Enterprise Computing 2025 HSC exam pack

Students are encouraged to use the familiarisation questions and past HSC examinations to assist in their preparation in using the functionality and features of the online exam.

Students should:

• use the stimulus material provided to support their response
• read questions carefully to construct relevant responses.

Question 14

In better responses, students were able to:

• demonstrate an understanding of quantitative and qualitative data that can be extracted from the advertising campaigns to make business decisions, for example, likes, shares, emojis, comments and trend patterns
• describe how quantitative or qualitative data could be used in data analysis to derive specific business insight, for example, engagement metrics and audience sentiment
• identify key features of quantitative and qualitative data, for example, the number of shares as structured data and comments as unstructured data.

Areas for students to improve include:

• describing how data that relates to the advertising campaign can be extracted and interpreted, rather than just describing what is collected
• demonstrating understanding of the various data collection tools that could be used to extract quantified data for analysis, such as a data mining
• demonstrating understanding of structured and unstructured data that can be extracted and used in various contexts.

Question 15 (b)

In better responses, students were able to:

• demonstrate understanding of the four structured query language (SQL) keywords, join tables to create a relationship, and recognise the criteria elements identified in the scenario
• use a correct join clause to link related fields in tables
• recognise the SQL syntax from the data tables provided in the stimulus to display accurate results, for example, the date uses ‘-’ to separate days, months and years, rather than ‘/’.

Areas for students to improve include: 

• demonstrating an understanding of SQL structure.

Question 16 (a)

In better responses, students were able to:

• use relevant examples relating to the scenario to show the use of the hardware component, for example, using a light sensor to capture the data relating to the brightness of a screen
• demonstrate understanding of the sensor requirements for the given scenario and articulate their use, for example, cameras, light sensor and pressure sensor.

Areas for students to improve include:

• demonstrating understanding of the range of sensors and their function in an intelligent system
• demonstrating understanding of how different hardware components contribute to the data collection of a system
• demonstrating understanding of how the collected sensor data is processed and used by the expert system to support decision-making in an intelligent system.

Question 16 (b)

In better responses, students were able to:

• demonstrate understanding of the relationship between data collection, processing and improved operational outcomes for the manufacturing process. For example, recognising that data from a light sensor measuring screen brightness can be processed by the expert system to automatically accept or reject a TV unit
• articulate specific and relevant Industry 4.0 concepts that would benefit a manufacturing process, for example, automation, inference, real-time data and optimisation
• explain that an expert system forms part of an intelligent automation layer which allows machines to make decisions based on real-time data.

Areas for students to improve include:

• demonstrating understanding of the significance of Industry 4.0 and its relevance in enhancing workflow and modern work practices, for example, automation
• demonstrating understanding of how expert systems use rules and logic to support decision-making in a range of modern work practices.

Question 17 (a)

In better responses, students were able to:

• demonstrate understanding that a data warehouse is a centralised repository for the coffee chain and the benefits of data warehousing and data mining
• provide examples of how data mining identifies trends and patterns which can then be used to by the coffee shop chain stores. For example, trends in what flavours sell in certain locations or seasons can be used to make future business decisions associated with this specific data, for example, which flavours to keep.

Areas for students to improve include: 

• demonstrating understanding of a range of data warehouse features and providing clear links to how these will help enterprises make decisions based on current data patterns and trends
• demonstrating understanding of the purpose of data warehousing and data mining in relation to a range of enterprises and the data analytics that can be extracted.

Question 17 (b)

In better responses, students were able to:

• identify hardware advancements and explain the benefits they have for the coffee shop in data processing, for example, an increase in RAM would allow for multiple data processing options in a faster time
• use relevant examples of hardware such as SSD, RAM, CPU, GPU and Network cables and relate how the advancements in the hardware benefit the coffee shop data processing and business decision-making. 

Areas for students to improve include: 

• explaining the difference between hardware and software enhancements in relation to business analytics.

Question 18

In better responses, students were able to:

• create a spreadsheet with test data using formulas to ensure all calculations work accurately with appropriately formatted cells, for example, change the total cost column format from number to $0.00
• construct a drop-down list of car size: small, medium, large, which allowed for the selection of only relevant data, or an IF statement where the inappropriate input returns an error message or nil value
• test the rental day discount formula to determine if a discount was correctly applied, where it is represented as a ‘YES’ or ‘NO’ in the discount field or displayed as a decimal.

Areas for students to improve include:

• demonstrating understanding of a range of formulas, for example, SUM, LOOKUP formulas and the syntax structure of formulas
• demonstrating understanding of IF statements and the parameter for the ‘ELSE’ component
• demonstrating understanding of the mathematics related to formulas and when a decimal is required.

Question 19

In better responses, students were able to:

• design a clear and well-organised interface that showed how users would enter registration details such as name, email and date of birth
• outline appropriate interface input control functions, for example, a text box for name, a calendar picker for date of birth, and radio buttons to select either ‘student’ or ‘teacher’
• demonstrate understanding that effective labelling involves more than naming fields by also identifying the purpose of buttons, selection controls and other interface features so the function of each element is clearly identified.

