A Level ICT: Data Processing and Information — Practice Questions

These are original questions written for Marlbridge, in the style and at the standard of the examination. They are not reproduced past-paper questions — examination boards hold copyright in their own papers. Use these alongside the official past papers available free from your board.

Related: Data Processing and Information revision notes

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Section A

1. Distinguish between data, information and knowledge, giving an example of each. [3]

2. State four factors that affect the quality of information. [4]

Section B

3. Explain the difference between validation and verification.

(a) Describe four validation checks and give a suitable field for each. [8] (b) Describe two methods of verification. [4] (c) Explain why validation cannot guarantee that data is correct. [2]

4. Explain the difference between a flat file and a relational database, and give three advantages of a relational database. [7]

5. Explain the terms primary key, foreign key and referential integrity. [6]

6. Explain four principles that data protection legislation typically places on organisations holding personal data. [8]

Section C

7. Distinguish between static and dynamic data, giving an example of each. [2]

8. A researcher can either run their own survey or use existing census data.

(a) State whether each is a direct or an indirect data source. [2] (b) Explain one advantage and one disadvantage of using the census data instead of running a new survey. [4]

9. Explain why payroll processing typically uses batch processing rather than real-time processing, and explain why an autopilot system could not use batch processing instead. [4]

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Answers

1. Data — raw facts and figures with no context, e.g. “42” [1]. Information — data given context and meaning, e.g. “42 students attended” [1]. Knowledge — information applied with understanding to make a decision, e.g. recognising that 42 is below average attendance and acting on it [1].

2. Any four: accuracy, relevance, completeness, timeliness (up to date), level of detail, presentation and clarity of format [1] [1] [1] [1].

3. (a) Any four, 2 marks each: Range check — the value must fall between set limits; suitable for age or a percentage mark [1] [1]. Format (picture) check — the data must match a pattern; suitable for a postcode or a date [1] [1]. Presence check — the field must not be left blank; suitable for a surname or student ID [1] [1]. Type check — the data must be of the correct data type; suitable for a numeric quantity field [1] [1]. Length check — a set number of characters; suitable for a phone number [1] [1]. Check digit — a calculated digit appended to a code; suitable for a barcode or ISBN [1] [1]. (b) Double entry — the data is entered twice, by the same or different operators, and the two versions are compared by the computer; any difference is flagged [1] [1]. Visual (proofreading) check — the operator compares the data on screen against the original source document [1] [1]. (c) Validation only checks that data is sensible and of the right form, not that it is true [1]; an age of 35 entered for a 34-year-old passes every check but is still wrong [1].

4. A flat file stores all data in a single table, so the same information is repeated on many records [1]. A relational database stores data in several linked tables, related by key fields [1]. Advantages: reduced data redundancy — each fact is stored once, saving storage [1]; improved data consistency — an update is made in one place, so records cannot disagree with one another [1]; improved data integrity and security, since access rights can be granted table by table [1]; greater flexibility — new queries and reports can combine tables without restructuring the data [1]; easier maintenance and expansion [1].

5. Primary key — a field (or combination) that uniquely identifies each record in a table; no two records may share it and it cannot be null [1] [1]. Foreign key — a field in one table that refers to the primary key of another table, creating the relationship between them [1] [1]. Referential integrity — the rule that a foreign key value must always match an existing primary key value in the related table [1], which prevents “orphan” records such as an order attached to a customer who does not exist [1].

6. Any four, 2 marks each: data must be processed lawfully and fairly, with the subject informed of how it will be used [1] [1]. It must be collected for specified, explicit purposes and not used for anything incompatible with them [1] [1]. It must be adequate, relevant and limited to what is necessary — organisations should not collect data “just in case” [1] [1]. It must be accurate and kept up to date, with inaccuracies corrected or erased [1] [1]. It must not be kept longer than necessary [1] [1]. It must be kept secure against unauthorised access, loss or damage, using encryption, access controls and backups [1] [1].

7. Static data does not change once recorded, e.g. a date of birth [1]. Dynamic data changes automatically as its source updates, e.g. a live share price or a sensor reading [1].

8. (a) The researcher’s own survey is a direct source [1]; the census data is an indirect source, since it was collected for a different original purpose [1]. (b) Any one advantage: the census data is cheap and immediately available, and often covers a very large sample [1] [1]. Any one disadvantage: it may be out of date, in the wrong format for the researcher’s needs, or carry an unknown bias, since it was not designed for this specific research question [1] [1].

9. Payroll involves a large volume of similar transactions with no urgency, so it can be collected and processed together in a scheduled batch, e.g. overnight when the system is otherwise idle [2]. An autopilot is a real-time control system, where the output (an adjustment to the aircraft’s controls) must feed back to influence the very next input immediately — a delay of even a few seconds while data is batched would be unsafe, since the system must respond continuously to changing conditions [2].

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Where marks are usually lost

• Confusing validation with verification.
• Giving a validation check without a suitable field.
• Saying a primary key “identifies the table” rather than each record.
• Listing data protection principles without explaining the obligation.