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WorksheetsArtificial Intelligence and Algorithms Worksheet
Total questions: 100
Worksheet time: 50mins
Which of the following is NOT an example of Artificial Intelligence?
A calculator adding numbers
A chatbot answering questions
A phone unlocking using face recognition
A recommender system suggesting videos
A traffic light that changes strictly by a fixed timer is best described as:
Intelligent system
Machine learning system
Non-AI automated system
Expert system
Which device works using predefined instructions only?
Chess-playing computer
Voice assistant
Washing machine with fixed cycles
Self-driving car
Why is a basic digital clock not considered AI?
It uses electricity
It cannot learn or adapt
It displays numbers
It uses a screen
Which scenario shows automation but not intelligence?
Robot vacuum mapping a house
ATM dispensing cash after PIN entry
Phone predicting next word
AI detecting diseases
An algorithm is best defined as:
A computer machine
A programming language
A step-by-step procedure to solve a problem
An intelligent robot
Which daily activity follows an algorithm?
Guessing lottery numbers
Cooking using a recipe
Sleeping
Watching clouds
Why must algorithms be finite?
To look simple
To avoid electricity
To eventually stop
To use memory
Which of the following is NOT a property of an algorithm?
A. Clear steps
B. Ambiguity
C. Finite steps
D. Input and output
Writing instructions to tie shoelaces is an example of:
Random process
Algorithm design
Machine learning
Artificial intelligence
The sequence 1, 11, 21, 1211 is generated by:
Adding numbers
Multiplying digits
Describing previous term
Subtracting values
What comes after 1211 in the look-and-say sequence?
1112
111221
1221
1311
Pattern recognition mainly involves:
Guessing
Detecting relationships
Memorizing facts
Drawing pictures
Why is look-and-say important in AI?
It uses electricity
It trains pattern analysis
It draws graphs
It stores images
Which skill is MOST applied in recognizing sequences?
Observation
Typing
Speaking
Printing
The contrapositive of “If it rains, the ground is wet” is:
If the ground is wet, it rained
If it did not rain, the ground is dry
If the ground is not wet, it did not rain
Rain causes floods
A statement and its contrapositive are:
Always false
Logically equivalent
Unrelated
Opposites
Logical reasoning in AI helps machines to:
Feel emotions
Make correct decisions
Sleep
Display colors
Which area relies heavily on logical equivalence?
Music
Ethics
Computer programming
Painting
Logic statements in AI are mainly used for:
Decoration
Decision making
Entertainment
Printing
The Dartmouth Conference (1956) is important because:
A. Computers were invented
B. Internet was discovered
C. AI was formally proposed
D. Robots were built
Who is considered one of the founders of AI?
Charles Darwin
John McCarthy
Isaac Newton
Albert Einstein
The term “Artificial Intelligence” was first used in:
1940
1956
1970
2000
Early AI focused mainly on:
Learning from big data
Logical reasoning and problem solving
Social media
Image filters
Dartmouth Conference is an example of:
AI failure
AI ethics debate
Historical milestone
Machine learning model
A Finite State Machine (FSM) is best described as:
A learning algorithm
A model with unlimited memory
A system that moves between defined states
A random process
Which real-life system can be modeled using an FSM?
Human emotions
Traffic light system
Weather patterns
Stock market prices
In an FSM, a state transition occurs when:
Memory is full
Input is received
Data is deleted
Output is hidden
FSMs are commonly used in:
Video games for character behavior
Poetry writing
Painting
Music composition
Why are FSMs suitable for simple AI agents?
They require large datasets
They are unpredictable
They are easy to design and control
They think like humans
A major ethical issue with AI-generated art is:
High electricity use
Ownership of creative work
File size
Screen resolution
If an AI is trained on artists’ work without permission, this raises concerns about:
Speed
Copyright infringement
Hardware failure
Color quality
Who is most affected by unethical AI art generation?
Programmers only
Artists and content creators
Internet providers
Device manufacturers
An ethical use of AI art tools involves:
Copying exact artworks
Giving credit and following licenses
Hiding data sources
Avoiding human input
Why is copyright important in AI development?
It increases file formats
It protects creators’ rights
It reduces creativity
It slows innovation
Bias in AI systems usually comes from:
Screen brightness
Training data
Computer fans
Internet speed
If a language model gives unfair responses to a group, this is an example of:
Optimization
Bias
Overfitting
Automation
Historical data can cause bias because:
It is always correct
It reflects past inequalities
It is too small
It is encrypted
One way to reduce bias in AI is to:
Use only old data
Remove all data
Use diverse and balanced datasets
Increase screen size
Why is bias a serious issue in AI used for exams or hiring?
It saves time
It affects fairness and equality
It improves accuracy
It reduces costs
Breadth-First Search (BFS) explores nodes:
Randomly
Depth first
Level by level
From last to first
Depth-First Search (DFS) explores nodes:
One branch deeply before backtracking
Level by level
Using probabilities
Using reinforcement
BFS is preferred when:
Memory is very limited
Finding the shortest path is required
Graph is infinite
Speed is irrelevant
DFS is often used when:
Graph is shallow
Memory usage must be minimal
Shortest path is needed
Levels matter
Which structure is commonly used in BFS?
Stack
Queue
Tree only
Table
Data partitioning means:
Deleting data
Splitting data into smaller parts
Encrypting files
Printing data
Why is data partitioning important in big data systems?
