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Each table contains rows called records, while the columns represent fields or attributes belonging to those records."},{"text":"A relational database stores data in tables; each row is a field.","correct":false,"explanation":"This confuses rows with columns: a field is a single attribute, normally represented by a column. Each row in a relational database table is a record."},{"text":"A relational database stores data in graphs; each row is a record.","correct":false,"explanation":"Graphs belong to graph database models and are organised using nodes and edges rather than rows. A relational database stores records as rows in tables."},{"text":"A relational database stores data in worksheets; each row is a tuple.","correct":false,"explanation":"This borrows terminology from spreadsheets and formal relational theory. At this level, a relational database stores data in tables, with each row described as a record."}]},{"id":"ced68209-60bb-4ce6-8f06-88096fd74521","cardType":"basic","points":1,"front":"What feature lets structured data fit neatly into tables?","back":"A predictable format with known fields.","mcqOptions":[{"text":"A predictable format with known fields.","correct":true,"explanation":"Structured data follows a predefined organisation or schema, so its fields are known in advance and can be placed consistently into table columns."},{"text":"A flexible format with optional fields.","correct":false,"explanation":"Flexible or optional fields are characteristic of semi-structured data rather than structured data. Structured data fits tables because it has a predictable format with known fields."},{"text":"A hierarchical format with linked branches.","correct":false,"explanation":"A hierarchy organises data into parent-child branches, not necessarily into tables. Structured data fits neatly into tables because its predictable format defines known fields."},{"text":"A continuous format with no fields.","correct":false,"explanation":"Data without defined fields cannot be mapped neatly to table columns and is likely to be unstructured. Structured data instead has a predictable format with known fields."}]},{"id":"5e206893-612d-4b97-b0a8-890ef25ff994","cardType":"cloze","points":null,"front":"Big Data is data that will not fit usual containers such as a [ ] or simple [ ].","back":"Big Data is data that will not fit usual containers such as a single-server database or simple row-and-column structure.","mcqOptions":[{"text":"Big Data is data that will not fit usual containers such as a single-server database or simple row-and-column structure.","correct":true,"explanation":"Big Data can exceed the capacity of one server and may be too varied or unstructured for a conventional row-and-column representation."},{"text":"Big Data is data that will not fit usual containers such as a single-server database or simple tree-and-branch structure.","correct":false,"explanation":"The usual simple structure being contrasted is a row-and-column structure, not a hierarchy. Big Data may exceed both that tabular model and a single-server database."},{"text":"Big Data is data that will not fit usual containers such as a distributed database or simple row-and-column structure.","correct":false,"explanation":"Distributed databases are commonly used to accommodate Big Data across multiple machines. The container it exceeds is a single-server database, alongside a simple row-and-column structure."},{"text":"Big Data is data that will not fit usual containers such as a spreadsheet file or simple key-value structure.","correct":false,"explanation":"This replaces both defining contrasts with different examples. Big Data is characterised here as exceeding a single-server database or a simple row-and-column structure."}]},{"id":"208d2226-7430-4408-bf6e-a83c9b68ae50","cardType":"basic","points":1,"front":"Which Big Data 'V' applies when data cannot be stored or processed conveniently on one server?","back":"Volume.","mcqOptions":[{"text":"Volume.","correct":true,"explanation":"Volume concerns the quantity of data. If the dataset is too large to store or process conveniently on one server, its volume is the relevant challenge."},{"text":"Velocity.","correct":false,"explanation":"Velocity concerns how quickly data arrives and must be processed, not whether one server has enough capacity. Excessive data size is the Volume characteristic."},{"text":"Variety.","correct":false,"explanation":"Variety refers to differences in data formats and structures. When the issue is that one server cannot conveniently handle the amount of data, the relevant V is Volume."},{"text":"Veracity.","correct":false,"explanation":"Veracity concerns data quality, reliability and accuracy rather than storage capacity. Data that is too large for convenient single-server handling demonstrates Volume."