Top 30 Machine Learning MCQs with Answers

The demand for machine learning (ML) and artificial intelligence (AI) specialists is estimated to grow exponentially by 2027. With the increase in demand and employment rate, there is increased competition to get the best machine learning jobs in high-tech companies with attractive salaries. Companies narrow down their hiring by filtering the top candidates among the best ones.

So, before you take the leap into the world of FAANG interviews and beyond, take some time to do a self-assessment. Go through the list of MCQs to delve deeper into your understanding of machine learning concepts and methodologies.

Machine Learning MCQs

Let us begin with machine learning MCQ questions and answers that cover ML basics to more advanced levels. You can evaluate your understanding of machine learning with these questions. This will help you build confidence and get better at machine learning concepts.

Here are some machine learning MCQ questions and answers.

Q1. ________ is the application of machine learning.

1. Sentimental analysis
2. Traffic prediction
3. Speech and face recognition
4. All of the above

Answer: d. All of the above

Q2. From the following, _______ is not a feature of K-Nearest Neighbors (KNN).

1. KNN is simple and pretty intuitive
2. KNN constantly evolves
3. KNN has assumptions
4. No Training Step

Answer: c. KNN has assumptions

Q3. Which of the following is the main goal of machine learning?

1. Enable computers to learn data
2. To automate manual tasks
3. To make computers intelligent
4. To generate self-aware machines

Answer: a. Enable computer to learn data

Q4. Choose the real-world application of ML from the following.

1. Fraud detection
2. Chatbots
3. Digital assistants
4. All of the above

Answer: d. All of the above

Q5. Which of the following ML algorithms can be used with unlabelled data?

1. Instance-based algorithms
2. Regression algorithms
3. Clustering algorithm
4. All of the above

Answer: c. Clustering algorithms

Read More: Machine Learning Engineer Salary in the USA

Q6. Machine learning is a subset of _______.

1. Deep learning
2. Data earning
3. Artificial intelligence
4. All of the above

Answer: c. Artificial intelligence

Q7. From the following, choose the successful applications of ML

1. Learning to recognize spoken words
2. Learning to classify astronomical structures
3. Learning to drive an autonomous vehicles
4. All of the above

Answer: d. All of the above

Q8. From the following, ________ is not machine learning.

1. Rule-based interference
2. Artificial intelligence
3. Both a and b
4. Neither a nor b

Answer: a. Rule-based interference

Q9. Choose a machine learning technique from the following.

1. Speech recognition and regression
2. Genetic programming and induction learning
3. Both a and b are correct
4. Neither a nor b is correct

Answer: b. Genetic programming and induction learning

Q10. Which of the following elements defines the Candidate-Elimination algorithm?

1. Just a set of candidates’ hypothesis
2. Set of instances, set of candidate hypothesis
3. Depends on the dataset
4. Just a set of instances

Answer: b. Set of instances, set of candidate hypothesis

Q11. FIND-S algorithm ignores ______.

1. Negative
2. Positive
3. With positive or negative
4. Neither positive nor negative

Answer: a. Negative

Q12. ________ is an example of stacking.

1. Voting Classifier
2. Random Forest
3. AdaBoost
4. Bagged Decision Trees

Answer: a. Voting Classifier

Q13. ________ is a clustering algorithm in ML.

1. CART
2. Expectation Maximisation
3. Apriori
4. Gaussian Naive Bayes

Answer: b. Expectation Maximisation

Q14. Which of the following is the most significant phase in the genetic algorithm?

1. Fitness function
2. Selection
3. Mutation
4. Crossover

Answer: d. Crossover

Q15. Dimensionality reduction reduces in _______.

1. Collinearity
2. Entropy
3. Stochastics
4. Performance

Answer: a. Collinearity

Q16. _______ model is a generative model used in ML

1. Naive Bayes
2. Linear Regression
3. Logistic Regression
4. Support vector machines

Answer: a. Naive Bayes

Q17. Choose the invalid statement for Ensemble voting.

