Statistics for Data Science & Analytics

The quiz is about MCQs Machine Learning Questions. Test your Machine Learning knowledge with these MCQs! Covering structured vs. unstructured data, types of machine learning models (supervised learning, unsupervised learning, and reinforcement learning), AI applications, and popular algorithms like decision trees, neural networks, and SVM. Perfect for interviews & exams! Let us start with the MCQs Machine Learning Quiz now.

Online MCQs Machine Learning Quiz with Answers

1. What term is used to describe a structured collection of data?

 Data Format

 Data Store

 Data Grid

 Data Set

2. Select the model you would try first if you had labeled non-continuous value data.

 Linear Regression

 Neither

 Either one

 Classification

3. What is the primary purpose of an Application Programming Interface (API)?

 Facilitate communication between hardware components

 Create graphical representations of data

 Design user interfaces for applications

 Allow communication between software components

4. Which type of machine learning model is primarily used to predict a numeric value?

 Regression

 Reinforcement

 Classification

 Clustering

5. Which of the following is an example of structured data?

 A compilation of cat videos

 Comments and likes on Facebook post

 A database of individual’s addresses, phone numbers, and post codes

 Lung x-ray images of smokers

6. Machine Learning programs can help:

 Automate resume screening

 Automate visual inspection in a manufacturing line

 Customize product recommendations

 Automate lead sorting in sales

7. What is the key difference between supervised and unsupervised models?

 Supervised models learn to predict based on known labels whereas unsupervised models are more concerned with discovering patterns in the dataset

 Unsupervised learning requires even more labeled training data since the model is less controlled during training

 Supervised models are better for prediction than unsupervised because the label is known

 Unsupervised models need fewer data points for accurate modeling as compared to supervised learning

8. Machine learning is an “iterative” process, meaning that an AI team often has to try many ideas before coming up with something good enough, rather than have the first thing they try work.

 True

 False

9. In a REST API architecture, what does the client typically receive from the web service after sending a request?

 XML document

 JSON file

 Binary data

 HTML file

10. PyTorch is what type of Python library?

 Linear algebra

 Machine learning

 Data visualization

 HTTP

11. What is Machine learning?

 The selective acquisition of knowledge through the use of manual programs

 The selective acquisition of knowledge through the use of computer programs

 The autonomous acquisition of knowledge through the use of computer programs

 The autonomous acquisition of knowledge through the use of manual programs

12. Suppose you are building a trigger word detection system and want to hire someone to build a system to map from Input $A$ (audio clip) to Output $B$ (whether the trigger word was said) using existing AI technology. Out of the list below, which of the following hires would be most suitable for writing this software?

 Data engineer

 AI Product Manager

 Machine learning engineer

 Machine learning researcher

13. Which of these is NOT one of the main skills embodied by data scientists?

 Hacking skills

 Math and stats

 Computer and IT skills

 Machine learning

14. Which of the following are machine learning models?

 Model training

 Unsupervised Learning

 Supervised Learning

 Reinforcement Learning

15. Suppose you want to use Machine Learning to help your sales team with automatic lead sorting. That is, input $A$ (a sales prospect) and output $B$ (whether your sales team should prioritize them). The 3 steps of the workflow, in scrambled order, are:

(i) Deploy a trained model and get data back from users
(ii) Collect data with both A and B
(iii) Train a machine learning system to input A and output B
What is the correct ordering of these steps?

 (i) (ii) (iii)

 (i) (iii) (ii)

 (ii) (iii) (i)

 (ii) (i) (iii)

16. Which R library is used for machine learning?

 caret

 ggplot

 dplyr

 string4

17. What tool is used to edit front-end languages like HTML, JavaScript, and CSS in the context of exploring machine learning models?

 GitHub

 Red Hat OpenShift

 CodePen

 IBM Cloud

18. Which of the following statements do you agree with?

 The terms “Deep learning” and “neural network” are used almost interchangeably

 Deep learning is a type of machine learning. (i.e., all deep learning algorithms are machine learning algorithms)

 AI is a type of deep learning. (i.e., all AI algorithms are deep learning algorithms)

 The terms “Machine learning” and “data science” are used almost interchangeably

19. Which of the following areas does not belong to machine learning?

 Reinforcement Learning

 Supervised Learning

 Numerical Computer

 Unsupervised Learning

20. Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.

