Data Science & Big Data Quiz

Question 1

What are the '3 Vs' that define Big Data?

Accepted Answer

Volume, Velocity, Variety

Answer

Value, Velocity, Variance

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Volume, Validity, Velocity

Answer

Value, Volume, Veracity

Question 2

What is a 'data scientist' and what skills does the role typically require?

Accepted Answer

A professional who collects, cleans, analyses, and interprets large datasets, combining programming, statistics, and domain expertise

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A scientist who studies climate data exclusively

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A database administrator who manages enterprise data systems

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An AI engineer who builds machine learning models only

Question 3

What is 'machine learning' and how does it differ from traditional programming?

Accepted Answer

In traditional programming, humans write explicit rules; in machine learning, algorithms learn patterns from data to make predictions or decisions without being explicitly programmed

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Machine learning is faster computing; traditional programming is slower

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Machine learning uses quantum computers; traditional programming uses classical computers

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Machine learning only works for image recognition; traditional programming handles everything else

Question 4

What is a 'neural network' in machine learning?

Accepted Answer

A computational model loosely inspired by the human brain, using layers of interconnected nodes (neurons) to process and learn from data

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A physical network of computing nodes

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A type of database optimised for large datasets

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A system for connecting multiple computers for parallel processing

Question 5

What is 'overfitting' in machine learning?

Accepted Answer

When a model learns training data too precisely — memorising noise rather than patterns — and performs poorly on new data

Answer

When a model takes too long to train

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When a model is too large to run on available hardware

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When too much data is used for training, leaving insufficient data for testing

Question 6

What is 'SQL' and why is it fundamental to data science?

Accepted Answer

Structured Query Language — used to query, manipulate, and manage relational databases

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Statistical Query Language — a statistical software package

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Scalable Query Logic — a machine learning framework

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System Quality Language — a software testing standard

Question 7

What is the purpose of 'data visualisation' in data science?

Accepted Answer

Representing data graphically to communicate insights, reveal patterns, and enable understanding that would be difficult to extract from raw numbers

Answer

Creating beautiful graphics for marketing materials

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Converting data into a format computers can process

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Storing large datasets more efficiently using visual compression

Question 8

What is 'A/B testing' in data science and technology?

Accepted Answer

A controlled experiment comparing two versions (A and B) of something — like a webpage or feature — to determine which performs better statistically

Answer

Testing two different machine learning algorithms against each other

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Comparing datasets from two different sources for accuracy

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A testing framework for software code quality

Question 9

What is 'Hadoop' and what problem did it solve in big data processing?

Accepted Answer

An open-source framework for distributed storage and processing of very large datasets across clusters of commodity hardware

Answer

A programming language for statistical analysis

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A database management system for structured data

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A cloud computing platform by Amazon

Question 10

What does 'ETL' stand for in data engineering?

Accepted Answer

Extract, Transform, Load — the process of moving data from source systems to a data warehouse

Answer

Encrypt, Transform, Load

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Evaluate, Test, Launch

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Entities, Tables, Links — a database design methodology

Question 11

What is the difference between 'supervised' and 'unsupervised' machine learning?

Accepted Answer

Supervised learning trains on labelled data (known inputs and outputs); unsupervised learning finds patterns in unlabelled data without predefined output labels

Answer

Supervised learning requires a teacher; unsupervised learning is self-taught by the computer

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Supervised learning is more accurate; unsupervised learning is faster

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Supervised learning runs on servers; unsupervised learning runs on edge devices

Question 12

What is a 'data warehouse' and how does it differ from a regular database?

Accepted Answer

A system optimised for analytical queries on large historical datasets — structured differently from OLTP databases designed for fast individual transactions

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A physical building where computer servers are stored

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A backup storage system for important databases

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A distributed database system spread across multiple servers

Question 13

What is 'correlation' and why does the phrase 'correlation is not causation' matter in data science?

Accepted Answer

Correlation measures statistical relationship between two variables; 'not causation' reminds us that a relationship doesn't prove one thing causes the other

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Correlation measures how similar two datasets look; causation means they were collected at the same time

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Correlation and causation are identical concepts — the distinction is a philosophical technicality

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Correlation is a weak relationship; causation is a strong one

Question 14

What is 'Python' and why is it the dominant language in data science?

Accepted Answer

A general-purpose, high-level programming language favoured in data science for its readable syntax, vast library ecosystem (NumPy, Pandas, scikit-learn, TensorFlow), and active community

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A scripting language used only for web automation

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A statistical computing language developed specifically for data analysis

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A database query language for analytical workloads

Question 15

What is 'natural language processing' (NLP) and what are its common applications?

Accepted Answer

AI techniques enabling computers to understand, interpret, and generate human language — used in chatbots, sentiment analysis, translation, and voice assistants

Answer

Software that translates programming languages between each other

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A compression algorithm for storing text data efficiently

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A protocol for natural language communication between APIs

Question 16

What is the 'bias-variance tradeoff' in machine learning?

Accepted Answer

A fundamental tension where reducing model error from bias (oversimplification) increases variance (sensitivity to noise) and vice versa — finding the optimal balance is central to model selection

Answer

Balancing the trade between biased training data and variable outputs

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Choosing between biased algorithms and unbiased algorithms

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The tradeoff between model accuracy and computational cost

Question 17

What is 'GDPR' and how has it affected data science practices?

Accepted Answer

General Data Protection Regulation — EU law requiring explicit consent for data collection, right to erasure, and data minimisation — reshaping how companies collect and use personal data

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Google Data Processing Regulations — Google's internal data policies

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Global Data Privacy Rules — an international treaty on data security

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Government Data Protection Requirements — US federal data privacy law

Question 18

What is 'feature engineering' in machine learning?

Accepted Answer

The process of using domain knowledge to create or transform input variables (features) that make ML models more effective

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The process of selecting which machine learning model to use

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Designing the physical hardware features of ML computing systems

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Documenting the features and capabilities of a trained model

Question 19

What is 'Apache Spark' and how does it improve on Hadoop MapReduce?

Accepted Answer

A unified analytics engine for big data processing that processes data in-memory rather than writing to disk — making it 10–100x faster than Hadoop MapReduce for many workloads

Answer

A streaming database for real-time analytics

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A competitor to Python's Pandas library for data manipulation

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A cloud-specific data processing framework

Question 20

What is a 'confusion matrix' in machine learning evaluation?

Accepted Answer

A table summarising classification model performance — showing true positives, false positives, true negatives, and false negatives

Answer

A matrix showing how confusing training data was to process

Answer

A visualisation of how different features are correlated

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A method for comparing multiple model architectures simultaneously