1. It takes non-linear combinations for learners
2. It takes linear combinations for learners
3. It is the easiest way to merge multiple classifiers
4. It is also called ensembles and linear opinion pools

Answer: a. It takes non-linear combinations for learners

Q18. ML comprises learning algorithms that

1. Improve their performance
2. Over time with experience
3. At executing some task
4. All of the above

Answer: d. All of the above

Q19. Data points possess negative residual if

1. The regression lines truly pass through the point
2. They are above the regression line
3. They are below the regression line
4. None of the above

Answer: c. They are below the regression line

Q20. Which of the following models is trained with data only in a single batch?

1. Batch learning
2. Offline learning
3. Both a and b
4. Neither a nor b

Answer: c. Both a and b

Q21. ______machine learning algorithm is associated with the idea of bagging?

1. Decision tree
2. Classification
3. Random forest
4. Regression

Answer: c. Random forest

Q22. Different learning methods do not involve _______.

1. Analogy
2. Memorization
3. Introduction
4. Deduction

Answer: c. Introduction

Q23. From the following, choose the evaluation metric commonly used for classification tasks in the presence of class imbalance.

1. R-squared
2. F1-score
3. Accuracy
4. Mean Squared Error (MSE)

Answer: b. F1-score

Q24. ________ is not a supervised ML algorithm.

1. K-means
2. SVM for classification problems
3. Decision Tree
4. Naive Bayes

Answer: a. K-means

Q25. What do we call an application of machine learning methods to large databases?

1. Big data computing
2. Artificial intelligence
3. Data mining
4. Internet of Things (IoT)

Answer: c. Data mining

Q26. From the following, which is the appropriate definition of neuro software?

1. Software used by neurosurgeons
2. Software used to examine neurons
3. An easy and powerful neural network
4. Both a and b

Answer: c. An easy and powerful neural network

Q27. If the ML model output does not include the target variable, the model is called ______.

1. Predictive model
2. Descriptive model
3. Reinforcement learning
4. All of the above

Answer: b. Descriptive  model

Q28. _______ is a supervised learning task.

1. Reinforcement learning
2. Dimensionality reduction
3. Clustering
4. Classification

Answer: d. Classification

Q29. ______ algorithm is used to identify frequent itemsets in transactional databases.

1. K-Means clustering
2. Decision Trees
3. Support Vector Machine (SVM)
4. Apriori algorithm

Answer: d. Apriori algorithm

Q30. Choose a valid statement with respect to bias and variance.

1. Models that underfit possess a low variance
2. Models that overfit possess a low bias
3. Models that underfit possess a high bias
4. Both a and b

Answer: d. Both a and b

Read More: 50+ Machine Learning Interview Questions

Advance Your Machine Learning Career With Interview Kickstart

Machine learning MCQs are usually of medium difficulty but with practice and preparation, you can ace any machine learning interview. With Interview Kickstart’s Machine Learning Course, you will learn the fundamentals of ML, programming languages like Python, classic machine learning concepts.

Led by industry experts (from the likes of Google, Facebook, and LinkedIn), our instructors will help you build a strong foundation in the subject, and give you all the tools required to be successful in your career and land your dream job.

You can check out some of the success stories of our alumni who have advanced their careers with the help of Interview Kickstart.

FAQs: Machine Learning MCQs

Q1. What are the career opportunities in machine learning?

Some of the most prominent career opportunities in machine learning are: machine learning engineer, NLP engineer, machine learning developer, AI engineer, product manager, data scientist and software developer.

Q2. How much does a machine learning engineer earn?

The average salary of a machine learning engineer in the USA is $152,973 per year.

Q3. What are the skills required to be a machine learning engineer?

Here are some of the skills required to be a successful machine learning engineer.

• ML basics and advanced concepts
• Statistics and probability
• Version control
• Programming languages like Python, C, Java or more
• DevOps

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