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Big Data And Distributed Computing MCQs

Practice Big Data And Distributed Computing MCQs for competitive exams.

Big Data And Distributed Computing MCQs

Practice questions from this topic.

In a distributed computing environment, what is the purpose of "data partitioning" or "sharding"?

  1. A. To introduce data redundancy
  2. B. To improve data visualization
  3. C. To increase data variety
  4. D. To distribute data across multiple nodes
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What is the primary challenge in processing and analyzing data with high velocity in a big data environment?

  1. A. Limited data volume
  2. B. Data skew
  3. C. Data variety
  4. D. Data veracity
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Which distributed computing framework is designed for real-time data stream processing and is often used for analyzing event data and monitoring applications?

  1. A. Apache Kafka
  2. B. Apache HBase
  3. C. Apache Spark Streaming
  4. D. Apache Hive
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In a distributed computing cluster, what is the primary role of a "Master Node" (or NameNode) in the Hadoop ecosystem?

  1. A. Storing metadata
  2. B. Managing job scheduling
  3. C. Storing and managing data blocks
  4. D. Managing data visualization
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In the context of big data analytics, what is the primary goal of "data enrichment"?

  1. A. To reduce data variety
  2. B. To decrease data velocity
  3. C. To enhance data with additional information
  4. D. To increase data reliability
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What is the primary purpose of "data cleansing" in big data preprocessing?

  1. A. To introduce errors into the data
  2. B. To increase data volume
  3. C. To improve data quality
  4. D. To slow down data velocity
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What is the main advantage of using a "NoSQL" database in big data applications?

  1. A. High data consistency
  2. B. Flexible schema and scalability
  3. C. Real-time data processing
  4. D. Columnar storage format
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What is the primary benefit of using a distributed data processing framework like Apache Spark over traditional batch processing systems?

  1. A. Reduced data variety
  2. B. Real-time data processing
  3. C. Simplified data velocity
  4. D. Enhanced data visualization
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In big data analytics, what is the primary challenge associated with "data veracity"?

  1. A. Limited data volume
  2. B. Limited data variety
  3. C. Limited data velocity
  4. D. Limited data reliability
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Which technology is commonly used for streamlining the data velocity aspect of big data, allowing for real-time data collection and analysis?

  1. A. Data lakes
  2. B. Data warehouses
  3. C. Internet of Things (IoT)
  4. D. Apache Kafka
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What does the term "YARN" stand for in the context of Hadoop and distributed computing?

  1. A. Yet Another Resource Negotiator
  2. B. Yet Another Real-time Network
  3. C. Yield and Return Notation
  4. D. Your Advanced Resource Node
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In big data analytics, what is the term for the process of transforming and preparing raw data for analysis, often involving cleaning and structuring the data?

  1. A. Data sampling
  2. B. Data siloing
  3. C. Data preprocessing
  4. D. Data encryption
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