WorksheetsAI and Algorithms Worksheet
Total questions: 100
Worksheet time: 50mins
Which of the following is NOT an AI application?
Voice assistant
Calculator
Face recognition system
Chatbot
A digital clock that shows time is an example of:
Artificial Intelligence
Machine learning
Non-AI program
Neural network
Which device works only by fixed rules without learning?
Self-driving car
Washing machine timer
Recommendation system
Smart assistant
Which system does not adapt to new data?
Thermometer
Spam filter
Speech recognition
Image classifier
A traffic light with fixed timing is:
AI system
Intelligent agent
Non-AI system
Learning system
An algorithm is best defined as:
A computer machine
A step-by-step procedure to solve a problem
A programming language
A data file
Which is an example of an algorithm?
Recipe for cooking rice
Mobile phone
Internet
Battery
Which feature is common to all algorithms?
They must use computers
They must be long
They have clear steps
They must use AI
What comes first in an algorithm?
Output
Input
Error
Algorithms are important because they:
Make computers heavy
Help solve problems logically
Increase electricity use
Replace humans
What comes next in the sequence: 1, 11, 21, ___ ?
12
1211
22
111
The Look-and-Say sequence works by:
Adding numbers
Subtracting values
Describing the previous number
Multiplying digits
What does “21” mean in Look-and-Say?
Two and one
Twenty-one
Two ones
One two
Which skill is mainly used in Look-and-Say?
Memory
Pattern recognition
Speed
Guessing
Look-and-Say sequences help learners develop:
Artistic skills
Logical thinking
Physical fitness
Drawing skills
The contrapositive of “If it rains, the ground is wet” is:
If it doesn’t rain, the ground isn’t wet
If the ground isn’t wet, it didn’t rain
If it rains, the ground isn’t wet
If the ground is wet, it rained
A statement and its contrapositive are:
Sometimes true
Always opposite
Logically equivalent
Unrelated
Logical equivalence means two statements:
Sound similar
Have the same truth value
Which word is commonly used in conditional statements?
And
But
If
Or
Logic is important in computing because it helps in:
Guessing
Decision making
Drawing
Typing
The Dartmouth Conference took place in:
1945
1956
1969
1980
The term “Artificial Intelligence” was first used at:
MIT
Harvard
Dartmouth Conference
Oxford
The Dartmouth Conference is important because it:
Built the first robot
Started AI as a field
Created the internet
Invented computers
AI history helps learners understand:
Past mistakes only
How ideas evolve
Phone usage
Social media
AI as a subject officially began in the:
1800s
1920s
1950s
2000s
A finite state machine is best described as:
A learning brain
A system with limited states
A neural network
A robot body
Traffic lights are examples of:
Neural networks
FSMs
Reinforcement learning
Big data
FSMs change state based on:
Emotions
Random chance
Inputs/events
Memory size
FSMs are mainly used to model:
Behavior
Images
Sound
Text
FSMs are best for systems that are:
Very complex
Continuous
Simple and rule-based
Unpredictable
Using someone’s artwork without permission is:
Fair use
Ethical
Copyright violation
Innovation
Ethics in AI refers to:
Speed of machines
Responsible use of technology
Computer size
Internet access
AI-generated art raises ethical concerns because:
It is slow
It may copy human work
It uses electricity
It needs data
Giving credit to original creators is called:
Hacking
Plagiarism
Attribution
Training
Ethical AI use encourages:
Stealing
Transparency and fairness
Secrecy
Bias in AI means:
Faster output
Unfair preference
More data
Accuracy
AI bias often comes from:
Hardware
Training data
Screen size
Power supply
Which is an example of bias?
Equal answers
Repeating stereotypes
Correct spelling
Fast response
Reducing bias requires:
Less data
Diverse and fair data
No rules
Smaller models
Why is AI bias dangerous?
It slows systems
It increases cost
It can cause unfair decisions
It improves accuracy
BFS stands for:
Best First Search
Breadth First Search
Binary Function Search
Basic File System
DFS stands for:
Depth First Search
Data File Search
Direct Fast Search
Dynamic Function System
BFS explores nodes:
Deep first
Randomly
Level by level
Backwards
DFS explores nodes by going:
(a)
Graph search is used in:
Maps and routes
Drawing
Printing
Typing
Data partitioning means:
Deleting data
Dividing data into parts
Encrypting data
Printing data
Partitioning helps to:
Slow processing
Improve performance
Lose data
Increase errors
Big data refers to data that is:
Small and simple
Too large to process easily
Always online
Text only
Partitioning is useful when data is:
Very small
Very large
Handwritten
Private
Data optimization aims to:
Waste memory
Improve efficiency
Reduce accuracy
Stop computation
A vector has:
Size only
Direction only
Magnitude and direction
Shape
Vector addition combines:
Letters
Directions and distances
Colours
Numbers only
Which is a real-life example of vector addition?
