Specification Map & Revision Checklist

This lesson provides a comprehensive map of the AQA A-Level Computer Science specification (7517), showing exactly what each section covers, which paper tests it, how frequently each topic appears in past papers, and a structured revision checklist to ensure you have covered everything. Use this as your master reference for revision planning.

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The AQA A-Level CS Specification: Structure Overview

The AQA specification is divided into 12 sections (4.1–4.12). Understanding which sections are tested on which paper is essential for targeted revision.

Section	Topic	Paper 1	Paper 2	Priority
4.1	Fundamentals of programming	Yes	Yes	Very High
4.2	Fundamentals of data structures	Yes	Yes	Very High
4.3	Fundamentals of algorithms	Yes	Yes	Very High
4.4	Theory of computation	Yes	Yes	High
4.5	Fundamentals of data representation	Yes	Yes	High
4.6	Fundamentals of computer systems	Yes	Yes	Medium-High
4.7	Fundamentals of computer organisation and architecture	Yes	Yes	Medium-High
4.8	Consequences of uses of computing	No	Yes	Medium
4.9	Fundamentals of communication and networking	No	Yes	Medium-High
4.10	Fundamentals of databases	No	Yes	Medium-High
4.11	Big Data	No	Yes	Medium
4.12	Fundamentals of functional programming	No	Yes	Medium-High

Key Point: Sections 4.1–4.7 are tested on BOTH papers. Sections 4.8–4.12 are tested on Paper 2 ONLY. This means that if you are strong in programming and algorithms (4.1–4.3), you have a major advantage on both papers.

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Section 4.1: Fundamentals of Programming

This is the largest and most important section of the specification. It covers all aspects of programming that you are expected to know and be able to implement.

Sub-Topic	Key Content	Exam Focus
Data types	Integer, real/float, Boolean, character, string, date/time, pointer/reference	Know the purpose and range of each type; when to use each
Programming concepts	Variable declaration, assignment, iteration (for, while, repeat), selection (if, case), nesting	Write code using all of these constructs fluently
Arithmetic operations	+, -, *, /, DIV (integer division), MOD (modulus), exponentiation	Know the difference between DIV and / ; MOD is frequently tested
Relational operations	=, <>, <, >, <=, >=	Used in conditions for selection and iteration
Boolean operations	AND, OR, NOT, XOR	Truth tables, Boolean expressions, simplification
Constants and variables	Difference between constants and variables; when to use each	Explain why constants improve maintainability and prevent accidental changes
String handling	Length, substring, concatenation, character codes, conversion	Write string processing functions from scratch
Exception handling	Try-catch/try-except, raising exceptions, custom exceptions	Know how to handle runtime errors gracefully
Subroutines	Functions (return a value) vs procedures (do not), parameters (by value vs by reference), local vs global scope	Write functions and procedures; explain parameter passing mechanisms
Recursion	Base case, recursive case, call stack, stack overflow, comparison with iteration	Trace recursive code; convert between iterative and recursive solutions
Object-oriented programming	Classes, objects, instantiation, encapsulation, inheritance, polymorphism, overriding, aggregation, composition, association	Write class definitions; explain OOP principles; draw class diagrams
File handling	Open, read, write, append, close; text files; binary files; CSV processing	Write code to read from and write to files

Revision Checklist: Section 4.1

Topic	Can Define	Can Explain	Can Code	Practised Exam Qs
Data types and type casting	[ ]	[ ]	[ ]	[ ]
Selection (if/else/case)	[ ]	[ ]	[ ]	[ ]
Iteration (for/while/repeat)	[ ]	[ ]	[ ]	[ ]
Arithmetic (DIV, MOD)	[ ]	[ ]	[ ]	[ ]
Boolean operations	[ ]	[ ]	[ ]	[ ]
String handling	[ ]	[ ]	[ ]	[ ]
Exception handling	[ ]	[ ]	[ ]	[ ]
Subroutines and parameters	[ ]	[ ]	[ ]	[ ]
Recursion	[ ]	[ ]	[ ]	[ ]
OOP (all principles)	[ ]	[ ]	[ ]	[ ]
File handling	[ ]	[ ]	[ ]	[ ]

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Section 4.2: Fundamentals of Data Structures

