Data Engineering Program Quiz 4

Data Engineering Quiz 4

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1. What is a defining characteristic of Big Data?

Data that is always stored in relational databases

Data that does not require parallel computing for analysis

Data that is structured and small in volume

Large volumes of data that require specialized tools for processing

2. Which of the following is NOT a component of the Hadoop ecosystem?

YARN

Spark Streaming

HDFS

MapReduce

3. In Hadoop, what is the primary role of HDFS?

To handle resource management and job scheduling

To store and manage distributed data across multiple nodes

To run machine learning algorithms on big data

To process large-scale datasets in-memory

4. What advantage does Apache Spark have over Hadoop’s MapReduce framework?

Spark operates entirely on disk, while MapReduce uses memory

Spark requires HDFS, while MapReduce does not

Spark provides in-memory processing, making it significantly faster

Spark processes data in batch mode only

5. Which of the following is a key technique for optimizing performance in Big Data processing?

Using a single machine for all computations

Disabling parallel execution to ensure consistency

Storing all data in relational databases for fast access

Minimizing data shuffling and using efficient partitioning strategies

6. What is the main purpose of YARN in the Hadoop ecosystem?

To provide in-memory data storage for real-time processing

To convert structured data into semi-structured formats

To manage and allocate cluster resources for running applications

To store unstructured data in NoSQL databases

7. When implementing a distributed data pipeline with Apache Spark, which component is responsible for executing tasks across a cluster?

SparkContext

Spark DataFrame

Spark Executors

Spark Driver

8. What is a major drawback of Hadoop’s MapReduce compared to Spark?

MapReduce is optimized for real-time data streaming

MapReduce relies heavily on disk I/O, making it slower

MapReduce supports in-memory processing

MapReduce does not support batch processing

9. In Apache Spark, what is the purpose of the DAG (Directed Acyclic Graph)?

To define the order of execution for transformations and actions

To manage Spark cluster resources

To track system performance metrics

To store large datasets in distributed file systems

10. What is an effective method to improve performance in Spark applications?

Using a larger number of small partitions

Increasing the number of shuffle operations

Avoiding data parallelism

Caching frequently used data in memory

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Data Engineering Program Quiz 4

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