Walking east then north
Reading a book
Typing text
Cooking
Vectors are commonly used in:
Literature
Physics
History
Art
Coordinate systems help to:
Confuse location
Describe position
Remove direction
Reduce size
A classification threshold decides:
Data size
Decision boundary
Hardware type
File format
Thresholds help models decide between:
Letters
Classes
Colours
Sizes
Changing a threshold affects:
Accuracy and errors
Screen brightness
Battery life
Speed only
Thresholds are used in:
Classification models
Word processors
Drawing tools
Calculators
A low threshold may increase:
False positives
Memory
Cost
Storage
Memory is used to store:
Nodes visited
Electricity
Screens
BFS usually uses:
Less memory
More memory
No memory
External memory only
DFS uses memory mainly for:
Levels
Stack/recursion
Queue
Files
Memory helps avoid:
Speed
Revisiting nodes
Accuracy
Output
Efficient memory use improves:
Performance
Colour
Sound
Shape
Which guarantees the shortest path (unweighted graph)?
DFS
BFS
Random search
Greedy search
DFS may miss shortest paths because it:
Explores deep paths first
Uses queues
Is slow
Uses too much memory
BFS is preferred when:
Depth matters
Shortest path is needed
Memory is limited
Graph is small
DFS is useful when:
All paths must be explored
Shortest path is required
Graph is wide
Levels matter
Both BFS and DFS are used in:
Graph traversal
Cooking
Sequence: A, B, C, D (level by level) suggests:
DFS
BFS
Random search
Greedy search
72. Sequence: A, B, E, H, then backtracking suggests:
BFS
DFS
Sorting
Classification
BFS uses which data structure?
Stack
Queue
Tree
Array
DFS uses which structure?
Queue
Stack
Table
File
Node order helps identify:
Algorithm used
Colour
Memory size
Output type
Reinforcement learning learns by:
Labels
Rewards and punishment
Fixed rules
Guessing
A game-playing AI often uses:
Supervised learning
Reinforcement learning
Unsupervised learning
Sorting
Reinforcement learning involves an:
Agent and environment
Teacher only
Data file
Calculator
The goal of reinforcement learning is to:
Maximize reward
Minimize memory
Reduce speed
Increase errors
Real-life example includes:
Traffic light control
Word typing
Printing
Drawing
Supervised learning uses:
No data
Labeled data
Random data
Hidden data
Features are:
Outputs
Input characteristics
Errors
Labels
Classifying fruits by size and colour uses:
Features
Rewards
States
Nodes
Supervised learning is commonly used in:
Image classification
Random guessing
Drawing
Typing
Labels tell the model:
How fast to run
The correct answer
Memory size
Data type
Overfitting means the model:
Learns too little
Learns noise instead of patterns
Is too slow
Uses no data
An overfitted model performs well on:
New data
Training data only
All data
Overfitting reduces:
Training accuracy
Generalization
Memory use
Speed
One way to reduce overfitting is:
More training data
No testing
Ignoring errors
Removing features randomly
Overfitting is a problem because it:
Looks good but fails in real life
Is fast
Uses less memory
Is simple
A recursive sequence is defined using:
Random numbers
Previous terms
Letters
Shapes
Example: aₙ = aₙ₋₁ + 2 is:
Random
Recursive
Static
Linear only
Recursion is useful in:
Repeating problems
Drawing only
Writing
Painting
Recursive definitions need a:
Loop
Base case
Colour
File
Without a base case, recursion may:
Stop
Loop forever
Improve accuracy
Save memory
Image segmentation means:
Resizing images
Dividing an image into parts
Printing images
Deleting images
Segmentation helps computers:
Understand objects
Type faster
Save power
Play music
Medical scans use segmentation to:
Detect regions
Increase colour
Reduce size
Add text
Segmentation is part of:
Computer vision
Networking
Databases
Word processing
Object boundaries are important in segmentation because they:
Show edges of objects
Increase memory
Reduce accuracy
Slow systems