Sub-Topic	Key Content	Exam Focus
Arrays	1D and 2D arrays, indexing, traversal	Implement operations on arrays
Records	Composite data types with named fields	Define and use records/structs
Stacks	LIFO structure; push, pop, peek, isEmpty	Implement stack operations; explain use cases (expression evaluation, backtracking, call stack)
Queues	FIFO structure; enqueue, dequeue; linear and circular variants; priority queues	Implement queue operations; explain circular queue mechanics
Linked lists	Dynamic structure with nodes and pointers; singly and doubly linked	Implement insert, delete, traverse; compare with arrays
Trees	Binary trees, binary search trees; in-order, pre-order, post-order traversal	Traverse trees; insert/search BST; draw trees from traversal sequences
Hash tables	Hash functions, collision resolution (linear probing, chaining), load factor	Explain hashing; calculate hash values; handle collisions
Dictionaries	Key-value pairs, access by key	Implement dictionary operations
Vectors	Mathematical vectors, vector operations, representation as lists/arrays or as functions	Perform vector addition, scalar multiplication; represent vectors as dictionaries

Revision Checklist: Section 4.2

Topic	Can Define	Can Explain	Can Code	Practised Exam Qs
Arrays (1D and 2D)	[ ]	[ ]	[ ]	[ ]
Records	[ ]	[ ]	[ ]	[ ]
Stacks (push, pop, peek)	[ ]	[ ]	[ ]	[ ]
Queues (enqueue, dequeue, circular)	[ ]	[ ]	[ ]	[ ]
Linked lists (insert, delete, traverse)	[ ]	[ ]	[ ]	[ ]
Binary trees and BSTs	[ ]	[ ]	[ ]	[ ]
Hash tables and collision resolution	[ ]	[ ]	[ ]	[ ]
Dictionaries	[ ]	[ ]	[ ]	[ ]
Vectors	[ ]	[ ]	[ ]	[ ]

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Section 4.3: Fundamentals of Algorithms

Sub-Topic	Key Content	Exam Focus
Graph traversals	Breadth-first search (BFS), depth-first search (DFS)	Trace BFS/DFS on a given graph; compare the two approaches
Tree traversals	In-order, pre-order, post-order	Write the traversal output for a given tree
Dijkstra's algorithm	Shortest path in a weighted graph	Trace Dijkstra's on a small graph; show the distance table at each step
Searching	Linear search, binary search	Implement both; compare time complexity; know when binary search is applicable
Sorting	Bubble sort, merge sort	Trace both algorithms; compare time and space complexity; implement in code
Optimisation	Understanding of tractable and intractable problems	Know examples of each; explain why some problems are computationally hard

Revision Checklist: Section 4.3

Topic	Can Define	Can Trace	Can Code	Practised Exam Qs
BFS and DFS	[ ]	[ ]	[ ]	[ ]
Tree traversals (in/pre/post)	[ ]	[ ]	[ ]	[ ]
Dijkstra's algorithm	[ ]	[ ]	[ ]	[ ]
Linear search	[ ]	[ ]	[ ]	[ ]
Binary search	[ ]	[ ]	[ ]	[ ]
Bubble sort	[ ]	[ ]	[ ]	[ ]
Merge sort	[ ]	[ ]	[ ]	[ ]

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Section 4.4: Theory of Computation

Sub-Topic	Key Content	Exam Focus
Abstraction and automation	Representational abstraction, abstraction by generalisation, information hiding, procedural abstraction	Define and give examples of each type
Finite state machines (FSMs)	Mealy machines, Moore machines, state transition diagrams, state transition tables	Draw and interpret FSMs; determine whether an input string is accepted
Regular expressions	Pattern matching syntax, metacharacters	Write regular expressions for given patterns; determine what strings match
Context-free languages	Backus-Naur Form (BNF), syntax diagrams	Write BNF for simple languages; interpret syntax diagrams
Turing machines	Universal Turing machine, Church-Turing thesis	Explain the concept of a Turing machine; describe its components
Classification of algorithms	Time complexity, Big O notation, tractable vs intractable, computable vs non-computable	Classify algorithms by time complexity; explain the halting problem
Halting problem	Proof that it is undecidable	Explain why no algorithm can determine whether any arbitrary program halts

Revision Checklist: Section 4.4

Topic	Can Define	Can Explain	Can Apply	Practised Exam Qs
Abstraction types	[ ]	[ ]	[ ]	[ ]
Finite state machines	[ ]	[ ]	[ ]	[ ]
Regular expressions	[ ]	[ ]	[ ]	[ ]
BNF and syntax diagrams	[ ]	[ ]	[ ]	[ ]
Turing machines	[ ]	[ ]	[ ]	[ ]
Big O notation	[ ]	[ ]	[ ]	[ ]
Halting problem	[ ]	[ ]	[ ]	[ ]

