# 50+ Big Data Analytics MCQ Questions with Answers (2025 Edition)

Preparing for a data analytics interview or want to test your knowledge of the Big Data ecosystem? You're in the right place. Multiple-choice questions are a fantastic way to quickly assess your understanding of key concepts and technologies. The better you know these fundamentals, the more confident you'll be in a technical interview.

We've compiled a comprehensive list of Big Data Analytics MCQs, complete with answers and explanations, covering everything from the 3 V's to the core functions of Hadoop, Spark, and NoSQL. Use this guide from Vtricks Technologies to prepare for your next big opportunity.

## Section 1: Big Data Fundamentals

1. Which of the following best describes the "Variety" characteristic of Big Data?

- a) The sheer amount of data being generated.
- b) The speed at which data is being generated and processed.
- c) The different types of data, such as structured, unstructured, and semi-structured.
- d) The trustworthiness and quality of the data.

Answer: c) The different types of data, such as structured, unstructured, and semi-structured.

Explanation: Variety refers to the diverse formats of data, including traditional databases (structured), text files, emails, video, and audio (unstructured).

2. What is the main goal of Big Data Analytics?

- a) To store as much data as possible for as long as possible.
- b) To uncover hidden patterns, unknown correlations, and other useful insights from large datasets.
- c) To move all organizational data to a cloud platform.
- d) To replace traditional relational databases entirely.

Answer: b) To uncover hidden patterns, unknown correlations, and other useful insights from large datasets.

Explanation: The ultimate purpose of analyzing big data is to gain business value by discovering patterns and insights that can lead to better decisions and strategic business moves.

## Section 2: Hadoop Ecosystem

3. In the Hadoop ecosystem, what is the role of YARN?

- a) To store data across the cluster in a distributed manner.
- b) To provide a SQL-like interface for querying data.
- c) To act as a resource manager and job scheduler for the cluster.
- d) To process data in-memory.

Answer: c) To act as a resource manager and job scheduler for the cluster.

Explanation: YARN (Yet Another Resource Negotiator) is responsible for allocating system resources to the various applications running in a Hadoop cluster and scheduling tasks to be executed.

4. The two core components of the Hadoop framework are:

- a) Hive and Pig
- b) HDFS and MapReduce
- c) Spark and Kafka
- d) YARN and HDFS

Answer: b) HDFS and MapReduce.

Explanation: The original foundation of Hadoop is HDFS (Hadoop Distributed File System) for storage and MapReduce for parallel processing. YARN was added in Hadoop 2.0.

## Section 3: Apache Spark

5. What is the primary data structure in Apache Spark?

- a) DataTable
- b) DataFrame
- c) RDD (Resilient Distributed Dataset)
- d) DataStream

Answer: c) RDD (Resilient Distributed Dataset).

Explanation: RDD is the fundamental, low-level data structure in Spark. DataFrames and Datasets, which provide more structure and optimization, are built on top of RDDs.

6. Spark's ability to perform in-memory computation makes it faster than Hadoop MapReduce because:

- a) It uses less RAM than MapReduce.
- b) It minimizes the need to read and write intermediate results to disk.
- c) It only works with structured data.
- d) It has a better user interface.

Answer: b) It minimizes the need to read and write intermediate results to disk.

Explanation: MapReduce writes results to HDFS after each map and reduce stage, creating significant I/O overhead. Spark can keep data in memory between stages, which is much faster.

## Section 4: NoSQL Databases

7. Which of the following is NOT a type of NoSQL database?

- a) Document Store
- b) Relational Database
- c) Key-Value Store
- d) Column-Family Store

Answer: b) Relational Database.

Explanation: Relational databases (like MySQL, PostgreSQL) are SQL-based and have a predefined schema, which is the opposite of the principles behind NoSQL databases.

8. A key-value store database like Redis or Riak is optimized for:

- a) Complex queries involving many tables.
- b) Storing and retrieving data using a simple key, providing very high performance.
- c) Analyzing graph or network-like data.
- d) Handling transactions with strong consistency.

Answer: b) Storing and retrieving data using a simple key, providing very high performance.

Explanation: Key-value stores operate like a dictionary or hash map, designed for speed and simplicity in read/write operations for a given key.

## Ready to Go Deeper?

How did you do? Understanding these concepts is the first step toward a successful career in data. If you found yourself guessing on a few, it might be time to strengthen your foundation.

Our Data Analytics Course in Bangalore covers all these topics and more, with hands-on labs and real-world projects that turn theoretical knowledge into practical, job-ready skills. We prepare you not just to answer these questions, but to solve the real problems behind them.