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Machine Learning Question Bank – Weeks 1–6

Total questions: 92

Worksheet time: 46mins

Name
Class
Date
1.

Form A: Machine Learning is best described as:

a)

Writing all rules manually

b)

Teaching computers to learn from data

c)

Storing information only

d)

Hardware instructions

2.

Which of the following defines Machine Learning?

a)

A programming language

b)

A system that learns patterns from data

c)

A storage tool

d)

Manual rule writing

3.

Form C: Machine Learning focuses on:

a)

Arithmetic only

b)

Learning from examples and data

c)

Manually coding each case

d)

Removing human input

4.

The first step in a machine learning project is:

a)

Data Collection

b)

Deployment

c)

Prediction

d)

Testing

5.

Which step handles missing values and duplicates?

a)

Data Cleaning

b)

Deployment

c)

Evaluation

d)

Prediction

6.

Dividing data into training and test sets happens in:

(a)  

7.

Supervised learning requires:

a)

Labeled data

b)

Unlabeled data

c)

No data

d)

Only images

8.

Form B: Unsupervised learning uses:

a)

Labels provided

b)

No labels

c)

Rewards

d)

Targets

9.

Form C: Which type of learning predicts house prices?

a)

Supervised

b)

Unsupervised

c)

Reinforcement

d)

Random guessing

10.

Form A: Classification is used when:

a)

Predicting categories

b)

Predicting continuous values

c)

Grouping unlabeled data

d)

Finding associations

11.

Form B: Email spam filtering is an example of:

a)

Classification

b)

Regression

c)

Clustering

d)

Dimensionality reduction

12.

Form C: A model that sorts pictures into dog, cat, or bird is:

a)

A) Classification

b)

B) Regression

c)

C) Association

d)

D) Reinforcement

13.

Group 5 (1 mark) Form A: Regression is used when the target is:

a)

A) A continuous number

b)

B) A category

c)

C) A label

d)

D) A cluster

14.

Form B: Predicting house prices is an example of:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

15.

Form C: Polynomial regression is useful for:

a)

A) Non-linear trends

b)

B) Only text

c)

C) Random labels

d)

D) Binary outcomes

16.

Form A: Decision Trees are good because:

a)

Easy to visualize

b)

Require no data

c)

Work only with numbers

d)

Random guessing

17.

Form B: Support Vector Machines (SVMs) are effective in:

a)

High-dimensional spaces

b)

Small data only

c)

Text only

d)

Clustering tasks

18.

Form C: Neural Networks require:

a)

Large amounts of data

b)

No training

c)

Just one feature

d)

Labels only

19.

Group 7 (1 mark) Form A: Clustering groups data based on:

a)

similarity

b)

random selection

c)

alphabetical order

d)

size of data

20.

Which of the following is related to similarity?

a)

Similarity

b)

Labels

c)

Targets

d)

Predictions

21.

Form B: Association rule learning finds:

a)

Relationships between items

b)

Random values

c)

Predictions

d)

Labels

22.

Form C: Dimensionality reduction helps to:

a)

A) Simplify data

b)

B) Add features

c)

C) Increase complexity

d)

D) Add noise

23.

Which ML task is used in fraud detection?

a)

Classification

b)

Clustering

c)

Regression

d)

Dimensionality reduction

24.

Form B: Customer segmentation is achieved using:

a)

Clustering

b)

Regression

c)

Classification

d)

Association rules

25.

Form C: Grouping oil wells by depth and pressure is an example of:

a)

Clustering

b)

Regression

c)

Reinforcement

d)

Association

26.

Reinforcement learning improves through:

a)

Rewards

b)

Labeled data

c)

Clusters

27.

Self-driving cars use:

a)

Reinforcement Learning

b)

Regression

c)

Clustering

d)

Association

28.

Form C: AlphaGo is an example of:

a)

Reinforcement Learning

b)

Regression

c)

Classification

d)

Clustering

29.

Which step checks if a model is overfitting?

a)

A) Model Evaluation

b)

B) Data Cleaning

c)

C) Deployment

d)

D) Data Collection

30.

Form B: Which metric is common for classification?

a)

Accuracy

b)

RMSE

c)

Mean price

d)

Depth

31.

Form C: Which metric is common for regression?

a)

Mean Squared Error

b)

F1-Score

c)

ROC Curve

d)

Clusters

32.

Overfitting means:

a)

Model learns noise

b)

Model generalizes well

c)

Data is missing

d)

No training used

33.

Overfitting means:

a)

Model learns noise

b)

Model generalizes well

c)

Data is missing

d)

No training used

34.

Form A: Underfitting means:

a)

Model is too simple

b)

Model memorizes data

c)

Perfect accuracy

d)

No error

35.

Form B: Underfitting means:

a)

Model is too simple

b)

Model memorizes data

c)

Perfect accuracy

d)

No error

36.

Underfitting means:

a)

Model is too simple

b)

Model memorizes data

c)

Perfect accuracy

d)

No error

37.

Form A: Which tool is used for training ML models?

a)

A) Python

b)

B) Excel

c)

C) Paint

d)

D) Word

38.

What tool refers to used for training ML models?

a)

Python

b)

Excel

c)

Paint

39.

Which tool is used for training ML models?

a)

Python

b)

Excel

c)

Paint

d)

Word

40.

Group 14 (1 mark) Form A: IoT sensors provide:

a)

Real-time data

b)

Labels only

c)

Predictions

d)

No output

41.

Group 14 (1 mark) Form B: IoT sensors provide:

a)

Real-time data

b)

Labels only

c)

Predictions

d)

No output

42.

IoT sensors provide:

a)

Real-time data

b)

Labels only

c)

Predictions

d)

No output

43.

