Big Data MCQs: Essential Questions for Data Scientists and Analysts

Big data is a notion that means very big and complex datasets. Big data goes beyond the abilities and applications of the traditional methodology for data processing. Such datasets are commonly characterized by their big size, consisting of semi-structured, structured, and unstructured data. They are often in a synchronous mode and come from different sources, including social media, sensors, devices, logs, and transactions. 

In this article, we present the big data MCQs for data scientists and analysts, and the 5 V’s of big data. 

The 5 V’s of Big Data

The following are the 5V’s of big data that you should know before answering the big data interview questions.

• Volume: Big data means an enormous volume of data that comes from meters, satellites, radio frequency identification (RFID), and social networks, which range in terabytes, petabytes, zettabytes, and so on. 

• Velocity: Large-scale data gets created and captured at high speed and is increasing. This data more often comes in the form of streams, which are the real-time data that need to be processed and analyzed to gain insights for the decision-making process.

• Variety: Big Data means a huge amount of different data types, which their structure can distinguish. This diversity is responsible for the storage levels as well as the issues with data processing and analysis.

• Variability: Big data might display variability in the structure, quality, and sources as it gets bigger and bigger over time. Handling this variability involves employing adaptive data processing methods and tools as a strategy.

• Veracity: Big data can be derived from uncertain or questionable quality, or it can be pulled from missing data of uncertain trustworthiness. The most important thing alongside data accuracy is making reliable decisions on the basis of these insights and implementing them.

Big Data MCQs for Data Scientists and Analysts

Let’s look at the Big Data MCQs for data scientists & analysts:

Q1. What is Big Data?

1. Data with a large file size

2. Data with high velocity and variety that exceeds traditional data processing capabilities

3. Data with high-security requirements

4. Data with a high level of accuracy

Answer: B. Data with high velocity and variety that exceeds traditional data processing capabilities

Q2. Which of the following is a Characteristic of BigDdata?

1. Low volume

2. Structured format

3. Low velocity

4. Predictable variety

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - Structured format

Q3. What do you mean by Big Data Analytics?

1. It is a process of gathering huge amounts of data.

2. The process of analysis of large and complicated data helps in revealing hidden patterns, trends, and insights.

3. The process of securing big data

4. The process of deleting unnecessary data

Answer: B. The process of analysis of large and complicated data helps in revealing hidden patterns, trends, and insights

Q4. Which Techniques are used in Big Data Analytics?

1. Regression analysis

2. Linear programming

3. Gradient descent

4. Machine learning

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘D’ - Machine learning

Q5. What is Hadoop?

1. A programming language for big data analytics

2. A distributed file system for storing big data

3. A framework for distributed storage and processing of big data

4. A database management system for big data

Answer: C. A framework for distributed storage and processing of big data

Q6. What is MapReduce in Hadoop Fundamentals? 

1. A programming language

2. A distributed computing model for processing big data

3. A database query language

4. A data visualization tool

Answer: B. A distributed computing model for processing big data

Q7. Which Phase in MapReduce is Responsible for Data Aggregation?

1. Map phase

2. Shuffle phase

3. Reduce phase

4. Merge phase

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - Reduce phase

Q8. What is the Function of Apache Spark in Processing Big Data?

1. Data storage

2. Data visualization

3. Data processing and analytics

4. Data security

Answer: C. Data processing and analytics

Q9. Which of the following is a Big Data Tool Employed for Real-Time Stream Processing? 

1. Hadoop

2. Apache Kafka

3. MySQL

4. MongoDB

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - Apache Kafka

Q10. Which of the following is a NoSQL Database Most Often Used to Deal with Huge Data Volumes?

1. MySQL

2. PostgreSQL

3. MongoDB

4. SQLite

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - MongoDB

Q11. What is the Purpose of a Data Warehouse in Big Data Analytics?

1. To store real-time data streams

2. To store structured data in relational databases

3. To integrate and analyze data from multiple sources for reporting and analysis

4. To store unstructured data such as images and videos

Answer: C. To integrate and analyze data from multiple sources for reporting and analysis

Q12. Which of the following is a Batch-Processing Framework Commonly Used in Big Data Analytics?

1. Apache Spark

2. Apache Kafka

3. Apache Storm

4. Apache Flink

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘D’ - Apache Flink

Q13. What is the Significant Strength of Using Apache Spark in Comparison with MapReduce if you Need to Process Big Data?

