Data Science And Big Data MCQs

1. What is the primary goal of Data Science?

a) To replace human decision-making with AI b) To extract insights and knowledge from data c) To store large amounts of data efficiently d) To create visually appealing charts

Correct Answer: b) To extract insights and knowledge from data

Explanation: Data Science combines statistics, programming, and domain expertise to analyze data and derive meaningful insights for decision-making.

2. Which of the following is NOT a key application of Big Data?

a) Fraud detection in banking b) Personalized recommendations in e-commerce c) Storing data in a single Excel file d) Predictive maintenance in manufacturing

Correct Answer: c) Storing data in a single Excel file

Explanation: Big Data involves processing large, complex datasets that cannot be handled by traditional tools like Excel.

3. Which step comes first in the Data Science Lifecycle?

a) Model Building b) Data Collection c) Data Visualization d) Deployment

Correct Answer: b) Data Collection

Explanation: The lifecycle starts with gathering relevant data before any analysis or modeling can take place.

4. Which industry benefits from Data Science in predicting disease outbreaks?

a) Retail b) Healthcare c) Automotive d) Entertainment

Correct Answer: b) Healthcare

Explanation: Healthcare uses data science for epidemiological modeling and predicting disease spread patterns.

5. What is the purpose of the "Data Cleaning" phase in the Data Science Lifecycle?

a) To make data visually appealing b) To remove or correct inaccurate, incomplete, or irrelevant data c) To convert all data to JSON format d) To reduce the size of the dataset

Correct Answer: b) To remove or correct inaccurate, incomplete, or irrelevant data

Explanation: Data cleaning ensures data quality by handling missing values, outliers, and inconsistencies before analysis.

6. Which technique would a streaming service like Netflix most likely use for its recommendation system?

a) Linear Regression b) Collaborative Filtering c) Time Series Analysis d) Computer Vision

Correct Answer: b) Collaborative Filtering

Explanation: Collaborative filtering analyzes user behavior patterns to recommend items based on preferences of similar users.

7. What is the main challenge that Data Science helps solve in the financial sector?

a) Website design b) Risk assessment and fraud detection c) Employee scheduling d) Office space optimization

Correct Answer: b) Risk assessment and fraud detection

Explanation: Banks and financial institutions use data science to detect fraudulent transactions and assess credit risks.

8. In the context of Big Data, what does the "3Vs" model refer to?

a) Volume, Velocity, Variety b) Vision, Value, Verification c) Virtual, Valid, Valuable d) Vector, Variable, Variance

Correct Answer: a) Volume, Velocity, Variety

Explanation: The 3Vs describe key characteristics of Big Data: large amounts (Volume), high speed of generation (Velocity), and different data types (Variety).

9. Which of these is an example of supervised learning?

a) Customer segmentation b) Spam email classification c) Anomaly detection in network traffic d) Product recommendation

Correct Answer: b) Spam email classification

Explanation: Supervised learning uses labeled data (spam/not spam) to train models for classification tasks.

10. What is the purpose of A/B testing in data science applications?

a) To compare two versions of a product or feature b) To clean datasets c) To visualize data distributions d) To reduce data storage costs

Correct Answer: a) To compare two versions of a product or feature

Explanation: A/B testing helps determine which version performs better by showing different versions to different user groups.

Data Science And Big Data MCQs

Data Science Fundamentals

Data Science Lifecycle

Applications of Data Science

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