Lesson 4.11.1.1

4.11.1.1 Big Data Quiz: AQA Computer Science, Unit 11

20 questions

Written by Revision Ninja · Last reviewed 11 October 2026

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Lesson 4.11.1.1, Big Data: 20 multiple choice questions for the AQA Computer Science (7517), Unit 11: Big Data, written with Revision Ninja.

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The 20 questions

  1. Which of these is one of the three characteristics of Big Data described in the specification?

    • Virtualisation
    • Velocity
    • Vendor
    • Validation

  2. What does volume mean when describing Big Data?

    • Data that is too big to fit into a single server.
    • The number of different file types that are stored in one database.
    • The number of users who connect to a web server each day.
    • The loudness of a data stream that is captured by a sensor in the field.

  3. What does velocity mean when describing Big Data?

    • The number of fields contained in each record of a relational table.
    • Streaming data that must be responded to within milliseconds to seconds.
    • The size in bytes of each individual file uploaded to a server.
    • The speed at which a processor completes one single machine instruction, measured in cycles per second on the processor's clock.

  4. What does variety mean when describing Big Data?

    • Data that is always stored as identical rows and columns in a single table.
    • Data in many forms, such as structured, unstructured, text and multimedia.
    • Many different database vendors offering products that are very similar to each other.
    • The number of different programming languages that a development team is able to use.

  5. Why are relational databases often not appropriate for much Big Data?

    • They are only available for single-user systems and cannot be shared between users, so each person needs a separate copy of the data.
    • They require data to fit into rows and columns and do not scale well across many machines.
    • They are always too slow to read any data, however small the data set may be.
    • They cannot store any numeric values, and Big Data is made up mostly of numbers.

  6. Which technique is needed to find patterns in large unstructured data sets?

    • Machine learning techniques.
    • Client-side rendering of the data inside a thin client device.
    • Manual data entry by a team of clerks checking each record one by one.
    • Normalisation of the data to third normal form in a relational schema.

  7. When data is too big to fit on a single server, how must the processing be handled?

    • It must be carried out by the printer queue of the server, one job at a time.
    • It must be limited to a single core of one processor in the server room.
    • It must be stored in one file of unlimited size on the server's local disk.
    • It must be distributed across more than one machine.

  8. Which features of functional programming make it easier to write correct code?

    • Long sequences of jump statements that move control between sections of code.
    • Immutable data structures and statelessness.
    • Mutable shared state that many threads update at the same time.
    • Global variables that every function is free to change at any moment.

  9. Which feature of functional programming supports running code on many servers?

    • Mutable objects that each server changes in place as it processes the data.
    • Statelessness, so functions can run on any machine without relying on shared state.
    • Reliance on a shared global variable that every server must update in turn.
    • Procedures that depend on the exact order of statements to produce each result, so that every statement must run in a fixed sequence.

  10. In a fact-based model, what does each fact capture?

    • An entire table of related records stored together in a single row.
    • A complete copy of the database schema for every user of the system.
    • A single piece of information.
    • The name of the programming language that is used to query the dataset.

  11. In a graph schema, what do nodes represent?

    • The physical servers on which the data set is stored.
    • The connections between two records held in a relational table.
    • Entities in the data set, such as a person or a product.
    • The data types assigned to each attribute of the schema.

  12. In a graph schema, what does an edge represent?

    • A duplicate copy of a node that is kept for backup purposes only.
    • A property that describes a single node in the data set in detail.
    • The identifier of the server on which the node is currently stored.
    • A relationship between two nodes.

  13. In a graph schema, what is a property?

    • A link that always connects one node to exactly one other node in the graph.
    • An attribute value attached to a node or an edge, such as a name or a date.
    • A table that stores all of the nodes of the graph in their original order.
    • A rule that removes duplicate edges from the data set whenever it is loaded.

  14. In a social network graph, Alice follows Bob since 2020. Which best describes this?

    • A property on Alice's node that stores the whole record for Bob.
    • A node labelled 2020 that is connected to both Alice and Bob in the graph.
    • Two separate nodes, one for Alice and one for Bob, with no link between them, and each node stores the date of its creation as a property.
    • An edge between the Alice and Bob nodes, with a property recording the year 2020.

  15. A dataset holds sensor readings streamed to the server every second. Which Big Data characteristic is most prominent?

    • Relational integrity, because each reading has a foreign key pointing to a sensor.
    • Velocity
    • Variety, because the readings always come in one fixed format from every sensor.
    • Normalisation, because the readings must be split into several related tables.

  16. A dataset mixes video, text comments and structured sales records. Which characteristic is most evident?

    • Velocity, because the sales records are updated every second on the server.
    • Variety
    • Volume, because the video files are small in size compared with the records.
    • Primary key uniqueness, because every record in the data set has an identifier.

  17. A company holds a data set too large for one server. Which approach is appropriate?

    • Distribute the data and processing across several machines, using functional programming to keep the code correct.
    • Remove the unstructured data so that the remaining data fits on one server.
    • Upgrade the processor of the single server and keep all of the data in one table.
    • Store all of the data in one spreadsheet file on a local hard drive of a desktop.

  18. Why do immutable data structures help when processing is distributed?

    • They mean that each machine must lock the entire data set before it reads any value.
    • They allow every machine to edit the same value at the same time without any coordination.
    • They ensure that all data is always stored in a relational table format on each machine.
    • Values never change once created, so copies held on different machines stay consistent.

  19. Evaluate: why are unstructured data sources hard to analyse with relational databases?

    • They must be stored in a graph schema, which relational systems do not support at all.
    • Their content does not fit into fixed rows and columns, so patterns must be found by other means such as machine learning.
    • They are always stored in binary form, which relational systems are unable to read.
    • They have too few attributes to be meaningful when placed in a table structure.

  20. A stateless function given the same input always returns the same output. Why does this help in distributed systems?

    • The function can only run on the server that originally created the data it uses.
    • Any server can run the function without needing to know the state held on other servers.
    • It removes the need for any networking between the servers in the cluster at all.
    • It guarantees that each server stores a copy of every previous result it has produced, so that results never need to be recalculated.

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