Python
  • SQL
  • Hadoop

Introduction to Big Data 101

Big Data refers to large and complex datasets that traditional data processing applications are inadequate to deal with. These datasets are characterized by their volume, velocity, variety, and veracity, often referred to as the “Four Vs”:

  1. Volume: Big Data involves large amounts of data, often ranging from terabytes to petabytes and beyond.
  2. Velocity: Data is generated at high speed and needs to be processed rapidly. This includes data from various sources such as social media, sensors, clickstreams, etc.
  3. Variety: Big Data comes in various formats, structured and unstructured, including text, images, videos, sensor data, log files, etc.
  4. Veracity: Refers to the reliability and accuracy of the data. Big Data often includes data from diverse sources, which may vary in quality and reliability.

Big Data technologies and techniques enable organizations to analyze these large and diverse datasets to extract insights, make better decisions, and gain a competitive edge. Some key components and technologies in the Big Data ecosystem include:

  1. Hadoop: An open-source framework for distributed storage and processing of large datasets across clusters of computers.
  2. Apache Spark: A fast and general-purpose cluster computing system for Big Data processing.
  3. NoSQL databases: Non-relational databases that can efficiently handle large volumes of unstructured data.
  4. Machine Learning and AI: Techniques and algorithms that help extract valuable insights from Big Data, automate processes, and predict future trends.
  5. Data Visualization: Tools and techniques for presenting Big Data insights in a visually understandable format, such as charts, graphs, and dashboards.
  6. Data Mining: The process of discovering patterns, correlations, and trends within large datasets to extract useful information.
  7. Data Governance and Security: Practices and policies to ensure the security, privacy, and compliance of Big Data assets.

Understanding and effectively utilizing Big Data can help organizations across various industries to improve decision-making, enhance customer experiences, optimize operations, and drive innovation.

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