Y12 EC Unit 2 Week 3 PreQuiz

Which of the following is NOT a key consideration when assessing data integrity in data visualisation?

• Ownership

• Source

• Validation

• Data redundancy

Why is data validation important in the development of a data visualisation?

• It ensures that data is formatted correctly.

• It identifies and removes ownership conflicts.

• It guarantees that the source data is from the cloud.

• It checks for potential errors and inaccuracies in the data.

What does data ownership in data visualisation primarily involve?

• Knowing who created the visualisation.

• Understanding who controls access to the data.

• Ensuring that data visualisation tools are licensed.

• Determining the type of chart used in the visualisation.

Which of the following is a risk to data integrity in a data visualisation project?

• Using multiple types of charts.

• Failing to validate the data source.

• Employing open-source visualisation tools.

• Incorporating redundant backups.

Enterprise data warehousing affects data visualisation by:

• Improving the scope of visible information.

• Supporting analysis of historical trends and patterns.

• Reducing data storage requirements.

• Eliminating the need for data refinement.

What is a primary benefit of using historical data trends in data visualisation?

• They reduce the complexity of current data.

• They provide context for understanding current data.

• They eliminate the need for data optimisation.

• They allow for real-time analysis.

Which process is involved in data refinement in data visualisation?

• Adding metadata to raw data.

• Filtering and cleaning data for accuracy.

• Compressing data to save storage space.

• Designing dashboards with fewer visuals.

How does enterprise data warehousing optimise data for visualisation?

• By reducing redundancy in stored data.

• By ensuring all data is presented in a single graph.

• By combining historical and current data for deeper analysis.

• By limiting the scope of analysis.

Big data affects the design of data visualisations by:

• Restricting the scope of visible information.

• Providing insights at a greater depth.

• Limiting the types of graphs that can be used.

• Reducing the number of data points available.

Which of the following is a challenge of visualising big data?

• Deciding on the best chart type.

• Managing the scope and depth of insights.

• Selecting colours for visual elements.

• Ensuring graphs are simple.