}]},{"id":"5ed0a9b3-0ff0-407d-9005-eaed06dd5f6d","cardType":"cloze","points":null,"front":"In Big Data, velocity means data [ ] and often needs [ ].","back":"In Big Data, velocity means data arrives continuously and often needs quick processing.","mcqOptions":[{"text":"In Big Data, velocity means data arrives continuously and often needs quick processing.","correct":true,"explanation":"Velocity describes the ongoing speed at which data is generated or received. Continuous incoming streams often have to be processed quickly to remain useful."},{"text":"In Big Data, velocity means data arrives continuously and often needs batch processing.","correct":false,"explanation":"Batch processing delays work until data has accumulated, so it does not capture the time-sensitive challenge. Velocity means data arrives continuously and often requires quick processing."},{"text":"In Big Data, velocity means data arrives at intervals and often needs quick processing.","correct":false,"explanation":"This mistakes periodic collection for the continuous flow associated with velocity. In Big Data, velocity means data arrives continuously and often needs quick processing."},{"text":"In Big Data, velocity means data changes format frequently and often needs quality checking.","correct":false,"explanation":"Changing formats relates to Variety, while quality checking relates to Veracity. Velocity means that data arrives continuously and often has to be processed quickly."}]},{"id":"04248c36-db9e-42c1-a12b-f95bd3ac3506","cardType":"basic","points":1,"front":"Which Big Data 'V' applies when data includes many different formats?","back":"Variety.","mcqOptions":[{"text":"Variety.","correct":true,"explanation":"Variety describes the range of formats and structures in a dataset, including combinations of structured, semi-structured and unstructured data."},{"text":"Volume.","correct":false,"explanation":"Volume refers to the amount of data rather than the number of formats represented. Many different data formats demonstrate the Big Data characteristic of Variety."},{"text":"Velocity.","correct":false,"explanation":"Velocity concerns how rapidly or continuously data arrives and requires processing. Differences among formats, such as text, images and tables, are examples of Variety."},{"text":"Veracity.","correct":false,"explanation":"Veracity concerns whether data is accurate, trustworthy and consistent. The presence of many different formats is instead described by the Big Data V called Variety."}]},{"id":"1faadaf5-7480-4c03-96d3-b9ee44e2a840","cardType":"basic","points":2,"front":"Why is unstructured data harder to analyse?","back":"Its meaning is not stored in known fields.","mcqOptions":[{"text":"Its meaning is not stored in known fields.","correct":true,"explanation":"Unstructured data lacks a predefined schema of named fields, so software cannot reliably locate and interpret each item using fixed table columns."},{"text":"Its meaning is stored in known fields.","correct":false,"explanation":"Known fields make data structured and easier to query using predefined columns. Unstructured data is harder to analyse because its meaning is not stored in known fields."},{"text":"It always contains too many records.","correct":false,"explanation":"Unstructured data can be small or large, so record quantity is not its defining difficulty. Analysis is harder because its meaning is not stored in known fields."},{"text":"It must use only numeric values.","correct":false,"explanation":"Unstructured data often includes text, images, audio or video rather than only numbers. Its difficulty comes from meaning not being organised into known fields."}]},{"id":"4f191e45-8b9b-4473-a60f-b2c7e0f5123b","cardType":"basic","points":2,"front":"Why can relational databases be unsuitable for varied Big Data?","back":"They expect data to fit a row-and-column format.","mcqOptions":[{"text":"They expect data to fit a row-and-column format.","correct":true,"explanation":"Relational databases rely on predefined tables containing rows and columns. Highly varied data may not share a consistent structure that fits this tabular model."},{"text":"They require data to use a graph-and-edge format.","correct":false,"explanation":"Nodes and edges belong to graph databases, not relational databases. Relational databases can be unsuitable because they expect data to fit a row-and-column format."},{"text":"They only process data in real-time streams.","correct":false,"explanation":"Relational databases are not limited to real-time streaming data and commonly store persistent records. Their limitation here is expecting a consistent row-and-column format."},{"text":"They cannot store data with known fields.","correct":false,"explanation":"Known fields are exactly what relational databases handle effectively through predefined columns. They struggle with varied Big Data when it does not fit a row-and-column format."