1️⃣ Question 1

What key benefit does cloud computing offer compared to traditional local installations?

• ❌ Cloud computing eliminates the need for users to store any data locally.

• ✅ Users can access the latest version of applications without purchasing retail copies.

• ❌ Cloud computing requires users to purchase and install applications locally.

• ❌ Users have more control over their applications in the cloud.

Explanation:
Cloud apps are always up-to-date, subscription-based, and don’t require local installation—this is a major advantage.

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2️⃣ Question 2

Primary advantages of using cloud for data scientists?

• ❌ The Cloud allows data scientists to store data locally and use advanced algorithms.

• ✅ The Cloud enables data scientists to work with large datasets and use advanced computing tools centrally.

• ❌ Up-to-date tools but restricted to specific time zones.

• ❌ Can only be accessed from laptops.

Explanation:
Cloud provides scalable storage, high-powered computation, and centrally available advanced tools—ideal for big data workloads.

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3️⃣ Question 3

Common “V’s of Big Data”?

• ❌ Vision, Velocity, and Visualization

• ✅ Velocity, Volume, Variety, Veracity, and Value

• ❌ Volume, Vector, and Verification

• ❌ Variety, Verification, and Value

Explanation:
Big Data is typically defined by 5 Vs: Volume, Velocity, Variety, Veracity, and Value.

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4️⃣ Question 4

How has interest in data science changed in recent years?

• ❌ Interest has remained constant.

• ❌ Shifted more to parents than students.

• ✅ Interest has increased, leading to more enrollments in related undergraduate courses.

• ❌ Interest has declined.

Explanation:
Demand for data skills has surged, causing universities to expand and fill DS/BA programs quickly.

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5️⃣ Question 5

Which open-source technology supports distributed storage and processing of big data?

• ✅ Apache Hadoop

• ❌ Apache Hive

• ❌ NoSQL databases

• ❌ Apache Spark

Explanation:
Hadoop is the foundational open-source system for distributed storage (HDFS) and parallel processing (MapReduce) of big data.

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🧾 Summary Table