OxfordAQA A Level Computer Science: Arrays and Lists — Revision Notes

Condensed for the final weeks. For the full explanation, use the Arrays and Lists study guide.

Static vs dynamic

	Static	Dynamic
Size	Fixed at creation	Grows/shrinks during execution
Example	Traditional array	Python’s built-in list
Advantage	Fast, predictable access	Adapts flexibly to varying data
Disadvantage	Wastes memory if oversized, fails if undersized	Extra overhead when resizing

Python has no built-in array type — students typically use lists (dynamic) even for conceptually static-array questions, unless using the array module (no strings, no multi-dimensional) or numpy.

Worked example: choosing a structure

Storing daily temperature readings for a year (365/366 known values) vs an unknown, growing list of game high scores.

Temperatures: STATIC (array) -- exact count known and fixed in
              advance, so a static structure avoids resizing
              overhead and gives fast, predictable indexed access
High scores:  DYNAMIC (list) -- count unknown and growing, so a
              structure that expands as needed is appropriate

Justifying the choice against whether the data size is known and fixed, or unknown and growing is what this sub-topic assesses — more than naming “array” or “list” correctly.

Two-dimensional structures

A 2D array/list of lists is indexed by two coordinates (typically row, column) — used for tabular or grid-based data (a spreadsheet, a game board). Exam questions will not require more than two dimensions.

Worked example: describing a 2D structure

A program needs to store the seating arrangement of a small cinema with 6 rows and 10 seats per row, where each seat is either booked or free.

Structure:  a two-dimensional array/list of lists, 6 rows x 10
            columns
Indexing:   seating[row][seat] -- e.g. seating[2][5] accesses row
            index 2, seat index 5 (using zero-based indexing, this
            is the 3rd row, 6th seat)
Content:    each element holds a boolean or simple value representing
            booked/free status for that specific seat

Being explicit about how the two coordinates map onto the real-world scenario (row = physical row, column = seat number within the row) is what distinguishes a strong answer from one that only states “use a 2D array” without describing the indexing scheme.

Why the static/dynamic distinction frames the rest of 3.2

This sub-topic’s static-versus-dynamic framing recurs across every later data structure: the queues and stacks covered elsewhere in 3.2 Fundamental Data Structures, and, at A-level only, the advanced structures of 3.10 (graphs, trees, hash tables, priority queues and dictionaries) – each later structure is itself built as either a static or dynamic implementation, and being able to say which, and why that choice suits the structure’s typical use case, is a skill introduced here and reused throughout the rest of the topic. Treat the advantages and disadvantages named in this sub-topic (predictable access vs resizing overhead) as a template applied again to every subsequent data structure, not content specific to arrays and lists alone.

Key terms

Data structure — an organised way of storing and accessing data suited to a task. Static data structure — fixed size at creation, unchanged during execution. Dynamic data structure — can grow/shrink while the program runs. Array — a static, fixed-size, indexed collection of same-type elements. List (Python) — a dynamic, indexed collection.

Programming-exam flexibility

In programming exam questions, it is acceptable to use either lists or arrays to solve a problem unless the question specifically states which must be used – so don’t assume a question phrased around “array” requires the array/numpy module in Python specifically if lists would solve the problem equally well and no data type is mandated.

Common mistakes

• Describing arrays and lists as interchangeable without acknowledging the static/dynamic distinction.
• Choosing a data structure without justifying the choice against whether the size is known/fixed or unknown/changing.
• Assuming exam questions require more than two dimensions.
• Forgetting Python’s list behaves dynamically even when the underlying concept tested is a static array.

Quick self-test

1. Give one advantage and one disadvantage of a static data structure.
2. Why does Python typically use lists even for conceptually “array” problems?
3. A scenario has a known, fixed number of elements. Which structure type is more appropriate?
4. What are the two coordinates typically used to index a 2D array?
5. What is the maximum number of dimensions required in this specification’s exam questions?
6. Describe how you would index a specific seat in a 6-row, 10-seats-per-row cinema layout stored as a 2D structure.

Answers: 1. Advantage: fast, predictable access since memory layout doesn’t change. Disadvantage: wastes memory if oversized, or fails if undersized. 2. Because Python has no built-in fixed-size array type; its built-in list behaves dynamically, so it is used even when a question is conceptually about a static array. 3. A static structure (array), since the fixed size avoids resizing overhead and gives fast indexed access. 4. Row and column. 5. Two dimensions. 6. seating[row][seat], e.g. seating[2][5] for row index 2, seat index 5, using zero-based indexing so this is physically the 3rd row and 6th seat.

Vocabulary precision matters

Keeping the words “array” and “list” mapped correctly to “static” and “dynamic” respectively – rather than treating all indexed collections as one interchangeable category – is the specific vocabulary precision this sub-topic rewards in written exam answers, even when your practical programming work uses Python lists throughout.

Related resources

• Arrays and Lists study guide
• Arrays and Lists practice questions

Official syllabus

OxfordAQA International AS and A-level Computer Science (9645) specification, Version 1.1 — oxfordaqa.com/9645.