}]},{"id":"b3897426-98ae-4e61-bf34-8c504c209bdd","cardType":"basic","points":2,"front":"Why can relational databases be unsuitable for very large Big Data?","back":"They do not scale naturally across many machines.","mcqOptions":[{"text":"They do not scale naturally across many machines.","correct":true,"explanation":"Traditional relational databases are usually designed around a single server or tightly coupled system, making horizontal scaling across numerous machines difficult for extremely large datasets."},{"text":"They cannot store data across multiple tables.","correct":false,"explanation":"Relational databases are specifically designed to organise data across multiple linked tables. The limitation is that they do not scale naturally across many machines."},{"text":"They do not support queries from many users.","correct":false,"explanation":"Relational databases can support many simultaneous users through concurrency controls. Their Big Data limitation is the difficulty of scaling naturally across many machines."},{"text":"They cannot validate data using primary keys.","correct":false,"explanation":"Primary keys are a standard relational database feature used to identify records and enforce integrity. The problem is limited natural scalability across many machines."}]},{"id":"e4af1fb7-f94a-406e-a4a6-94c9817af03f","cardType":"cloze","points":null,"front":"Machine learning helps Big Data by learning [ ] and extracting information from [ ].","back":"Machine learning helps Big Data by learning patterns from data and extracting information from messy, varied datasets.","mcqOptions":[{"text":"Machine learning helps Big Data by learning patterns from data and extracting information from messy, varied datasets.","correct":true,"explanation":"Machine learning identifies patterns within large datasets and can extract useful information even when the source data is unstructured, inconsistent or drawn from varied formats."},{"text":"Machine learning helps Big Data by learning fixed database rules and extracting information from small, uniform datasets.","correct":false,"explanation":"Machine learning discovers patterns rather than merely following fixed database rules, and it is especially valuable for extracting information from messy, varied datasets."},{"text":"Machine learning helps Big Data by learning patterns from data and extracting information from perfectly structured tables.","correct":false,"explanation":"Restricting the source to perfectly structured tables overlooks a major benefit of machine learning. It can extract useful information from messy, varied datasets."},{"text":"Machine learning helps Big Data by learning individual file names and extracting information from messy, varied datasets.","correct":false,"explanation":"Learning file names does not reveal meaningful relationships in Big Data. Machine learning instead learns patterns from data and applies them to messy, varied datasets."}]},{"id":"0714618b-6bd2-4476-8cff-a2436e65ff27","cardType":"basic","points":1,"front":"When data does not fit on one server, what type of processing is needed?","back":"Distributed processing across multiple machines.","mcqOptions":[{"text":"Distributed processing across multiple machines.","correct":true,"explanation":"Distributed processing divides a dataset or workload between multiple connected machines, allowing data too large for one server to be processed collectively."},{"text":"Sequential processing across multiple machines.","correct":false,"explanation":"Sequential processing performs tasks one after another and does not describe coordinated work divided between machines. Data exceeding one server requires distributed processing across multiple machines."},{"text":"Centralised processing across multiple machines.","correct":false,"explanation":"Centralised processing concentrates work on one main system, conflicting with the need to share an oversized workload. Distributed processing divides it across multiple machines."},{"text":"Batch processing across multiple machines.","correct":false,"explanation":"Batch processing groups jobs for execution but does not necessarily distribute them between servers. Data that cannot fit on one server requires distributed processing."}]},{"id":"04b288ed-5135-40cd-b13a-83f99493b894","cardType":"basic","points":1,"front":"What is a group of connected servers used together called?","back":"A cluster.","mcqOptions":[{"text":"A cluster.","correct":true,"explanation":"A cluster is a group of connected servers that cooperate as one system, sharing storage or processing work to handle large datasets and demanding tasks."},{"text":"A database.","correct":false,"explanation":"A database is an organised collection of data, not the connected servers themselves. A group of servers working together is called a cluster."