CAF-3 · Chapter 4
Challenges and Technologies in Big Data MCQs with Answers
15 multiple-choice questions on Challenges and Technologies in Big Data for CAF-3 Data, Systems and Risks. Try each one before revealing the answer and explanation.
Practise this chapter interactivelyQuestion 1
A global ride-hailing app like Uber or Careem processes trillions of bytes (petabytes) of GPS location data, user logins, and ride requests every single day. Which characteristic of Big Data does this massive scale of data represent?
- A) Variety
- B) Volume
- C) Veracity
- D) Value
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Answer: B) Volume
Volume refers to the massive scale and sheer amount of data generated, such as the trillions of bytes processed by platforms like Uber and Careem to initiate and carry out transactions
Question 2
A multinational e-commerce company collects customer data in the form of structured transaction receipts, unstructured product review videos, and semi-structured clickstream JSON files. This mix of different data formats represents which characteristic of Big Data?
- A) Veracity
- B) Velocity
- C) Variety
- D) Volume
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Answer: C) Variety
Variety indicates that Big Data comes in multiple formats, including structured (databases), unstructured (videos/posts), and semi-structured (JSON/XML) data
Question 3
A financial institution relies on social media feeds to predict stock market movements. However, they struggle to filter out fake news, biased opinions, and bot-generated spam. Which Big Data challenge are they facing?
- A) Value
- B) Veracity
- C) Velocity
- D) Volume
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Answer: B) Veracity
Veracity refers to the accuracy, reliability, and trustworthiness of the data; filtering out fake news and inconsistencies is a major challenge in ensuring data veracity
Question 4
A stock exchange processes thousands of high-frequency financial trades in a fraction of a millisecond. The speed at which this data is generated and processed highlights which characteristic of Big Data?
- A) Veracity
- B) Variety
- C) Velocity
- D) Value
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Answer: C) Velocity
Velocity refers to the high speed and rate at which data is generated, collected, and processed, which is critical in environments like financial markets . --------------------------------------------------------------------------------
Question 5
A fitness company manufactures smartwatches that monitor users' heart rates and daily step counts, sending this data back to the company's servers for health analysis. This is an example of data collected from:
- A) Transactional records
- B) IoT (Internet of Things) devices
- C) Web scraping bots
- D) Open-source government portals
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Answer: B) IoT (Internet of Things) devices
IoT devices, such as smartwatches and home sensors, are a primary source of Big Data, continuously generating and transmitting user data
Question 6
A travel agency uses automated software bots to extract real-time pricing information from the websites of competing airlines. This data collection method is known as:
- A) Application Programming Interfaces (APIs)
- B) Sensor logging
- C) Web Scraping
- D) Survey generation
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Answer: C) Web Scraping
Web scraping is a technique primarily used to extract large amounts of data, such as pricing or product details, directly from websites
Question 7
An automotive manufacturer installs sensors inside car engines to record fuel consumption, engine temperature, and wear-and-tear metrics every second. What type of Big Data source is this?
- A) Social Media data
- B) Machine-Generated data
- C) Transactional data
- D) Semi-structured web data
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Answer: B) Machine-Generated data
Machine-generated data includes data from sensors, industrial machinery, and automated systems used for tracking performance and predicting maintenance . --------------------------------------------------------------------------------
Question 8
A car manufacturer collects sensor data from its production line machines. Using analytics, the company detects signs of wear-and-tear and schedules repairs before the machinery actually breaks down. This application is known as:
- A) Descriptive Analytics
- B) Web Scraping
- C) Predictive Maintenance
- D) Edge Computing
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Answer: C) Predictive Maintenance
Manufacturers use Big Data for predictive maintenance, analyzing sensor data to predict and schedule maintenance before a costly breakdown occurs
Question 9
During the COVID-19 pandemic, global health organizations analyzed massive datasets from mobile tracking, hospital admissions, and travel logs to predict where the virus would spread next. This demonstrates the use of Big Data in which industry?
- A) Manufacturing
- B) Healthcare
- C) Retail
- D) Finance
Show answer & explanation
Answer: B) Healthcare
In healthcare, Big Data is utilized for tracking disease outbreaks, analyzing patient records, and improving public health responses, as seen during the COVID-19 pandemic
Question 10
An online streaming platform analyzes a user's past viewing history, search queries, and pause/rewind habits in real-time to recommend movies they are highly likely to watch. This is an application of Big Data aimed at:
- A) Ensuring physical data security
- B) Understanding user preferences and personalizing experiences
- C) Reducing cloud storage costs
- D) Automating payroll processing
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Answer: B) Understanding user preferences and personalizing experiences
Retail and digital platforms leverage Big Data to understand user preferences and offer personalized experiences, such as real-time movie or product recommendations . --------------------------------------------------------------------------------
Question 11
Despite its benefits, organizations often struggle with Big Data because inaccurate, incomplete, or inconsistent datasets can lead to flawed insights and poor decision-making. This challenge is primarily related to:
- A) Data Quality
- B) Edge Computing
- C) Storage capacity
- D) Processing speed
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Answer: A) Data Quality
Data Quality is a significant challenge; given the massive volume and variety of Big Data, inaccurate or inconsistent data directly leads to flawed insights and poor decisions
Question 12
To handle massive amounts of unstructured data like customer emails, social media posts, and multimedia files, organizations typically avoid traditional relational databases (RDBMS) and instead use:
- A) Spreadsheets
- B) NoSQL Databases
- C) Only physical on-premises hardware
- D) Simple CSV files
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Answer: B) NoSQL Databases
NoSQL databases (like MongoDB or Cassandra) are specifically designed to handle and scale unstructured and semi-structured Big Data efficiently, unlike traditional relational databases
Question 13
To reduce latency and save bandwidth, a smart traffic light system processes data directly at the intersection (close to the sensor) rather than sending all raw video footage back to a centralized cloud server. This technology is known as:
- A) Edge Computing
- B) Hadoop Processing
- C) Blockchain
- D) Web Scraping
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Answer: A) Edge Computing
Edge computing involves processing data closer to the source (like IoT devices or sensors) rather than in centralized cloud servers, allowing for real-time analysis and reduced latency
Question 14
Which of the following is a major ethical concern when organizations collect and analyze vast amounts of Big Data from their users?
- A) Reducing the cost of data storage
- B) Privacy, bias, and transparency
- C) Processing data in real-time
- D) Choosing between structured and unstructured formats
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Answer: B) Privacy, bias, and transparency
Ethical concerns in Big Data revolve heavily around user privacy, avoiding bias in data models, and maintaining transparency about how data is used
Question 15
To overcome the challenge of fragmented data scattered across different departments, an organization uses middleware and platforms to consolidate the data into a single, unified system for analysis. This solution addresses the problem of:
- A) Data Integration
- B) Predictive Maintenance
- C) Web Scraping
- D) Edge Computing
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Answer: A) Data Integration
Data integration platforms and middleware are used to solve the challenge of fragmented data, consolidating various sources into a unified system for effective analysis