Machine Learning Exam Online with Certification

Validate your machine learning skills with our online exam! Gain insights, identify improvement areas, and earn a certification—all from home. Showcase your expertise and boost your career!

1 Hour

Mid - Level

500+ Students Certified

1000 Rs - Special Offer

1 Hr

Mid - Level

500+ Certified

1000 Rs
Special Offer

/50

Machine Learning Exam

1 / 50

1) In K-Means, K represents:

A) Iterations

B) Classes

C) Features

D) Clusters

2 / 50

2) K-Means is used for:

A) Prediction

B) Classification

C) Regression

D) Clustering

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3) PCA is used for:

A) Regression

B) Classification

C) Feature selection

D) Dimensionality reduction

4 / 50

4) Which is NOT clustering algorithm?

A) Hierarchical

B) Decision Tree

C) K-Means

D) DBSCAN

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5) Bias-Variance tradeoff refers to:

A) Hardware

B) Data size

C) Model complexity

D) Feature scaling

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6) Which library is used for ML in Python?

A) Pandas

B) Matplotlib

C) Scikit-learn

D) NumPy

7 / 50

7) Dataset is divided into:

A) Classes and clusters

B) Input and output

C) Train and test

D) Rows and columns

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8) Logistic Regression is mainly used for:

A) Dimensionality reduction

B) Binary classification

C) Clustering

D) Continuous output

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9) Confusion matrix is used to evaluate:

A) Clustering

B) Classification

C) Dimensionality

D) Regression

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10) The future of ML is:

A) Finished

B) Limited

C) Declining

D) Growing

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11) Normalization range is usually:

A) 0 to 1

B) -1 to 1

C) 1 to 10

D) Any value

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12) Linear Regression is used for:

A) Classification

B) Prediction

C) Clustering

D) Association

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13) Which distance is commonly used in K-Means?

A) Jaccard

B) Manhattan

C) Euclidean

D) Cosine

14 / 50

14) Standardization uses:

A) Mean & Std Dev

B) Mode

C) Min & Max

D) Median

15 / 50

15) Increasing K in KNN will:

A) Reduce data

B) Increase dimensions

C) Decrease noise sensitivity

D) Increase overfitting

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16) KNN stands for:

A) Known Neural Network

B) K Nearest Neighbors

C) Key Neuron Number

D) Kernel Node Network

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17) Machine Learning is a subset of:

A) Database Management

B) Artificial Intelligence

C) Computer Networks

D) Data Entry

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18) Training data is used to:

A) Test the model

B) Clean data

C) Build the model

D) Visualize data

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19) Overfitting means:

A) Model memorizes training data

B) Model has low accuracy

C) Model performs poorly on training data

D) Model performs well on new data

20 / 50

20) Unsupervised learning uses:

A) Test data

B) Output data

C) Labeled data

D) Unlabeled data

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21) ML models improve performance by:

A) Guessing

B) Learning from experience

C) Repeating rules

D) Manual updates

22 / 50

22) Which language is most commonly used for ML?

A) HTML

B) C

C) Python

D) PHP

23 / 50

23) Loss function measures:

A) Error

B) Features

C) Accuracy

D) Clusters

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24) Which ML algorithm is used in recommendation systems?

A) Clustering

B) Collaborative Filtering

C) Linear Regression

D) PCA

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25) Which is a classification algorithm?

A) Linear Regression

B) Decision Tree

C) K-Means

D) PCA

26 / 50

26) Association Rule Mining finds:

A) Relationships

B) Predictions

C) Errors

D) Clusters

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27) Underfitting means:

A) Model too complex

B) Perfect fit

C) High accuracy

D) Model too simple

28 / 50

28) ML is used in healthcare for:

A) Image analysis

B) Disease prediction

C) All of the above

D) Diagnosis support

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29) Feature means:

A) Error value

B) Prediction

C) Output variable

D) Input variable

30 / 50

30) Which algorithm is supervised?

A) Linear Regression

B) Apriori

C) PCA

D) K-Means

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31) PCA reduces:

A) Rows

B) Columns

C) Noise

D) Labels

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32) Train-test split ratio commonly used is:

A) 90–10 only

B) 60–40

C) 50–50

D) 70–30 or 80–20

33 / 50

33) Gradient Descent is used for:

A) Optimization

B) Scaling

C) Classification

D) Clustering

34 / 50

34) Cross-validation helps in:

A) Visualization

B) Data entry

C) Overfitting reduction

D) Data cleaning

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35) Feature scaling improves:

A) None

B) Accuracy

C) Both A and B

D) Model speed

36 / 50

36) Support Vector Machine uses:

A) Centroid

B) Distance

C) Probability

D) Hyperplane

37 / 50

37) In Decision Tree, splitting is based on:

A) Entropy / Gini

B) Mean value

C) Distance

D) Variance only

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38) In supervised learning, data is:

A) Random

B) Unstructured

C) Labeled

D) Unlabeled

39 / 50

39) Big data + ML helps in:

A) Better decisions

B) Prediction

C) All of the above

D) Automation

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40) Which of the following is NOT a type of Machine Learning?

A) Reinforcement Learning

B) Descriptive Learning

C) Unsupervised Learning

D) Supervised Learning

41 / 50

41) Silhouette score measures:

A) Cluster quality

B) Loss

C) Variance

D) Accuracy

42 / 50

42) Elbow method is used to find:

A) K value

B) Accuracy

C) Entropy

D) Loss

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43) Hierarchical clustering creates:

A) Tree (Dendrogram)

B) Matrix

C) Graph

D) Table

44 / 50

44) Spam detection is an example of:

A) Classification

B) Association

C) Regression

D) Clustering

45 / 50

45) Learning rate controls:

A) Data size

B) Accuracy

C) Speed of training

D) Output class

46 / 50

46) Reinforcement learning is based on:

A) Distance

B) Labels

C) Clusters

D) Rewards & penalties

47 / 50

47) Apriori algorithm is used for:

A) Regression

B) Classification

C) Clustering

D) Association rules

48 / 50

48) Which algorithm uses probability?

A) PCA

B) Naive Bayes

C) K-Means

D) KNN

49 / 50

49) Which metric is used for classification?

A) Variance

B) RMSE

C) MSE

D) Accuracy

50 / 50

50) The main goal of Machine Learning is to:

A) Write programs manually

B) Store data

C) Learn patterns from data

D) Design hardware

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Benefits of Enrolling in Machine Learning Exam

➤ Recognition for Machine Learning skills.
➤ Validated expertise in Machine Learning.
➤ Industry recognized certification.
➤ Professional growth opportunities.
➤ Boost your resume.

Target Audience

➤ Students and beginners looking to enhance their Machine Learning skills.
➤ Professionals seeking to validate their Machine Learning expertise for career advancement.
➤ Developers wanting to demonstrate their proficiency in Machine Learning for job opportunities.
➤ Freelancers who need to showcase their Machine Learning capabilities to potential clients.
➤ Anyone interested in improving their Machine Learning knowledge for personal or professional growth.

Course Objectives

➤ Can understand the basics concepts and principles
➤ Can identify areas for improvement in your Machine Learning skills
➤ Earn a recognized assessment of your Machine Learning expertise
➤ Can identify and fix common errors
➤ Earn a completion certificate