Level:
Correct: 0
Incorrect: 0
Marks: 0/0
Exam Practice
Subject:
Topics:
Cambridge IGCSE Computer Science (Paper 1) Smart practice A mixed round aimed at your weak spots
Unit 1
Data representation
Number systems
Binary, denary and hexadecimal conversions
0%
Uses of hexadecimal
0%
Binary addition and overflow
0%
Logical binary shifts
0%
Two's complement
0%
Text, sound and images
Character sets: ASCII and Unicode
0%
Representing sound
0%
Representing images
0%
Data storage and compression
Data storage units and file size calculations
0%
Data compression
0%
Unit 2
Data transmission
Types and methods of data transmission
Packets and packet switching
0%
Methods of data transmission
0%
The USB interface
0%
Methods of error detection
Parity, checksum and echo check
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Check digits and ARQ
0%
Encryption
Encryption
0%
Unit 3
Hardware
Computer architecture
The CPU, Von Neumann architecture and the FDE cycle
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Cores, cache and clock speed
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Instruction sets and embedded systems
0%
Input and output devices
Input devices
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Output devices
0%
Sensors
0%
Data storage
Primary storage: RAM and ROM
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Secondary storage: magnetic, optical and solid-state
0%
Virtual memory and cloud storage
0%
Network hardware
Network hardware: NIC, MAC and IP addresses, routers
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Unit 4
Software
Types of software and interrupts
System software, application software and operating systems
0%
Firmware and interrupts
0%
Programming languages, translators and IDEs
High-level languages, low-level languages and assembly language
0%
Compilers and interpreters
0%
Integrated development environments (IDEs)
0%
Unit 5
The internet and its uses
The internet and the world wide web
The internet, the world wide web, URLs and HTTP(S)
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Web browsers, locating web pages and cookies
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Digital currency
Digital currency and blockchain
0%
Cyber security
Cyber security threats
0%
Keeping data safe: security solutions
0%
Unit 6
Automated and emerging technologies
Automated systems
Automated systems
0%
Robotics
Robotics
0%
Artificial intelligence
Artificial intelligence
0%
Avg
score:
–
Topics
done:
0/39

Artificial intelligence

Practise 26 exam-style questions on Artificial intelligence for Cambridge IGCSE Computer Science (Paper 1). Write your answer as you would in the exam and get it marked in seconds against the mark scheme, point by point, with an AI tutor on hand if you get stuck.

  • 26 questions
  • 1–6 marks each
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20 questions, marked as you go

Sample questions with model answers

Here are 4 of the questions in this topic, each with a model answer, an explanation of why it earns the marks and the mark scheme an examiner would use.

Question 1

1 mark

Give one area, other than medicine, where expert systems are used.

Model answer

Diagnosing faults in car engines.

Explanation

Expert systems are used wherever specialist knowledge can be captured as facts and rules, such as fault diagnosis, prospecting for minerals and making financial decisions.

Mark scheme
  1. 1 mark

    Any one from: car / engine fault diagnosis; diagnosing computer faults; mineral or oil prospecting; financial decisions (e.g. approving loans or mortgages, investments); tax advice; plant or animal identification; playing chess; careers advice

    accept any other suitable area

Question 2

2 marks

State the difference between the knowledge base and the rule base of an expert system.

Model answer

The knowledge base stores the facts about the subject, whereas the rule base stores the rules, such as IF ... THEN statements, that are used with those facts to reach conclusions.

Explanation

Facts and rules are kept separately. For example, in a medical system a fact might be that a disease causes a rash, and a rule might be IF fever AND rash THEN suspect measles. The inference engine uses both.

Mark scheme
  1. 1 mark

    The knowledge base stores facts (about the specialist area)

  2. 1 mark

    The rule base stores the rules (e.g. IF ... THEN) for using / linking the facts to reach conclusions

Question 3

3 marks

Describe how a new expert system is set up.

Model answer

First, information is gathered from human experts in the subject, for example by interviewing them. This information is used to create the knowledge base of facts and the rule base of IF ... THEN rules. The interface is then designed, and the system is tested using problems whose correct answers the experts already know.

Explanation

An expert system is only as good as the knowledge put into it, so building one starts with collecting expertise from people. It then needs its four components to be created and must be tested before it can be trusted.

Mark scheme
  1. 1 mark

    Information is gathered from human experts (e.g. by interviews or questionnaires)

    max 3

  2. 1 mark

    The knowledge base is created / filled with facts

  3. 1 mark

    The rule base is created / IF ... THEN rules are written

  4. 1 mark

    The inference engine is designed / set up to use the rules and facts

  5. 1 mark

    The interface (questions and output screens) is designed

  6. 1 mark

    The system is tested, e.g. against problems whose answers the experts already know

Question 4

4 marks

Compare expert systems and machine learning.

Model answer

Both are types of AI that use data and rules to make decisions. However, an expert system's facts and rules are entered by people, based on knowledge from human experts, and it does not change unless people update it. A machine learning program adapts its own processes and data automatically, so it improves as it receives more data. An expert system can explain the rules it followed to reach a conclusion, whereas the decisions of a machine learning program can be harder to explain.

Explanation

The central difference is who changes the system: people update an expert system, while a machine learning program adapts itself. A comparison needs both sides of each difference; describing only one of the two systems earns little credit.

Mark scheme
  1. 1 mark

    Similarity: both are types of AI / both use data and rules to reason and make decisions

    max 1 for a similarity; max 4

  2. 1 mark

    An expert system's knowledge and rules are entered by people (from human experts), whereas a machine learning program adapts its own processes / data

  3. 1 mark

    An expert system does not learn by itself and must be updated by people, whereas machine learning improves automatically with new data / experience

  4. 1 mark

    An expert system is limited to one specialist area of knowledge, whereas machine learning can adapt to new situations / data

  5. 1 mark

    An expert system can explain how it reached its conclusion (rules followed), whereas a machine learning program's decisions can be hard to explain

  6. 1 mark

    Machine learning usually needs a large amount of data to learn from, whereas an expert system needs knowledge from human experts

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