Group 15 (1 mark) Form A: Anomaly detection identifies:

a)

Outliers

b)

Labels

c)

Training sets

d)

Targets

44.

Form B: Anomaly detection identifies:

a)

Outliers

b)

Labels

c)

Training sets

d)

Targets

45.

Anomaly detection identifies:

a)

Outliers

b)

Labels

c)

Training sets

d)

Targets

46.

Which of the following is correct?

a)

Outliers

b)

Labels

c)

Training sets

d)

Targets

47.

Form A: PCA is used for:

a)

Dimensionality reduction

b)

Predictions

c)

Classification

d)

Clustering

48.

Form B: PCA refers to used for:

a)

Dimensionality reduction

b)

Predictions

c)

Classification

d)

Clustering

49.

PCA is used for:

a)

Dimensionality reduction

b)

Predictions

c)

Classification

d)

Clustering

50.

Market basket analysis uses:

a)

A) Association rules

b)

B) Regression

c)

C) Classification

d)

D) Reinforcement

51.

Form B: Market basket analysis refers to uses:

a)

A) Association rules

b)

B) Regression

c)

C) Classification

d)

D) Reinforcement

52.

Market basket analysis uses:

a)

A) Association rules

b)

B) Regression

c)

C) Classification

53.

Form A: Speech-to-text systems use:

a)

Supervised learning

b)

Clustering

c)

Reinforcement

d)

Association

54.

Form B: Speech-to-text systems use:

a)

Supervised learning

b)

Clustering

c)

Reinforcement

d)

Association

55.

Speech-to-text systems use:

a)

Supervised learning

b)

Clustering

c)

Reinforcement

d)

Association

56.

Predicting weather temperature is:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

57.

Form B: Predicting weather temperature refers to:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

58.

Predicting weather temperature is:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

59.

Grouping documents by topics is:

a)

Clustering

b)

Regression

c)

Reinforcement

d)

Labels

60.

Form B: Grouping documents by topics refers to:

a)

Clustering

b)

Regression

c)

Reinforcement

d)

Labels

61.

Grouping documents by topics is:

a)

Clustering

b)

Regression

c)

Reinforcement

d)

Labels

62.

Form A: Reward feedback is used in:

a)

Reinforcement

b)

Classification

c)

Regression

d)

Association

63.

Form B: Reward feedback refers to used in:

a)

Reinforcement

b)

Classification

c)

Regression

d)

Association

64.

Reward feedback is used in:

a)

Reinforcement

b)

Classification

c)

Regression

d)

Association

65.

Group 22 (1 mark) Form A: An example of supervised learning is:

a)

Predicting house prices using labeled data

b)

Clustering customers based on purchasing behavior

c)

Finding patterns in unlabeled text data

d)

Detecting anomalies without prior examples

66.

What refers to an example of supervrrefers toed learning?

a)

Predicting exam scores

b)

Grouping customers

c)

Market basket analysis

d)

PCA

67.

Which is an example of supervised learning?

a)

Predicting exam scores

b)

Grouping customers

c)

Market basket analysis

d)

PCA

68.

Which task reduces noise in images?

a)

Dimensionality reduction

b)

Clustering

c)

Classification

d)

Association

69.

What task reduces norefers toe in images?

a)

Dimensionality reduction

b)

Clustering

c)

Classification

d)

Association

70.

Which task reduces noise in images?

a)

Dimensionality reduction

b)

Clustering

c)

Classification

d)

Association

71.

In ML, features are:

a)

Input variables

b)

Predictions

c)

Labels

72.

Form B: In ML, features are:

a)

Input variables

b)

Predictions

c)

Labels

d)

Rewards

73.

In ML, features are:

a)

Input variables

b)

Predictions

c)

Labels

d)

Rewards

74.

In ML, the label is:

a)

The output

b)

The input

c)

The cluster

d)

The dimension

75.

In ML, the label refers to:

a)

The output

b)

The input

c)

The cluster

d)

The dimension

76.

In ML, the label is:

a)

The output

b)

The input

c)

The cluster

d)

The dimension

77.

Form A: Data cleaning involves:

a)

Removing errors

b)

Adding noise

c)

Training models

d)

Predictions

78.

Data cleaning involves:

a)

Removing errors

b)

Adding noise

c)

Training models

d)

Predictions

79.

Data cleaning involves:

a)

Removing errors

b)

Adding noise

c)

Training models

d)

Predictions

80.

Cross-validation helps to:

a)

Evaluate models

b)

Clean data

c)

Collect data

d)

Add labels

81.

Cross-validation helps to:

a)

Evaluate models

b)

Clean data

c)

Collect data

d)

Add labels

82.

Cross-validation helps to:

a)

Evaluate models

b)

Clean data

c)

Collect data

d)

Add labels

83.

Form A: Which is an oil and gas ML use case?

a)

A) Well classification

b)

B) Image painting

c)

C) Text formatting

d)

D) File storage

84.

What refers to an oil and gas ML use case?

a)

Well classification

b)

Image painting

c)

Text formatting

85.

Which is an oil and gas ML use case?

a)

Well classification

b)

Image painting

c)

Text formatting

d)

File storage

86.

Spam email detection is:

a)

Classification

b)

Regression

c)

Clustering

d)

Reinforcement

87.

Form B: Spam email detection refers to:

a)

Classification

b)

Regression

c)

Clustering

d)

Reinforcement

88.

Spam email detection is:

a)

Classification

b)

Regression

c)

Clustering

d)

Reinforcement

89.

Predicting stock prices is:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

90.

Form B: Predicting stock prices refers to:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

91.

Predicting stock prices is:

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement

92.

Which of the following is the correct answer?

a)

Regression

b)

Classification

c)

Clustering

d)

Reinforcement