},{"text":"A process.","correct":false,"explanation":"A process is a running program or task on a computer, rather than a collection of connected servers. Such a server group is a cluster."},{"text":"A protocol.","correct":false,"explanation":"A protocol defines communication rules between systems but is not the group of systems. Connected servers used together form a cluster."}]},{"id":"35a7fc92-67ee-47dd-88b6-0c369ccced56","cardType":"basic","points":2,"front":"Why send processing tasks to where Big Data is stored?","back":"Moving huge raw datasets across a network is slow.","mcqOptions":[{"text":"Moving huge raw datasets across a network is slow.","correct":true,"explanation":"Transferring enormous raw datasets consumes substantial bandwidth and time, so sending computation to the stored data is usually more efficient than moving the data."},{"text":"Moving huge processed results across a network is slow.","correct":false,"explanation":"Processed results are usually much smaller than the original data, so moving them is less costly. The main problem is transferring huge raw datasets across the network."},{"text":"Running small processing tasks across a network is slow.","correct":false,"explanation":"Sending compact processing instructions normally requires far less network capacity than transferring the underlying data. The bottleneck is moving huge raw datasets."},{"text":"Storing huge raw datasets across a network is impossible.","correct":false,"explanation":"Distributed systems can store large datasets across networked machines, so it is not impossible. The issue is that repeatedly moving huge raw datasets is slow."}]},{"id":"2da207c4-7a34-46e3-8ea1-c8ba5c599c96","cardType":"basic","points":1,"front":"After machines process separate chunks, what happens to their results?","back":"Partial results are combined.","mcqOptions":[{"text":"Partial results are combined.","correct":true,"explanation":"Each machine produces a result for its assigned chunk, and these partial results are then combined to create the final output for the complete dataset."},{"text":"Partial results are discarded.","correct":false,"explanation":"Discarding each machine’s output would prevent the system from producing an overall answer. The partial results must be combined into the final result."},{"text":"Raw datasets are combined.","correct":false,"explanation":"The data chunks are processed separately to avoid unnecessary movement of raw data. It is the resulting partial outputs that are combined afterwards."},{"text":"Processing tasks are combined.","correct":false,"explanation":"Tasks are divided among machines before processing rather than combined afterwards. Once processing finishes, the machines’ partial results are combined."}]},{"id":"ad38408b-d27e-41d2-af6b-b12c5b72b0fd","cardType":"cloze","points":null,"front":"Functional programming is based on evaluating [ ] and avoiding changes to [ ].","back":"Functional programming is based on evaluating functions and avoiding changes to shared state.","mcqOptions":[{"text":"Functional programming is based on evaluating functions and avoiding changes to shared state.","correct":true,"explanation":"Functional programming emphasises evaluating functions without side effects and minimising changes to shared state, making concurrent and distributed execution easier to reason about."},{"text":"Functional programming is based on evaluating procedures and avoiding changes to local variables.","correct":false,"explanation":"Procedural execution and avoiding local variables do not define functional programming. It centres on evaluating functions while avoiding changes to shared state."},{"text":"Functional programming is based on evaluating functions and avoiding changes to program control.","correct":false,"explanation":"Functional programs still require control over evaluation and execution. Their key restriction is avoiding changes to shared state, which reduces side effects and interference."},{"text":"Functional programming is based on evaluating database queries and avoiding changes to shared state.","correct":false,"explanation":"Database queries may use functional ideas, but they are not the foundation of the paradigm. Functional programming is based on evaluating functions and avoiding shared-state changes."}]},{"id":"ba4df65e-bdda-4425-82ef-8efa25fbf9d5","cardType":"basic","points":2,"front":"How do immutable data structures help distributed programs?","back":"One machine cannot unexpectedly alter data another uses.","mcqOptions":[{"text":"One machine cannot unexpectedly alter data another uses.","correct":true,"explanation":"Immutable data cannot be modified after creation, preventing one machine from changing shared information while another machine is reading or processing it."