Font size
WorksheetsData Science HSC Exam(9/12/25)
Total questions: 50
Worksheet time: 1hrs 5mins
What does data bias in an enterprise system most likely cause?
What does data bias in an enterprise system most likely cause?
Unfair or incorrect outcomes
Which of the following is a legal implication of poor data protection in enterprise computing?
Which of the following best describes structured data?
Why do enterprises use cloud-based tools in data science?
Which of the following best describes the purpose of data cleaning in enterprise data science projects?
To increase the size of the dataset regardless of quality.
To remove or correct inaccurate, incomplete, or irrelevant data to improve data quality.
To encrypt data for security purposes.
To intentionally introduce errors for testing.
What is a key benefit of using data visualisation tools in enterprise computing?
They make data less accessible to users.
They help identify patterns and trends for better decision-making.
They increase the complexity of data analysis.
They reduce the need for data security.
Which of the following is an ethical consideration when using data science in enterprises?
Ignoring data privacy regulations.
Ensuring data is used fairly and does not discriminate against individuals or groups.
Maximising data storage costs.
Focusing only on technical performance.
What is the primary benefit of using data visualization in enterprise data science?
To make data less accessible to stakeholders.
To help communicate insights and patterns in data more effectively.
To replace the need for data cleaning.
To increase the complexity of data analysis.
Which of the following best describes the role of data governance in enterprise data science?
It focuses solely on increasing data storage capacity.
It ensures data quality, security, and compliance with regulations.
It eliminates the need for data privacy policies.
It is only concerned with data visualization techniques.
What is a potential risk of not anonymizing sensitive enterprise data?
Increased risk of data breaches and privacy violations.
Enhanced data visualization capabilities.
Improved data accuracy.
Faster data processing speeds.
Which practice helps ensure fairness in enterprise data science models?
Using diverse and representative datasets during model training.
Focusing only on maximizing model accuracy.
Limiting model evaluation to technical metrics.
Ignoring feedback from affected stakeholders.
Which of the following is a common challenge when integrating data from multiple sources in enterprise data science?
Reducing the amount of available data.
Eliminating the need for data security measures.
Focusing only on unstructured data.
Ensuring consistent data formats and quality across sources.
What is the main purpose of implementing data privacy policies in enterprise environments?
To comply with legal regulations and protect sensitive information.
To increase data redundancy.
To make data accessible to everyone without restrictions.
To slow down data processing speeds.
Which of the following best describes unstructured data in enterprise data science?
Data that lacks a specific format, such as emails, images, or videos.
Data organized in tables with predefined columns.
Data stored only in spreadsheets.
Data that is always numerical and quantitative.
Which of the following is a primary reason for using machine learning algorithms in enterprise data science?
To automate data analysis and uncover patterns in large datasets.
To eliminate the use of data visualization tools.
To manually process all enterprise data.
To reduce the need for data security.
What is the main advantage of using big data technologies in enterprise computing?
They reduce the need for data cleaning.
They allow for the storage and analysis of vast amounts of data efficiently.
They make data less accessible to users.
They increase the risk of data loss.
Which of the following best describes the role of data ethics in enterprise data science?
Ensuring that data is collected, stored, and used responsibly and fairly.
Focusing only on maximizing profits.
Ignoring the impact of data-driven decisions on individuals.
Allowing unrestricted access to all enterprise data.
What is a potential consequence of using biased training data in enterprise machine learning models?
Improved data security.
Unintended discrimination against certain groups.
Increased transparency in decision-making.
Models that produce fair and accurate predictions.
Which of the following best describes semi-structured data in enterprise environments?
Data that is only used for graphical representation.
Data that is completely unorganized and lacks any format.
Data organized strictly in tables with fixed columns.
Data that contains both structured elements and unstructured content, such as JSON or XML files.
Structured data (a) in a (b) format, like rows (c) in (d) table. An example is customer records in a CRM (Customer Relationship Management) system.
Data security (a) access, (b) information, and (c) with laws, (d) data breaches.
(a) can lead to (b) results and unfair (c) , which may harm business operations and (d) trust.
Data (a) helps users understand trends (b) in data (c) charts and graphs, making it easier to (d) .
One social implication of collecting large amounts of (a) that it may lead to (b) for individuals if data (c) or shared (d)
Define 'structured data' and give one example used in enterprise systems.
Explain why data security is important in enterprise computing systems.
How can bias in data collection affect enterprise decision-making?
