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.Â
In this article, we present the big data MCQs for data scientists and analysts, and the 5 V’s of big data.Â
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.Â
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.
Let’s look at the Big Data MCQs for data scientists & analysts:
Data with a large file size
Data with high velocity and variety that exceeds traditional data processing capabilities
Data with high-security requirements
Data with a high level of accuracy
Answer: B. Data with high velocity and variety that exceeds traditional data processing capabilities
Low volume
Structured format
Low velocity
Predictable variety
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ – Structured format
It is a process of gathering huge amounts of data.
The process of analysis of large and complicated data helps in revealing hidden patterns, trends, and insights.
The process of securing big data
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
Regression analysis
Linear programming
Gradient descent
Machine learning
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘D’ – Machine learning
A programming language for big data analytics
A distributed file system for storing big data
A framework for distributed storage and processing of big data
A database management system for big data
Answer: C. A framework for distributed storage and processing of big data
A programming language
A distributed computing model for processing big data
A database query language
A data visualization tool
Answer: B. A distributed computing model for processing big data
Map phase
Shuffle phase
Reduce phase
Merge phase
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ – Reduce phase
Data storage
Data visualization
Data processing and analytics
Data security
Answer: C. Data processing and analytics
Hadoop
Apache Kafka
MySQL
MongoDB
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ – Apache Kafka
MySQL
PostgreSQL
MongoDB
SQLite
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ – MongoDB
To store real-time data streams
To store structured data in relational databases
To integrate and analyze data from multiple sources for reporting and analysis
To store unstructured data such as images and videos
Answer: C. To integrate and analyze data from multiple sources for reporting and analysis
Apache Spark
Apache Kafka
Apache Storm
Apache Flink
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘D’ – Apache Flink
Spark supports only batch processing.
Spark provides in-memory processing, which makes it faster than disk-based MapReduce.
Spark does not support data streaming.
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.
Tableau
Microsoft Excel
Power BI
MATLAB
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘A’ – Tableau
To visualize and summarize data to understand its underlying structure
To clean and preprocess raw data before analysis
To perform hypothesis testing on large datasets
To deploy machine learning models for prediction
Answer: A. To visualize and summarize data to understand its underlying structure
Limited storage capacity
Lack of data variety
Data privacy and confidentiality concerns
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
The process of ensuring data consistency and accuracy
The process of managing data access and permissions
The process of defining data quality standards and policies
The process of storing and retrieving large volumes of data
Answer: C. The process of defining data quality standards and policies
K-means clustering
Linear regression
Random forest
Principal component analysis (PCA)
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ – Random forest
To analyze historical data and identify patterns
To summarize and visualize large datasets
To predict future outcomes based on historical data
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
ETL (Extract, Transform, Load) tools
Apache Kafka
NoSQL databases
Hadoop Distributed File System (HDFS)
Answer: A. ETL (Extract, Transform, Load) tools
To store raw data in a distributed file system
To convert data into a structured format
To summarize and aggregate data for analysis
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
Lower upfront costs and scalability
Higher security risks and slower performance
Limited storage capacity and processing power
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
Lack of data variety
Low data volume
Data duplication and inconsistency
High data velocity
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘C’ – Data duplication and inconsistency
Vertical scaling
Horizontal scaling
Static partitioning
Sequential processing
Answer: The correct answer to this big data MCQ for data scientists and analysts is ‘B’ – Horizontal scaling
To minimize data storage costs
To improve processing speed and efficiency
To enforce data governance policies
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
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.Â
Interview Kickstart’s Data Science course is your companion in preparing you for your upcoming interview and cracking your dream job!Â
Continuous learning and exploration of emerging technologies are always important to stay updated in this dynamic and fast-changing environment.
Happy analyzing!Â
Q1. How can Big Data Analytics benefit businesses?Â
Big Data Analytics can be beneficial to businesses in many ways:Â
Improve customer serviceÂ
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.Â
Q3. What is the Advantage of Using Promises Instead of Callbacks?Â
The advantages include:Â
It provides in-built error handling
Improves Readability
Reduced coupling
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