},{"text":"One machine cannot unexpectedly access data another uses.","correct":false,"explanation":"Distributed machines may need to access the same data, and immutability does not prevent reading. It prevents one machine from unexpectedly altering data another uses."},{"text":"One machine cannot unexpectedly copy data another uses.","correct":false,"explanation":"Immutability does not prevent data from being copied between machines. It ensures that existing data cannot be altered unexpectedly while another machine uses it."},{"text":"One machine cannot unexpectedly locate data another uses.","correct":false,"explanation":"Finding data is unrelated to whether a structure is mutable. Immutable structures help because one machine cannot unexpectedly alter data being used elsewhere."}]},{"id":"fede5dd2-91aa-431b-8b77-954ef26fe97e","cardType":"basic","points":1,"front":"What must a stateless function's result depend on?","back":"Only its input.","mcqOptions":[{"text":"Only its input.","correct":true,"explanation":"A stateless function uses only the input supplied for that invocation, so identical inputs produce identical results without relying on stored state or previous calls."},{"text":"Only its stored state.","correct":false,"explanation":"Relying on stored state would make the function stateful because its result could change between calls. A stateless function’s result depends only on its input."},{"text":"Only its previous output.","correct":false,"explanation":"Using a previous output introduces dependence on earlier calls, which is a form of state. The result of a stateless function depends only on its current input."},{"text":"Only its server environment.","correct":false,"explanation":"Depending on server-specific conditions could produce different results after migration or failure. A stateless function must calculate its result using only the supplied input."}]},{"id":"de8e6cbe-d64f-4343-9639-47596cc0d05a","cardType":"basic","points":2,"front":"Why does statelessness help if a server fails?","back":"Another server can repeat the task with the same input.","mcqOptions":[{"text":"Another server can repeat the task with the same input.","correct":true,"explanation":"Because no previous state is required, the task can be sent to another server with the same input and repeated to obtain the same result."},{"text":"Another server can restore the state without the same input.","correct":false,"explanation":"Restoring state is unnecessary for a stateless task, and the original input is still required. Another server can repeat the task by receiving the same input."},{"text":"Another server can recover the memory from the failed server.","correct":false,"explanation":"A failed server’s volatile memory may be unavailable, so recovery cannot depend on it. Statelessness allows another server to repeat the task using the same input."},{"text":"Another server can continue the task using stored session data.","correct":false,"explanation":"Continuing from stored session data relies on state and may bind the task to a particular server. A stateless task can instead be repeated with the same input."}]},{"id":"3cb93416-89f9-431c-bcfe-ffcaa5598bb9","cardType":"basic","points":1,"front":"Which functional feature helps apply the same operation to many records?","back":"Higher-order functions.","mcqOptions":[{"text":"Higher-order functions.","correct":true,"explanation":"Higher-order functions can take functions as arguments, allowing one operation to be supplied and applied systematically across many records, as with map-style processing."},{"text":"Recursive functions.","correct":false,"explanation":"Recursion makes a function call itself and is mainly used for repetitive or self-similar problems. Higher-order functions support applying a supplied operation across many records."},{"text":"Global variables.","correct":false,"explanation":"Global variables provide shared mutable state rather than a mechanism for applying an operation repeatedly. Higher-order functions can accept the operation and apply it to many records."},{"text":"Mutable data structures.","correct":false,"explanation":"Mutability allows stored values to be changed, but it does not itself apply one operation across a dataset. Higher-order functions provide that reusable processing pattern."}]},{"id":"fc6850af-8f0a-4da9-8224-3bcef32ada56","cardType":"cloze","points":null,"front":"A fact-based model stores data as separate [ ], each recording [ ].","back":"A fact-based model stores data as separate facts, each recording one piece of information.","mcqOptions":[{"text":"A fact-based model stores data as separate facts, each recording one piece of information.","correct":true,"explanation":"Fact-based modelling breaks knowledge into individual facts, with each fact expressing one specific piece of information rather than placing an entire entity in one fixed record."