OxfordAQA A Level Computer Science: Arrays and Lists — Practice Questions

These are original questions written for Marlbridge, in the style and at the standard of the examination. They are not reproduced past-paper questions — examination boards hold copyright in their own papers. Use these alongside the official past papers available free from your board.

Related: Arrays and Lists study guide · Arrays and Lists revision notes

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Section A

1. Define a static data structure and a dynamic data structure, giving one example of each. [4]

2. State the maximum number of dimensions required in exam questions for this specification. [1]

Section B

3. State one advantage and one disadvantage of a static data structure, and one advantage and one disadvantage of a dynamic data structure. [4]

4. A programmer needs to store the number of goals scored by each of 20 players in a five-a-side league across a fixed, already-scheduled season. Justify whether a static or dynamic data structure is more appropriate. [3]

5. A programmer needs to store an unknown, growing list of chat messages sent during a live stream. Justify whether a static or dynamic data structure is more appropriate. [3]

6. Explain why Python’s built-in list is described as dynamic, even when it is being used to represent something conceptually static, such as an array. [4]

7. A program stores the test scores of a class of 25 students across 4 tests, so that a specific student’s specific test score can be looked up directly. Describe an appropriate data structure for this, and explain how it would be indexed. [5]

8. Evaluate the claim that a programmer should always choose a dynamic data structure over a static one, because it is more flexible and can never run out of space. [8]

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Answers

1. A static data structure has a fixed size set at creation, which cannot change during a program’s execution [1] — for example, a traditional array [1]. A dynamic data structure can grow or shrink in size while a program is running, allocating or releasing memory as needed [1] — for example, Python’s built-in list [1].

2. Two dimensions [1].

3. Static — advantage: offers faster, more predictable access, since its memory layout does not change [1]. Static — disadvantage: wastes memory if oversized, or fails if undersized, since its size cannot adapt once created [1]. Dynamic — advantage: adapts flexibly to varying amounts of data [1]. Dynamic — disadvantage: that flexibility brings extra overhead when the structure needs to resize [1].

4. A static structure (array) is more appropriate [1]. The number of players (20) is known in advance and fixed for the whole season, so a static structure avoids the overhead of resizing and gives fast, predictable indexed access to any player’s goal count [1] [1].

5. A dynamic structure (list) is more appropriate [1]. The number of chat messages is not known in advance and grows continuously as the stream runs, so a structure that can expand as needed is more appropriate than a fixed-size structure that would need repeated resizing or risk running out of space [1] [1].

6. Python does not contain a built-in fixed-size array type in the way languages such as C# or Java do [1]. Its built-in list behaves dynamically — it can grow or shrink during execution [1], so a programmer typically uses a list even when the underlying concept being modelled is a static array, since the list’s size and contents are never actually fixed at creation even if the programmer only ever treats it as such [1] — unless they specifically import the array module or numpy to get a genuinely fixed-size, typed structure instead [1]. (The vocabulary distinction — array meaning static, list meaning dynamic — still matters for a written answer, even though the practical Python tool used may be the same dynamic list either way.)

7. An appropriate structure is a two-dimensional array or list of lists, indexed by two coordinates — here, student and test [1] [1]. For example, scores[student][test] would access a specific student’s score in a specific test, using zero-based indexing so scores[2][1] accesses the 3rd student’s 2nd test score [1] [1]. With zero-based indexing across 25 students and 4 tests, the student index would run from 0 to 24 and the test index from 0 to 3 [1]. (Being explicit about which coordinate maps to which real-world quantity — student vs test — is what distinguishes a complete answer from one that only states “use a 2D array”.)

8. Case for always choosing dynamic: a dynamic structure adapts flexibly to varying amounts of data and avoids the risk of a fixed-size structure being undersized and failing when demand is unknown or grows unexpectedly [1] [1]. Case against: the claim overstates the trade-off. A dynamic structure still carries extra overhead when it needs to resize [1], and “can never run out of space” is not strictly true either, since a dynamic structure is ultimately bounded by the memory actually available to the program. Where the number of elements is genuinely known and fixed in advance — such as 365 daily temperature readings across a calendar year — a static structure gives faster, more predictable access without any resizing overhead at all [1] [1], so choosing dynamic in that case trades away a real performance advantage for a flexibility the problem does not need. Judgement: the right choice depends on whether the data size is known and fixed, or unknown and changing [1], not on a blanket preference for one category. A dynamic structure is the safer default only when the data genuinely cannot be bounded in advance; when the count is known, a static structure remains the more appropriate and more efficient choice, so the claim that dynamic should always be preferred is not supported [1] [1].

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Where marks are usually lost

• Describing arrays and lists as interchangeable terms without acknowledging the static/dynamic distinction.
• Choosing a data structure for a scenario without justifying it against whether the size is known/fixed or unknown/changing.
• Assuming a question requires more than two dimensions.
• Stating a 2D structure’s indexing without specifying which coordinate represents which real-world quantity.
• Treating “dynamic is always better” as true without acknowledging the resizing overhead and the efficiency case for static structures.