In the exam you will be given threee questions to choose from. In class we will look at Complexity and Tractability.
Candidates will receive a printed resource booklet to support their answers. Candidates will be required to respond in short and /or extended answers (800–1500 words in total) to questions relating to their choice of ONE of the following areas of computer science:
• Complexity and tractability
• Big data
• Formal languages.
For Complexity and tractability, questions may cover: polynomial and non-polynomial time complexity, Big O notations O(1), O(log n), O(n) , O(n^k), O(2^n), O(n!), and best-case, worst-case, and average-case time complexity, complexity classes (P, NP, NP-complete), solving complex problems (approximation algorithms /heuristics), algorithm design and optimisation, optimal solutions (Travelling salesman /knapsack, etc.).
Teachers are encouraged to help their students to develop answering techniques to ensure that they are able to respond clearly and concisely within the total recommended word limit of 1500 words.
Teachers are strongly encouraged to prepare students to be able to apply their understanding of computer science to unfamiliar contexts.
Teachers should prepare students to identify and articulate instances where overlap with various areas of computer science occurs, e.g. with artificial intelligence.
Further information about digital external assessment can be found on the NZQA website.
The topic for the exams change evey year. Complexity and tractability was examined in 2024 and 2025. The NZQA site has the 2025 past paper and exemplars for A, M, E for 2025 and 2024.
The assessement report for last year's exam show what the markers are looking for:
Candidates who were awarded Achievement commonly:
identified and described key concepts within the chosen topic, using simple explanations
named relevant algorithms or techniques and describe their general purpose
provided a basic real-world example and identified a simple benefit or limitation
demonstrated descriptive understanding with limited technical depth.
Candidates who were awarded Achievement with Merit commonly:
explained how processes worked, using appropriate computer science terminology
described key stages or components of algorithms or systems
applied concepts to real-world contexts with discussion of effectiveness and limitations
demonstrated sound understanding but limited justification or evaluation.
Candidates who were awarded Achievement with Excellence commonly:
demonstrated in-depth algorithmic understanding, clearly explaining how and why processes operate
justified conclusions using technical reasoning, calculations, or comparisons
integrated low-level mechanisms with system-level applications and implications
showed synthesis, evaluation, and insight beyond isolated explanations.
Candidates who were awarded Not Achieved commonly:
provided incomplete or incorrect explanations of core concepts
failed to correctly identify or explain required algorithms or mechanisms
gave irrelevant or insufficient evidence across criteria
responded in vague or general terms without demonstrating computer science understanding.