It reduces electricity
It improves processing speed
It increases file size
It hides errors
Which system benefits MOST from data partitioning?
Single-user calculator
Distributed database
Paper records
Notebook
Partitioning allows big data systems to:
Process data in parallel
Avoid storage
Ignore errors
Reduce users
A real-life example of data partitioning is:
Sorting books by subject in shelves
Mixing all files together
Writing everything on one page
Deleting duplicates
Vector addition involves:
Removing directions
Combining magnitude and direction
Counting numbers only
Drawing curves
If a robot moves 3 m east then 4 m north, the final position depends on:
Speed only
Vector addition
Subtraction
Rotation
Vectors are commonly used in AI for:
Text typing
Image and motion representation
Printing
Sound volume only
Which field uses vectors heavily?
Computer vision
Poetry
History
Grammar
Vector addition helps AI systems to:
A. Understand spatial movement
B. Store passwords
C. Increase bias
D. Reduce memory
A classification threshold determines:
File size
Decision boundary
Screen color
Hardware speed
Changing a threshold can affect:
Accuracy and errors
Keyboard layout
Data type
Internet access
In face recognition, a high threshold means:
Easier matching
Stricter matching
Random results
No decisions
Thresholds help balance:
Speed and memory
False positives and false negatives
Cost and profit
Input and output
Why are thresholds important in medical AI systems?
They save power
They affect diagnosis decisions
They reduce data
They simplify coding
BFS generally uses more memory because it:
Stores entire levels
Avoids queues
Deletes nodes
Uses recursion
DFS uses less memory because it:
Explores one path at a time
Stores all nodes
Uses databases
Requires maps
Memory in search algorithms stores:
Music files
Visited nodes
Colors
Images only
Limited memory systems often prefer:
BFS
DFS
Random search
Learning algorithms
Why is memory management important in AI search?
To reduce bias
To avoid system crashes
To increase art quality
To improve graphics
BFS guarantees the shortest path when:
Graph is weighted
All edges have equal cost
Memory is low
Graph is deep
DFS does NOT guarantee shortest path because it:
Uses queues
Explores deeply first
Stores all nodes
Uses levels
GPS navigation systems rely more on BFS-like strategies because:
They ignore distance
They need shortest routes
They avoid maps
They reduce memory
DFS is more suitable for:
Maze exploration
Shortest route finding
Level order traversal
Traffic routing
Which statement is TRUE?
A. BFS always uses less memory
B. DFS always finds shortest path
C. BFS finds shortest path in unweighted graphs
D. DFS ignores structure
Visiting nodes level by level indicates:
DFS
BFS
Random walk
Reinforcement learning
A node order A → B → D → H → E suggests:
BFS
DFS
Sorting
Partitioning
Backtracking is a key feature of:
BFS
DFS
Vector addition
Classification
Which structure helps DFS backtracking?
Queue
Stack
Table
Array only
Recognizing traversal order helps in:
Debugging algorithms
Drawing art
Playing music
File compression
Reinforcement learning is based on:
Labeled data
Rewards and penalties
Fixed rules
Random guessing
A game-playing AI that improves by winning or losing uses:
Supervised learning
Reinforcement learning
Unsupervised learning
Automation
Reinforcement learning agents learn through:
Trial and error
Memorization
Copying data
Printing results
Which is a real-life example of reinforcement learning?
Spam filtering
Robot learning to walk
Sorting numbers
Typing text
Why is reinforcement learning suitable for robotics?
A. Robots need labels
B. Robots interact with environments
C. Robots store art
D. Robots avoid sensors
Supervised learning requires:
No data
Labeled examples
Random rewards
No features
In email spam detection, a feature could be:
Screen size
Frequency of certain words
Keyboard type
File format
Feature-based classification works by:
Guessing outcomes
Using input characteristics
Drawing images
Sorting shelves
Which task commonly uses supervised learning?
Image labeling
Clustering
Exploration
Compression
Accurate labels are important because they:
Increase bias
Guide correct learning
Reduce speed
Remove features
Overfitting occurs when a model:
Learns general patterns
Performs well only on training data
Ignores data
Uses few features
An overfitted model usually performs:
Better on new data
Worse on new data
Equally everywhere
Randomly
Overfitting is often caused by:
Too little training
Too complex models
Balanced datasets
Clear features
One way to reduce overfitting is to:
Add noise
Use more training data
Remove testing
Increase bias
Why is overfitting dangerous in real systems?
It saves memory
It gives unreliable predictions
It speeds training
It simplifies models
A recursive sequence is defined using
A. Random numbers
B. Previous terms
C. External data
D. Graphs only
The Fibonacci sequence is an example of:
Iteration only
Recursion
Classification
Sorting
Recursion in computing involves:
Looping forever
A function calling itself
Storing images
Drawing vectors
Recursive thinking helps in:
Problem decomposition
Painting
Singing
Printing
Which condition is necessary in recursion?
Infinite calls
Base case
Random stop
High memory
Image segmentation means:
Compressing images
Dividing images into meaningful regions
Coloring pictures
Blurring images
Segmentation helps AI to:
Detect objects
Increase file size
Reduce accuracy
Avoid vision
Medical imaging uses segmentation to:
Decorate scans
Identify organs or tumors
Reduce resolution
Hide details
Self-driving cars use segmentation to:
Play music
Identify roads and pedestrians
Print maps
Store videos
Computer vision segmentation is important because:
Images are large
Machines must understand visual scenes
Cameras are expensive
Colors are attractive
