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Enroll Here: Big Data 101 Cognitive Class Exam Quiz Answers
Big Data 101 Cognitive Class Certification Answers
Module 1 – What is Big Data?
Question 1: Name one of the drivers of Volume in the Big Data Era?
- Scalable infrastructure
- An increase in cost to store data
- 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
Module 2 – Beyond the Hype
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
Module 3 – Big Data and Data Science
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
Module 4 – Big Data Use Cases
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
Module 5 – Processing Big 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
Big Data 101 Final Exam Answers – Cognitive Class
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: 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: 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: 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: Module 5: In the Hadoop framework, a rack is a collection of ____________?
- Yarn
- Networks
- Bits
- Nodes
- Distributed files
Question 8: 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: Module 5: The Hadoop framework is mostly written in the Java programming language. True/False
- False
- True
Question 10: 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 the vast amount of structured and unstructured data that inundates businesses on a day-to-day basis. This data comes from various sources such as social media, sensors, devices, business transactions, and more. The term “big” doesn’t just refer to the sheer volume of data but also encompasses its velocity (the speed at which data is generated and processed), variety (the different types of data), and veracity (the quality and reliability of the data).
Here’s a breakdown of some key concepts in Big Data:
- Volume: Big Data involves massive volumes of data, often measured in terabytes, petabytes, or even exabytes. Traditional data management tools struggle to handle this scale.
- Velocity: Data is generated at an unprecedented speed, whether it’s from social media interactions, IoT devices, or other sources. This real-time or near-real-time processing capability is essential for extracting value from data streams.
- Variety: Data comes in various forms: structured, semi-structured, and unstructured. Structured data fits neatly into databases, while unstructured data, like social media posts or emails, doesn’t have a predefined data model. Semi-structured data lies somewhere in between, like JSON or XML files.
- Veracity: With such vast amounts of data coming from diverse sources, ensuring data quality becomes a significant challenge. Veracity refers to the reliability, accuracy, and trustworthiness of the data.
- Value: The ultimate goal of Big Data analysis is to derive insights and make data-driven decisions that create value for businesses or organizations. This could be improving operational efficiency, enhancing customer experience, or discovering new business opportunities.
- Variability: Data can be inconsistent. For instance, social media data might have peaks and troughs in activity, or sensor data might vary based on environmental factors. Handling this variability is crucial for meaningful analysis.
- Visualization: Given the complexity and size of Big Data, visualization plays a crucial role in understanding and communicating insights. Data visualization tools help analysts and decision-makers grasp patterns and trends more easily.
- Machine Learning and AI: Big Data often goes hand in hand with machine learning and AI. These technologies help in analyzing large datasets efficiently, uncovering hidden patterns, and making predictions or recommendations based on the data.
- Data Privacy and Security: With the abundance of personal and sensitive data being collected, ensuring privacy and security is paramount. Regulations like GDPR and CCPA set standards for how data should be handled and protected.
- Data Lakes and Data Warehouses: These are storage systems designed for handling Big Data. Data lakes store raw, unstructured data, while data warehouses organize structured data for analysis. Both play crucial roles in managing and analyzing Big Data effectively.
Understanding and leveraging Big Data can provide organizations with valuable insights to drive innovation, improve decision-making processes, and gain a competitive edge in today’s data-driven world.
