Big Data & Analytics Technologies: Hadoop and Spark

Question 1
1. What is the primary role of HDFS in the Hadoop ecosystem?

(A) A. It serves as a distributed storage system for large datasets.

(B) C. It functions as an in-memory analytics tool.

(C) D. It is a local file system optimized for managing small datasets.

(D) B. It acts as a real-time data processing engine.

Question 2
2. Which component in the Hadoop ecosystem is responsible for managing and scheduling cluster resources?

(A) A. YARN (Yet Another Resource Negotiator)

(B) B. HDFS (Hadoop Distributed File System)

(C) D. MapReduce (Data processing framework)

(D) C. Hive (Data warehousing tool)

Question 3
3. Which framework is renowned for offering in-memory computation, significantly speeding up data processing compared to Hadoop's traditional MapReduce?

(A) B. Apache Hive

(B) A. Apache Spark

(C) C. Apache Pig

(D) D. Apache Storm

Question 4
4. What is the primary function of the MapReduce programming model in Hadoop?

(A) A. It processes and analyzes large datasets by dividing tasks (mapping) and aggregating results (reducing).

(B) D. It organizes and indexes data in a NoSQL database.

(C) B. It stores data across distributed nodes.

(D) C. It manages and allocates cluster resources.

Question 5
5. What is one significant advantage of Apache Spark over traditional Hadoop MapReduce?

(A) B. It enhances data storage efficiency on disks.

(B) A. It offers faster data processing due to in-memory computation.

(C) D. It enables real-time data visualization directly.

(D) C. It provides better fault tolerance than HDFS.

Question 6
6. Which of the following best describes the role of Apache Hive in the Hadoop ecosystem?

(A) B. It serves as a distributed file storage system.

(B) D. It manages configuration and cluster services.

(C) C. It operates as an in-memory processing engine.

(D) A. It provides a SQL-like interface for querying large datasets stored in Hadoop.

Question 7
7. When choosing between Hadoop and Apache Spark for a big data project, which factor is most critical?

(A) C. The project budget constraints related to hardware costs.

(B) B. The requirement for a robust distributed file system.

(C) D. Whether batch processing is acceptable, using Hadoop MapReduce exclusively.

(D) A. The need for in-memory, real-time processing and iterative analytics.

Question 8
8. What challenge in big data processing does Hadoop primarily address?

(A) B. Short-term data caching in memory for rapid access.

(B) C. Real-time streaming analysis of data.

(C) A. Efficient storage and batch processing of very large, unstructured datasets.

(D) D. Creating interactive data visualization dashboards.

Question 9
9. How does the principle of data locality enhance the processing performance in Hadoop?

(A) C. By continuously replicating data across all nodes in real-time.

(B) D. By centralizing processing in a high-performance node to minimize data transfer.

(C) B. By caching all data in memory across the cluster.

(D) A. By moving computation to the node where data is stored, reducing network traffic.

Question 10
10. What does the term 'schema-on-read' mean in the context of big data processing?

(A) D. The system automatically adjusts the schema during both data write and read operations.

(B) C. Data must conform to a predefined schema before it can be stored.

(C) A. Data is interpreted according to a schema at the time of reading, allowing for flexibility with unstructured data.

(D) B. A fixed schema is enforced at the time data is written into storage.

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