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

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1) In K-Means, K represents:

A) Features

B) Clusters

C) Iterations

D) Classes

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2) In supervised learning, data is:

A) Unstructured

B) Unlabeled

C) Labeled

D) Random

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3) Train-test split ratio commonly used is:

A) 50–50

B) 60–40

C) 70–30 or 80–20

D) 90–10 only

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4) Which library is used for ML in Python?

A) Pandas

B) Matplotlib

C) Scikit-learn

D) NumPy

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5) Silhouette score measures:

A) Cluster quality

B) Variance

C) Accuracy

D) Loss

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6) Loss function measures:

A) Error

B) Clusters

C) Features

D) Accuracy

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7) Overfitting means:

A) Model performs poorly on training data

B) Model has low accuracy

C) Model memorizes training data

D) Model performs well on new data

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8) Which algorithm uses probability?

A) Naive Bayes

B) PCA

C) K-Means

D) KNN

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9) Unsupervised learning uses:

A) Labeled data

B) Test data

C) Output data

D) Unlabeled data

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10) Which is NOT clustering algorithm?

A) DBSCAN

B) Hierarchical

C) K-Means

D) Decision Tree

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11) Normalization range is usually:

A) Any value

B) 0 to 1

C) -1 to 1

D) 1 to 10

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12) PCA reduces:

A) Rows

B) Columns

C) Labels

D) Noise

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13) Which language is most commonly used for ML?

A) HTML

B) Python

C) PHP

D) C

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

A) Supervised Learning

B) Reinforcement Learning

C) Unsupervised Learning

D) Descriptive Learning

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15) The future of ML is:

A) Finished

B) Declining

C) Limited

D) Growing

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16) Which algorithm is supervised?

A) PCA

B) Apriori

C) Linear Regression

D) K-Means

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17) Apriori algorithm is used for:

A) Regression

B) Classification

C) Association rules

D) Clustering

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18) Spam detection is an example of:

A) Classification

B) Association

C) Clustering

D) Regression

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19) Bias-Variance tradeoff refers to:

A) Feature scaling

B) Data size

C) Model complexity

D) Hardware

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20) KNN stands for:

A) Known Neural Network

B) Kernel Node Network

C) Key Neuron Number

D) K Nearest Neighbors

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21) Linear Regression is used for:

A) Clustering

B) Prediction

C) Classification

D) Association

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22) Gradient Descent is used for:

A) Optimization

B) Scaling

C) Clustering

D) Classification

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23) Which metric is used for classification?

A) MSE

B) RMSE

C) Variance

D) Accuracy

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24) In Decision Tree, splitting is based on:

A) Variance only

B) Mean value

C) Entropy / Gini

D) Distance

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25) Dataset is divided into:

A) Input and output

B) Rows and columns

C) Classes and clusters

D) Train and test

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26) ML models improve performance by:

A) Guessing

B) Repeating rules

C) Manual updates

D) Learning from experience

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27) Standardization uses:

A) Mean & Std Dev

B) Mode

C) Min & Max

D) Median

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28) Feature means:

A) Output variable

B) Input variable

C) Prediction

D) Error value

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29) Elbow method is used to find:

A) K value

B) Entropy

C) Accuracy

D) Loss

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30) Which is a classification algorithm?

A) Linear Regression

B) PCA

C) Decision Tree

D) K-Means

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31) Feature scaling improves:

A) Model speed

B) None

C) Accuracy

D) Both A and B

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32) Reinforcement learning is based on:

A) Labels

B) Rewards & penalties

C) Clusters

D) Distance

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33) ML is used in healthcare for:

A) All of the above

B) Diagnosis support

C) Disease prediction

D) Image analysis

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34) Training data is used to:

A) Visualize data

B) Clean data

C) Build the model

D) Test the model

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35) Confusion matrix is used to evaluate:

A) Clustering

B) Classification

C) Dimensionality

D) Regression

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36) Underfitting means:

A) High accuracy

B) Model too complex

C) Model too simple

D) Perfect fit

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37) K-Means is used for:

A) Clustering

B) Regression

C) Classification

D) Prediction

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38) Learning rate controls:

A) Data size

B) Output class

C) Speed of training

D) Accuracy

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39) Which ML algorithm is used in recommendation systems?

A) Clustering

B) Collaborative Filtering

C) PCA

D) Linear Regression

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40) Increasing K in KNN will:

A) Increase overfitting

B) Reduce data

C) Increase dimensions

D) Decrease noise sensitivity

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41) Support Vector Machine uses:

A) Probability

B) Hyperplane

C) Distance

D) Centroid

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42) Hierarchical clustering creates:

A) Tree (Dendrogram)

B) Graph

C) Table

D) Matrix

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43) Big data + ML helps in:

A) Better decisions

B) All of the above

C) Automation

D) Prediction

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44) Machine Learning is a subset of:

A) Computer Networks

B) Database Management

C) Artificial Intelligence

D) Data Entry

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45) Association Rule Mining finds:

A) Errors

B) Clusters

C) Predictions

D) Relationships

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46) The main goal of Machine Learning is to:

A) Store data

B) Learn patterns from data

C) Design hardware

D) Write programs manually

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

A) Jaccard

B) Cosine

C) Manhattan

D) Euclidean

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48) PCA is used for:

A) Dimensionality reduction

B) Regression

C) Feature selection

D) Classification

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49) Cross-validation helps in:

A) Data cleaning

B) Overfitting reduction

C) Visualization

D) Data entry

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50) Logistic Regression is mainly used for:

A) Clustering

B) Dimensionality reduction

C) Continuous output

D) Binary classification

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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