 True

 False

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Online MCQs Machine Learning Quiz

• What is the primary purpose of an Application Programming Interface (API)?
• Which of the following are machine learning models?
• In a REST API architecture, what does the client typically receive from the web service after sending a request?
• What term is used to describe a structured collection of data?
• Which type of machine learning model is primarily used to predict a numeric value?
• Which R library is used for machine learning?
• What tool is used to edit front-end languages like HTML, JavaScript, and CSS in the context of exploring machine learning models?
• PyTorch is what type of Python library?
• Which of the following is an example of structured data?
• Which of these is NOT one of the main skills embodied by data scientists?
• Which of the following statements do you agree with?
• Machine learning is an “iterative” process, meaning that an AI team often has to try many ideas before coming up with something good enough, rather than have the first thing they try work.
• Suppose you want to use Machine Learning to help your sales team with automatic lead sorting. That is, input $A$ (a sales prospect) and output $B$ (whether your sales team should prioritize them). The 3 steps of the workflow, in scrambled order, are: (i) Deploy a trained model and get data back from users, (ii) Collect data with both A and B (iii) Train a machine learning system to input A and output B What is the correct ordering of these steps?
• Machine Learning programs can help:
• Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.
• Suppose you are building a trigger word detection system and want to hire someone to build a system to map from Input $A$ (audio clip) to Output $B$ (whether the trigger word was said) using existing AI technology. Out of the list below, which of the following hires would be most suitable for writing this software?
• What is the key difference between supervised and unsupervised models?
• Select the model you would try first if you had labeled non-continuous value data.
• What is Machine learning?
• Which of the following areas does not belong to machine learning?

Take Quiz on Deep Learning

Introduction to Machine Learning

Machine Learning (ML) is a transformative branch of Artificial Intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It powers applications like recommendation systems, fraud detection, and self-driving cars.

Key Concepts in Machine Learning

1. Supervised Learning – Algorithms learn from labeled data (e.g., classification, regression, spam detection, sales forecasting).
2. Unsupervised Learning – Finds hidden patterns in unlabeled data (e.g., clustering, dimensionality reduction, customer segmentation).
3. Reinforcement Learning – Trains models via rewards/punishments (e.g., game-playing AI, self-driving cars).
4. Deep Learning – Uses neural networks for complex tasks (e.g., image recognition).

What is Machine Learning?

Machine Learning algorithms analyze large datasets to identify patterns, make predictions, and automate decision-making. Unlike traditional programming, machine learning systems adapt and improve over time, making them essential for data-driven businesses.

Why Learn Machine Learning?

• High demand for ML engineers in tech, healthcare, finance, and e-commerce.
• Automates repetitive tasks, improving efficiency and accuracy.
• Enhances predictive analytics, helping businesses make smarter decisions.

Top Machine Learning Applications

• Natural Language Processing (NLP) – Powers chatbots like ChatGPT.
• Computer Vision – Used in facial recognition and medical imaging.
• Recommendation Systems – Drives platforms like Amazon and YouTube.

Think you have mastered machine learning? Put your skills to the test with this 20-question MCQs Machine Learning Quiz covering core concepts like supervised vs. unsupervised learning, neural networks, model evaluation, and more! Whether you are a student, data scientist, researcher, or ML enthusiast, this machine learning quiz will challenge and sharpen your understanding. This machine learning quiz is perfect for exam preparation, interviews, or self-assessment. Let us start with the online machine learning quiz now.

Online Machine Learning Quiz

• Real-world problems can be highly complex and should only be solved by complex logical rules
• The best way to solve a problem using machine learning is by using the technique with the highest probability of solving it.
• Machine learning is a combination of different capabilities all working together and cannot be defined in a singular way.
• Machine learning is a breakthrough system whereby solutions to complex problems, such as human and environmental errors, can be programmed directly into machines.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Predicting future fashion trends so he can plan for new designs and products sooner.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Using customers’ measurements to automatically recommend the right size.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Sort new clothing stock according to audience preference.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Recommending clothing budgets for customers based on their socio-economic status.
• XYZ is developing an app that reads text messages out loud from a screen in Spanish. What machine learning approach would you recommend to help Jake make his app a success?
• Which of the following best describes machine learning?
• Which of the following describes the way machine learning solves real-world problems?
• Which of the following businesses could potentially benefit the most from machine learning?
• Which of the following are components in building a machine learning algorithm?
• Suppose we build a prediction algorithm on a data set, and it is 100% accurate on that data set. Why might the algorithm not work well if we collect a new data set?
• What are the typical sizes for the training and test sets?
• What are some common error rates for predicting binary variables (i.e., variables with two possible values like yes/no, disease/normal, clicked/didn’t click)?
• Suppose that we have created a machine learning algorithm that predicts whether a link will be clicked with 99% sensitivity and 99% specificity. The rate the link is clicked is 1/1000 of visits to a website. If we predict the link will be clicked on a specific visit, what is the probability it will be clicked?
• Select the scenarios where Machine Learning is particularly beneficial compared to traditional programming.
• Which Python library is used for machine learning?
• What is the primary task of model training in machine learning?

Take a Quiz about Data Science

Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. From recommendation systems to self-driving cars, ML powers modern innovations. It uses algorithms like neural networks, decision trees, and regression to analyze data and improve accuracy over time.