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Section 4.5: Fundamentals of Data Representation

Sub-Topic	Key Content	Exam Focus
Number systems	Binary, denary, hexadecimal; conversion between systems	Perform conversions fluently and quickly
Binary arithmetic	Addition, subtraction (two's complement), multiplication, overflow	Perform binary arithmetic; identify overflow
Floating-point representation	Mantissa, exponent, normalisation, precision vs range trade-off	Convert between denary and floating-point; normalise
Bit manipulation	Shifts (logical and arithmetic), masks, AND/OR/XOR operations	Apply bit operations; explain their uses
Character encoding	ASCII, Unicode (UTF-8, UTF-16, UTF-32)	Explain differences; calculate storage requirements
Analogue/digital conversion	Sampling, sampling rate, bit depth, Nyquist theorem	Calculate file sizes; explain quality trade-offs
Compression	Lossy vs lossless, RLE, Huffman coding, dictionary-based	Compare approaches; calculate compressed sizes

Revision Checklist: Section 4.5

Topic	Can Define	Can Calculate	Can Explain	Practised Exam Qs
Number base conversions	[ ]	[ ]	[ ]	[ ]
Binary arithmetic	[ ]	[ ]	[ ]	[ ]
Floating-point representation	[ ]	[ ]	[ ]	[ ]
Bit manipulation	[ ]	[ ]	[ ]	[ ]
Character encoding	[ ]	[ ]	[ ]	[ ]
Analogue/digital conversion	[ ]	[ ]	[ ]	[ ]
Compression algorithms	[ ]	[ ]	[ ]	[ ]

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Section 4.6: Fundamentals of Computer Systems

Sub-Topic	Key Content	Exam Focus
Hardware and software	Relationship between hardware and software; system software vs application software	Define and distinguish categories
Classification of languages	Low-level (machine code, assembly), high-level; advantages/disadvantages of each	Compare language levels; explain when each is appropriate
Translators	Compilers, interpreters, assemblers; stages of compilation (lexical analysis, syntax analysis, code generation, optimisation)	Explain each translator type; describe compilation stages
Boolean algebra	Laws (De Morgan's, commutative, associative, distributive), simplification, Karnaugh maps	Simplify Boolean expressions; draw logic circuits
Logic gates	AND, OR, NOT, NAND, NOR, XOR; truth tables; circuit diagrams	Draw and interpret logic circuits; construct truth tables

Revision Checklist: Section 4.6

Topic	Can Define	Can Explain	Can Apply	Practised Exam Qs
Hardware vs software	[ ]	[ ]	[ ]	[ ]
Language classification	[ ]	[ ]	[ ]	[ ]
Compilers and interpreters	[ ]	[ ]	[ ]	[ ]
Stages of compilation	[ ]	[ ]	[ ]	[ ]
Boolean algebra and simplification	[ ]	[ ]	[ ]	[ ]
Logic gates and circuits	[ ]	[ ]	[ ]	[ ]

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Section 4.7: Fundamentals of Computer Organisation and Architecture

Sub-Topic	Key Content	Exam Focus
Von Neumann architecture	Stored program concept, fetch-decode-execute cycle, buses (address, data, control)	Describe the FDE cycle; explain the role of each bus
Harvard architecture	Separate instruction and data memory; comparison with Von Neumann	Compare both architectures; explain when Harvard is used
Registers	PC, MAR, MDR, CIR, ACC, status register	State the purpose of each register
Instruction set	Machine code format (opcode, operand), addressing modes (immediate, direct, indirect, indexed)	Interpret machine code instructions; explain addressing modes
Assembly language	Mnemonics, labels, directives; relationship to machine code	Read and write simple assembly programs
I/O and interrupts	Interrupt handling, interrupt service routines, vectored interrupts, priorities	Explain the interrupt handling process
Processors	CISC vs RISC, pipelining, parallel processing, multi-core	Compare CISC and RISC; explain pipelining benefits and hazards
GPUs	GPU structure, parallel processing, use in general-purpose computing (GPGPU)	Explain why GPUs are suited to parallel tasks

Revision Checklist: Section 4.7