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HSC Enterprise Computing exam 2026Exam: Thu 29 Oct · NESA timetable

Your HSC Enterprise Computing exam:

When and how long

  • Enterprise Computing9.50 am to 12.20 pm2 h 30 min in total, including reading time

NESA: the exam start time shown on your timetable is when reading time begins, and you must arrive well before it. Finishing times marked approximate are shown as approx.

Source: 2026 HSC written exam timetable (NESA), checked Wednesday 23 September 2026. Where a start time, reading time or duration isn't shown, the timetable doesn't publish it: check your personal timetable and the front of your paper.

Paper format

Higher School Certificate Examination - Enterprise Computing: 80 marks, 2 h 20 min writing time plus 10 minutes reading time.

  • Objective response20 marks
  • Short answer60 marks

NESA HSC exam specification for Enterprise Computing 11-12 (2022): an online exam of 2 hours 30 minutes including 10 minutes reading time, 80 marks, sat on a computer. About 20 marks of objective-response items (14 to 18 items, each worth 1 to 4 marks) and about 60 marks of short-answer items (16 to 18 items, at least three worth 4 to 8 marks). This desk mock uses 1-mark multiple choice for the objective items and, as the course has no bank of past questions yet, fills the short-answer section with our original dot-point practice questions. The time split per section is our suggestion.

From the official specification: source.

What the exam covers

We don't have past-paper frequency data for this exam, so here is the course, module by module. Make sure every module is covered.

Night-before and exam-morning checklists

The night before

  • Check your personalised timetable on Students Online: the start time shown is when reading time begins.[2]
  • Confirm your venue and the start time.[1]
  • Pack a clear bag: several black pens (no erasable ink), 2B pencils, sharpener, eraser and a ruler.[1]
  • Pack an approved calculator (check NESA's list) and a compass or protractor if the exam needs them.[1]
  • Fill a clear, label-free water bottle.[1]
  • A plain watch only if you want one (no smart or programmable watch); it goes on the desk.[1]
  • Stop revising around 7 to 8 pm, set two alarms and sleep.[1]

Exam morning

  • Eat a real breakfast.[1]
  • Arrive well before the start time to allow for seating and checks.[2]
  • Leave your phone and other electronic devices outside the exam room.[1]
  • Use the bathroom before you go in.[1]
  • In reading time, read and plan only: no writing, marking or annotating.[1]
  • You can't leave in the first hour or the last 15 minutes.[1]
  1. HSC exam day: what to actually expect
  2. NESA: HSC written exam timetable

Exam-week survival kit: The last 7 days · The night before and exam morning · What to bring, and what's banned · How to use reading time · If you're sick or something goes wrong · Handling exam-week stress.

Last-week revision

HSC Enterprise Computing cram sheet

Key formulas, definitions and facts copied from our Enterprise Computing syllabus pages. One page when printed.

Data Science

Why blockchain verifies data

Tampering with an old block changes its hash, breaking the link to every later block, and the altered copy disagrees with all the other copies. Blockchain makes records tamper-evident and gives a trusted shared history without a single central authority.

From: How blockchain manages and verifies data

Nominal: named categories with no order (payment method, suburb). Valid: counts, mode.

From: Quantitative vs qualitative data and the four levels of measurement

Relevance: does it answer this question, for this population and time?

From: Data sampling, active and passive collection, and data quality
Social, ethical or legal?
  • Legal: required by law (privacy law, copyright law).
  • Ethical: what is right, even if legal (not exploiting vulnerable users).
  • Social: effects on communities and society (loss of trust, discrimination, cultural harm).
    Many issues are all three. Say which aspect you are discussing.
From: Social, ethical and legal issues in using data

Data Visualisation

Simplifying understanding: people read shapes, colours and positions faster than rows of numbers.

From: Why we visualise data, software features and finding patterns

Roll-up: aggregate to a higher level (month to year).

From: How hardware and software changed data analytics, and OLAP

Accuracy: do the values and the visual impression match reality? A correct number can still be shown misleadingly.

From: Evaluating bias in data visualisations

Data backup

  • Regular automated backups (full, incremental or differential).
  • The 3-2-1 rule: three copies, two media, one off-site.
  • Version history to recover from mistakes or ransomware.
  • Regular restore testing.
From: Creating a data visualisation and keeping its data secure

Enterprise Project

Two questions
  • Verification: "Did we build the system right?" Does it meet the specification and work correctly (tests pass, calculations correct)?
  • Validation: "Did we build the right system?" Does it meet the client's and users' real needs in their environment?
From: Verifying, validating, evaluating and maintaining an enterprise system
Choosing an approach

Ask: How clear and stable are the requirements? How big and complex is the system? What skills, time and budget are available? How involved can users be? What are the risks if it fails?

From: Gathering requirements and choosing a development approach

Direct (cut-over): switch completely on a set date. Cheapest and fastest; highest risk.

From: Developing and testing an implementation plan

Intelligent Systems

Core ethical questions

Is it fair? Is it transparent and explainable? Who is accountable? Is privacy protected? Is it safe and reliable? Does it benefit the people it affects?

From: Disruptive effects, ethics and emerging AI in intelligent systems

Structured (automated decisions): routine, repetitive, clear rules and complete information. The system can decide automatically (reorder stock at the reorder point, calculate pay, approve a small refund).

From: Decision support systems and structured, semi-structured and unstructured decisions

Truth maintenance: records the justification for each belief so that when a fact changes, dependent conclusions are withdrawn or revised. Keeps knowledge consistent over time.

From: Expert systems, forward and backward chaining, and inference techniques
Weighing surveillance

Benefits: security, crime and fraud prevention, safety, efficiency and personalised services.
Concerns: privacy, consent, accuracy and bias (misidentification), function creep, data breaches, chilling effects on behaviour and freedom of expression.

From: Simulation, data modelling, automation and surveillance
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