},{"text":"A fact-based model stores data as separate records, each recording one piece of information.","correct":false,"explanation":"Calling the units records confuses fact-based modelling with conventional record-based storage. The model stores separate facts, each of which records one piece of information."},{"text":"A fact-based model stores data as separate facts, each recording an entire database table.","correct":false,"explanation":"A fact is deliberately small and does not contain a whole table. Fact-based models store separate facts, with each fact recording one piece of information."},{"text":"A fact-based model stores data as separate objects, each recording all information about one thing.","correct":false,"explanation":"Grouping all information about an entity into one object is an object-based approach. A fact-based model instead stores separate facts, each recording one piece of information."}]},{"id":"fa54c201-4c14-4aca-8de0-96305efe98d4","cardType":"basic","points":2,"front":"Why can fact-based models suit complex datasets?","back":"New fact types can be added without redesigning one fixed table.","mcqOptions":[{"text":"New fact types can be added without redesigning one fixed table.","correct":true,"explanation":"Facts are stored independently by type, so a model can be extended with new kinds of facts without altering the columns of a single rigid table."},{"text":"Every fact type must use the same columns in one fixed table.","correct":false,"explanation":"Forcing every fact type into identical columns recreates the rigidity of a fixed relational structure. Fact-based models suit complexity because new fact types can be added without redesigning one table."},{"text":"Complex relationships are removed before the data is stored.","correct":false,"explanation":"Removing relationships would discard important information rather than model complexity. Fact-based models remain flexible because new fact types can be introduced without redesigning a fixed table."},{"text":"All facts are combined into one permanently fixed table.","correct":false,"explanation":"Combining everything into a permanently fixed table makes structural changes difficult. Fact-based modelling allows new fact types to be added without redesigning one fixed table."}]},{"id":"b3989408-4fa1-4c0a-a525-866b1c639e31","cardType":"cloze","points":null,"front":"In a graph schema, [ ] represent things and [ ] represent relationships.","back":"In a graph schema, nodes represent things and edges represent relationships.","mcqOptions":[{"text":"In a graph schema, nodes represent things and edges represent relationships.","correct":true,"explanation":"A graph models entities or other things as nodes, while edges connect those nodes to represent the relationships that exist between them."},{"text":"In a graph schema, nodes represent things and properties represent relationships.","correct":false,"explanation":"Properties store named values or attributes, not the connections between things. In a graph schema, nodes represent things and edges represent relationships."},{"text":"In a graph schema, properties represent things and edges represent relationships.","correct":false,"explanation":"Properties describe nodes or edges by storing named values; they are not the main representation of things. Nodes represent things, while edges represent relationships."},{"text":"In a graph schema, edges represent things and nodes represent relationships.","correct":false,"explanation":"This reverses the two central graph components. Nodes represent entities or things, while edges connect nodes and therefore represent relationships between them."}]},{"id":"3e2b49d5-02d3-4b04-9ce5-44a39bb06445","cardType":"basic","points":1,"front":"In a graph schema, what represents a named value such as age or timestamp?","back":"A property.","mcqOptions":[{"text":"A property.","correct":true,"explanation":"A property is a named attribute attached to a node or edge, storing a value such as an age, name, weight or timestamp."},{"text":"A node.","correct":false,"explanation":"A node represents a thing or entity, not one named value describing it. A value such as age or timestamp is stored as a property."},{"text":"An edge.","correct":false,"explanation":"An edge represents a relationship connecting nodes rather than an individual named value. Values such as age or timestamp are represented by properties."},{"text":"A schema.","correct":false,"explanation":"A schema defines the permitted structure of the graph rather than storing a particular named value. An age or timestamp is represented as a property."}]}]}],"examCountdown":{"examDate":"2027-06-07","paperLabel":"Paper 1"},"heading":"Big Data","title":"Big Data Flashcards","topbar":{"backHref":"/a-level/computer-science/aqa/flashcards"},"topicSidebar":"$L37"}]}],"topicId":"64e9d874-23b4-5667-9d04-8e183dedb169","walled":"$L38"}],"$L39","$L3a"]