2.02 Data Suitability for Processing Quiz

What is the first step in ensuring data is suitable for processing?

• Analysing the data

• Collecting the data

• Cleaning the data

• Visualising the data

Answer explanation

Cleaning the data is the first step to ensure it is suitable for processing. This involves removing inaccuracies and inconsistencies, which is essential before any analysis or visualization can take place.

Which of the following is a common method for handling missing data?

• Ignoring the data

• Deleting the entire dataset

• Filling in missing values with the mean

• Doubling the data

Answer explanation

Filling in missing values with the mean is a common method for handling missing data, as it allows for maintaining dataset size and can provide a reasonable estimate for the missing values.

Why is it important to remove duplicate data entries?

• To increase the size of the dataset

• To ensure data accuracy and integrity

• To make the data more colourful

• To confuse the data processor

Answer explanation

Removing duplicate data entries is crucial to ensure data accuracy and integrity. Duplicates can lead to misleading results and errors in analysis, making it essential to maintain a clean and reliable dataset.

What is data normalisation?

• Making all data values zero

• Converting data into a standard format

• Removing all data outliers

• Doubling the data values

Answer explanation

Data normalization refers to converting data into a standard format, which helps in reducing redundancy and improving data integrity. This process ensures that data is consistent and comparable across different datasets.

Which of the following is a benefit of data standardisation?

• It makes data processing slower

• It increases data redundancy

• It allows for easier comparison of data

• It makes data less reliable

Answer explanation

Data standardisation allows for easier comparison of data by ensuring that it is formatted consistently. This consistency helps in analyzing and interpreting data accurately, making it a key benefit.

What is the purpose of data validation?

• To make data processing more complex

• To ensure data is accurate and meets quality standards

• To increase the amount of data

• To make data processing more expensive

Answer explanation

The purpose of data validation is to ensure data is accurate and meets quality standards. This process helps maintain data integrity, preventing errors and inconsistencies that could affect analysis and decision-making.

Which of the following is an example of a data validation technique?

• Ignoring data errors

• Using data encryption

• Checking data against a set of rules

• Doubling the data entries

Answer explanation

Checking data against a set of rules is a data validation technique that ensures data accuracy and integrity by verifying it meets predefined criteria, making it the correct choice.

What is the role of data transformation in data processing?

• To make data less readable

• To convert data into a suitable format for analysis

• To increase data errors

• To make data processing impossible

Answer explanation

Data transformation is essential in data processing as it converts data into a suitable format for analysis, ensuring that it can be effectively utilized for insights and decision-making.

Which of the following is a common data transformation technique?

• Data encryption

• Data duplication

• Data scaling

• Data deletion

Answer explanation

Data scaling is a common data transformation technique that adjusts the range of data values, making it easier to analyze and model. In contrast, encryption, duplication, and deletion serve different purposes.

Why is it important to ensure data is suitable for processing before analysis?

• To make the analysis more difficult

• To ensure accurate and reliable results

• To increase the cost of data processing

• To make data processing slower

Answer explanation

Ensuring data is suitable for processing is crucial to achieve accurate and reliable results. Poor quality data can lead to misleading conclusions, making the analysis ineffective.