Big Data | AQA A-Level Computer Science (7517)

Big Data

42 questions
4 subtopics
Paper 2: the written exam
Paper 2

What makes data 'big', why a lack of structure rather than size defeats a relational database, distributed processing and the functional style that suits it, and fact-based and graph representations.

Examined on Paper 2.

Sample questions from Big Data

Answer each one closed book first, then open the answer.

The 4 subtopics

One subtopic is one session. Work down the list.

Subtopic	What it covers	Questions
Volume, Velocity and Variety	Recall questions on what Big Data means, the three characteristics and the test for each, structured against unstructured data, and the sources that stream continuously.	11
Why Big Data Resists Analysis	Recall questions on why a lack of structure beats size as an obstacle, why relational databases do not fit, and what machine learning is asked to do instead of a query.	10
Distributed Processing and Functional Programming	Recall questions on distributing work across machines, immutable data, statelessness, higher-order functions, and why this style suits code that runs in many places at once.	11
Fact-Based Models and Graph Schemas	Recall questions on the fact-based model and what one fact holds, nodes, edges and properties, and how a graph schema stores relationships a relational schema would join for.	10

Big Data is 42 of the 1,516 questions in the guide.Get the guide, £9

How the guide is worked

Answering a question from memory stores it far better than reading the answer again. The guide runs that as a fixed procedure on one subtopic at a time, about twenty minutes a session.

Step 1 · Closed book
Cover the answers. Work through one subtopic and write down what you can. Leave blanks where you have nothing.
Step 2 · Open book
Go back to the top. Read each printed answer and write it out in full, including the ones you had right.
Step 3 · Closed book again
Same questions, same order, from memory. The gap between pass one and pass three is the session result.

Read the full method, the return schedule and the research behind it.