Areas for students to improve include:

• demonstrating understanding of the role and purpose of different design tools. For example, interface design drawing shows input data from a user, whereas a flowchart represents the flow of data in a system
• ensuring all required elements for an interface to work are included, for example, a ‘submit’ button
• labelling each feature so the purpose of every field or control is identifiable
• demonstrating understanding of the role of form controls, for example, radio buttons allow for only one option to be selected, whereas checkboxes allow for multiple options to be selected.

Question 20

In better responses, students were able to:

• demonstrate understanding of a range of spreadsheet features to assist in analysing data visually, for example, use of conditional formatting to highlight information in a particular data range
• recognise the use of pivot tables to filter data sets for effective data analysis
• recognise how different graph formats clarify visual understanding of relevant information for the graph described, for example, a line graph is an appropriate graph to use to show the variables of fish population over time.

Areas for students to improve include:

• demonstrating understanding of the various types of data that can be presented in a spreadsheet and the tools used for presenting data visually for ease of understanding, for example, using pivot tables and slicers
• demonstrating understanding of the role of spreadsheets and the tools that can be used when analysing data, for example, graphs and filters.

Question 21

In better responses, students were able to:

• demonstrate understanding of the difference between a level 0 and level 1 Dataflow Diagram (DFD), including multiple processes and a data store in DFD
• identify the external entities, processes and data store elements in the scenario accurately and use the correct DFD symbols
• identify the main processes, including ticket generation, seat allocation and payment system, and include relevant text information on data flow lines.

Areas for students to improve include:

• demonstrating understanding of the difference between data flow and flowchart symbols and their purpose by constructing a suitable data flow diagram (DFD) for this system
• ensuring all data flow lines have arrows and are labelled showing the data moving throughout the system.

Question 22 (a)

In better responses, students were able to:

• outline how the tool can be used to track the management of a project, for example, a Gantt chart to schedule and track a team's allocated activities.

Areas for students to improve include:

• demonstrating understanding of the difference between project management tools and software applications. For example, a tool has a specific role, such as a calendar to schedule and track events, whereas software is a program that is used to carry out a variety of tasks. 

Question 22 (b)

In better responses, students were able to:

• identify relevant data security methods used to protect cloud-based data and show how they maintain the security of the data.

Areas for students to improve include:

• providing details about specific cybersecurity methods that can be used by enterprises, for example, login procedures, multi factor authentication (MFA), biometrics, CAPTCHA, firewalls or backup procedures.

Question 23 (a)

In better responses, students were able to:

• describe the ways that specific software advancements contribute to dynamic data dashboards through advancements that assist users’ visual understanding of presented data to support informed business decisions. For example, online analytic processing (OLAP)
• include information detailing how real-time data is processed quickly and updates the data dashboard so that users have current and relevant data to make informed business decisions.

Areas for students to improve include:

• providing accurate information about common business software such as spreadsheets that have the integration of OLAP tools to allow non-technical users to process and analyse data in a meaningful way
• identifying a range of key features of specific data analytic software and improvements over time, for example, PowerBI and Tableu.

Question 23 (b)

In better responses, students were able to:

• interpret the data dashboard in the stimulus and provide specific examples of how the management team can quickly understand the complex employee productivity data due to the simplified presentation method
• explain how a range of spreadsheet tools are used to visually interpret complex datasets in relation to the scenario, for example, line graphs represent trends over a period
• identify that users are likely to have a positive experience with the data dashboard as their cognitive load is reduced due to the simplified presentation.

Areas for students to improve include:

• interpreting specific data visualisation tools in a data dashboard to understand the purpose of data being presented
• recognising a range of benefits to the user of data being presented in a simplified manner such as a data dashboard, to assist with analysis and informed business decision making.

Question 24

In better responses, students were able to:

• explain how specific outsourcing methods, such as hiring offshore developers, contracting freelancers for specialist tasks, or engaging remote service providers, enhance productivity, lower expenses, and support long-term enterprise expansion
• explain relevant advantages and disadvantages unique to both work models. For example, freelance workers can benefit the enterprise in the long term because workers are temporarily hired over a short period and have limited employment entitlements, whereas offshore development can have further financial incentives for an enterprise, though there may be issues with product quality based on different locational design standards.

Areas for students to improve include:

• addressing offshore development and freelance work as two separate options for enterprises
• explaining how a range of outsourcing methods can support the growth of modern businesses
• explaining modern working models available through advancements in technology, for example, video conferencing to connect with international business remotely.

Question 25

In better responses, students were able to:

• explain the difference between structured, semi-structured and unstructured decision support systems (DSS) and the function of intelligent systems within a DSS
• distinguish clearly between the 3 DSS (structured, semi-structured, and unstructured) by explaining that they are designed to handle decisions of increasing complexity
• distinguish between the scenarios in the stimulus and accurately identify relevant examples aligned to structured, semi-structured and unstructured DSS. For example, the transport demand forecasting scenario is only relevant to semi-structured and unstructured support systems.

Areas for students to improve include:

• providing a range of scenarios that relate to structured, semi-structured and unstructured DSS, rather than just intelligent systems
• applying an understanding of structured, semi-structured and unstructured decisions when referring to stimulus examples, such as workforce planning for semi-structured decision making
• explaining the difference between intelligent systems and decision support systems and how these can work in conjunction with each other.