Describe one social implication of enterprise systems collecting large amounts of personal data.
Describe one example of an innovative technology that has improved enterprise computing
Explain, by means of an example, the importance of securely collecting, storing, and manipulating data in enterprise systems.
Explain how social, ethical, and legal issues affect the design of enterprise systems using data science.
Evaluate how the use of innovative technologies, such as AI and machine learning, has improved enterprise computing systems.
Which of the following is a primary goal of data governance in enterprise data science?
To eliminate the need for data cleaning.
To focus only on increasing data storage capacity.
To make all data publicly available without restrictions.
To ensure data is managed securely, accurately, and in compliance with policies.
What is a common method used to protect sensitive data in enterprise environments?
Encrypting data both at rest and in transit.
Ignoring data access controls.
Sharing sensitive data with all employees.
Storing data only in physical files.
Which of the following best describes the impact of poor data quality on enterprise decision-making?
It leads to more accurate and reliable insights.
It can result in incorrect analyses and misguided business strategies.
It improves the speed of data processing.
It has no effect on business outcomes.
Which of the following is a key challenge in maintaining data quality in enterprise data science?
Ignoring data validation processes.
Focusing only on structured data sources.
Reducing the amount of data collected.
Ensuring data is consistently accurate and up-to-date across all systems.
What is the primary purpose of implementing access controls in enterprise data systems?
To ensure only authorized personnel can view or modify sensitive data.
To speed up data processing.
To allow unrestricted access to all users.
To increase data redundancy.
Organize these options into the right SQL categories
The fields of a table (Surname, Suburb, DOB, Email)
The tables( Customers, Accounts, Product)
Suburb="Liverpool" and ItemPrice > 50
Price= "ascending"
CompanyName, City, SaleID, SaleDate
Customers, Sales
FirstName, LastName
ProductName, Quantity
OrderID, CustomerID
SaleDate > '2023-01-01'
SaleDate = "descending"
FirstName = "ascending"
P – Point
Enterprises must keep (a) when it (b) especially (c)
E – Evidence
For example, when collecting data, enterprises (a) so hackers cannot read personal information. The data is then stored in a (b) two-factor authentication (2FA), (c) meaning only approved staff can see or change the data. When manipulating data for Data Science, (d) to keep the information accurate.
E – Explain
These safety steps protect the data (a) or lost. (b) protects data during collection, secure storage stops (c) and controlled manipulation ensures the data stays correct (d)
L – Link
Therefore, (a) of data is (b) computing system using Data Science, as it (c) and supports (d)
Organize these options into the right levels of measurements categories
Categories or labels without any order.
Data with a meaningful order, but the differences between the values are not consistent.
Ordered data with meaningful intervals between values, but no true zero point.
Similar to interval data but with a true zero point, meaning "nothing" is possible.
Gender (Male, Female)
Colours (Red, Blue, Green)
Rankings (First, Second, Third)
Survey ratings (Poor, Fair, Good, Excellent)
Temp. in Celsius or Fahrenheit (e.g., 10°C, 20°C)
Dates (e.g., 1990, 2000, 2010)
Weight (e.g., 0 kg, 50 kg,
Height (e.g., 0 cm, 150 cm, 180 cm)
Income (e.g., $0, $10,000)
No numerical meaning.
You can only count or categorize them.
You can order the data, but you can't measure the exact difference between the values.
Zero does not mean "nothing" (e.g., 0°C doesn’t mean no temperature).
True zero means that zero represents the absence of the quantity.
Collect and analyse data:
Enterprises use data science to (a) preferences, (b) This helps them (c) what customers (d)
Predict customer needs:
By using (a) enterprises can (b) or (c) may (d)
Improve personalisation:
(a) helps create (b) — like showing (c) that match a (d)
Example – Amazon:
Amazon uses (a) to (b) on customers’ (c) and purchases. This (d) easier, and more (e)
AI helps enterprises make (a) by finding patterns in big data.
AI can (b) such as customer behaviour, sales, or risks.
Example: A supermarket uses AI to (c) which products sell fast. AI then helps the store (d) don’t go empty.
Improvement: AI makes systems faster, smarter, and more accurate than manual decision-making.
Cloud services give enterprises more storage, more (a)
Data science tools ( (b) ) run easily in the cloud.
Example: A retail business stores customer data in the cloud and uses cloud analytics to see (c)
Improvement: Cloud computing helps enterprises scale, share data, and work from (d)