1. Spark supports only batch processing.

2. Spark provides in-memory processing, which makes it faster than disk-based MapReduce.

3. Spark does not support data streaming.

4. Spark is limited to processing structured data.

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - Spark provides in-memory processing, which makes it faster than disk-based MapReduce.

Q14. Which of the following Tools is Commonly used for Interactive Data Visualization in Big Data Analytics?

1. Tableau

2. Microsoft Excel

3. Power BI

4. MATLAB

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘A’ - Tableau

Q15. What is the Fundamental Role of Exploratory Data Analysis (EDA) in Today’s Data Analytics and Big Data?

1. To visualize and summarize data to understand its underlying structure

2. To clean and preprocess raw data before analysis

3. To perform hypothesis testing on large datasets

4. To deploy machine learning models for prediction

Answer: A. To visualize and summarize data to understand its underlying structure

Q16. What's the Security Risk of Big Data?

1. Limited storage capacity

2. Lack of data variety

3. Data privacy and confidentiality concerns

4. Slow data processing speed

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - Data privacy and confidentiality concerns

Q17. What is Data Governance in Big Data?

1. The process of ensuring data consistency and accuracy

2. The process of managing data access and permissions

3. The process of defining data quality standards and policies

4. The process of storing and retrieving large volumes of data

Answer: C. The process of defining data quality standards and policies

Q18. What is the Name of the Machine Learning Algorithm that Most People Use when Analyzing Large Volumes of Data Through Classification?

1. K-means clustering

2. Linear regression

3. Random forest

4. Principal component analysis (PCA)

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - Random forest

Q19.  What Does it Mean to have a Predictive Model in Big Data Analytics?

1. To analyze historical data and identify patterns

2. To summarize and visualize large datasets

3. To predict future outcomes based on historical data

4. To store and manage data efficiently

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - To predict future outcomes based on historical data

Q20. Which Technology Feature is Normally Used in an Integrating System of a Big Data Environment?

1. ETL (Extract, Transform, Load) tools

2. Apache Kafka

3. NoSQL databases

4. Hadoop Distributed File System (HDFS)

Answer: A. ETL (Extract, Transform, Load) tools

Q21. What is Data Transformation in Big Data Processing?

1. To store raw data in a distributed file system

2. To convert data into a structured format

3. To summarize and aggregate data for analysis

4. To visualize data using charts and graphs

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - To convert data into a structured format

Q22. What is Valuable About Cloud Computing Technologies for the Processing of Big Data?

1. Lower upfront costs and scalability

2. Higher security risks and slower performance

3. Limited storage capacity and processing power

4. Dependence on local hardware infrastructure

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘A’ - Lower upfront costs and scalability

Q23. Which of the following is a Common Challenge in Big Data Quality Management?

1. Lack of data variety

2. Low data volume

3. Data duplication and inconsistency

4. High data velocity

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ - Data duplication and inconsistency

Q24. From Different Approaches that Exist to Make Scalability Possible in Big Data Systems, Which One is Most Frequently Used for Achieving this?

1. Vertical scaling

2. Horizontal scaling

3. Static partitioning

4. Sequential processing

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - Horizontal scaling

Q25. Why is Performance Tuning Significant in the Process of Handling Big Data?

1. To minimize data storage costs

2. To improve processing speed and efficiency

3. To enforce data governance policies

4. To enhance data visualization capabilities

Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ - To improve processing speed and efficiency

Start Your Journey as a Data Scientist with Us 

Data scientists and analysts working with large and complex datasets must have the means to verify knowledge of its challenges. The major objectives of MCQs can be met by human resource managers, educators, or employers in assessing the understanding and capabilities a person has with big data methods and technologies. 

Interview Kickstart’s Data Science course is your companion in preparing you for your upcoming interview and cracking your dream job! 

Continuous learning and exploration of emerging technologies are always important to stay updated in this dynamic and fast-changing environment.

Happy analyzing! 

FAQs: Big Data MCQs for Data Scientists and Analysts

Q1. How can Big Data Analytics benefit businesses? 

Big Data Analytics can be beneficial to businesses in many ways: 

• Improve customer service 

• Increase worker productivity

• Reduce expenses

Q2. What are the Challenges that Come with a Big Data Project?

Big Data Projects do not come with any challenges except managerial in-house skills among the working team. 

Q3. What is the Advantage of Using Promises Instead of Callbacks? 

The advantages include: 

• It provides in-built error handling

• Improves Readability

• Reduced coupling

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