DSE - Data Science Big Data Technologies Questions and Answers

Question 1

In the context of the "3 V's" of Big Data, which "V" refers to the different forms of data, such as structured (e.g., database tables), semi-structured (e.g., JSON, XML), and unstructured (e.g., text, images, video)?

Accepted Answer

Variety

Answer

The 'Variety' of Big Data refers to the heterogeneity of data types. This includes structured data from traditional databases, semi-structured data like JSON or XML files, and unstructured data such as text documents, videos, and images. Volume refers to the amount of data, Velocity to the speed of data generation, and Veracity to the data's quality and trustworthiness.

Question 2

A data engineering team is migrating a legacy data processing pipeline from Hadoop MapReduce to Apache Spark to improve performance for iterative machine learning algorithms. What is the primary architectural advantage of Spark that enables this significant speedup?

Accepted Answer

In-memory data processing, which avoids slow disk I/O between steps.

Answer

Apache Spark's main performance advantage over traditional Hadoop MapReduce is its ability to perform in-memory processing. Spark can load data into a cluster's memory and query it repeatedly without writing intermediate results to disk. This significantly speeds up iterative algorithms and interactive data analysis, whereas MapReduce writes to disk after each map and reduce stage.

Question 3

What is the primary role of Apache YARN (Yet Another Resource Negotiator) within the Hadoop 2.x and later ecosystems?

Accepted Answer

Managing cluster resources (CPU, memory) and scheduling jobs for various processing frameworks.

Answer

YARN's fundamental role is to decouple resource management from data processing. It acts as the cluster's operating system, managing resources like CPU and memory and scheduling tasks submitted by different applications (e.g., MapReduce, Spark, Tez). HDFS handles storage, Hive provides a SQL interface, and frameworks like MapReduce or Spark execute the processing logic.

Question 4

A large retail company wants to create a central repository to store vast amounts of raw, unstructured data from various sources, including social media feeds, web server logs, and IoT sensor data. The data will be used by data scientists for exploratory analysis without a predefined schema. Which of the following solutions is best suited for this requirement?

Accepted Answer

Data Lake

Answer

A Data Lake is designed to store massive amounts of raw data in its native format. It uses a 'schema-on-read' approach, which is ideal for unstructured and semi-structured data where the use case is not defined upfront. In contrast, a Data Warehouse requires a predefined 'schema-on-write', making it suitable for structured data and specific reporting tasks.

Question 5

Which of the following technologies is primarily designed as a distributed, fault-tolerant, and high-throughput messaging system used to ingest and publish real-time streams of data?

Accepted Answer

Apache Kafka

Answer

Apache Kafka is a distributed event streaming platform built to handle high-throughput, real-time data feeds. It acts as a publish-subscribe messaging system, decoupling data producers from data consumers, and is a cornerstone of modern streaming data pipelines. Spark is a processing engine, HDFS is for storage, and Hive is for querying.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE - Data Science Big Data Technologies Questions and Answers