Machine Learning Basics MCQs
Machine Learning Basics – MCQs with Answers & Explanations
Class: 8 | Subject: Computer Science
Section: Artificial Intelligence
Topic: Machine Learning Basics
CBSE Board Examination Practice Questions
Section: Artificial Intelligence
Topic: Machine Learning Basics
CBSE Board Examination Practice Questions
These Multiple Choice Questions (MCQs) are designed strictly as per the NCERT syllabus, making them ideal for CBSE Class 8 Computer Science examination standard.
1. What is Machine Learning?
Answer: b)
Explanation: Machine Learning (ML) is a part of Artificial Intelligence where computers learn patterns from data and improve their performance without being explicitly programmed for every task.
Explanation: Machine Learning (ML) is a part of Artificial Intelligence where computers learn patterns from data and improve their performance without being explicitly programmed for every task.
2. Machine Learning is a subset of:
Answer: b)
Explanation: Artificial Intelligence is a broad field, and Machine Learning is one of its important branches that focuses on learning from data.
Explanation: Artificial Intelligence is a broad field, and Machine Learning is one of its important branches that focuses on learning from data.
3. What does ML mainly require to learn?
Answer: a)
Explanation: Machine Learning systems require large amounts of data to identify patterns and make predictions.
Explanation: Machine Learning systems require large amounts of data to identify patterns and make predictions.
4. An example of Machine Learning is:
Answer: b)
Explanation: Spam filters learn from examples of spam emails and automatically classify new emails.
Explanation: Spam filters learn from examples of spam emails and automatically classify new emails.
5. ML systems improve with:
Answer: a)
Explanation: The more relevant data a system receives, the better it learns and improves accuracy.
Explanation: The more relevant data a system receives, the better it learns and improves accuracy.
6. Predicting exam marks based on past performance is an example of:
Answer: a)
Explanation: ML uses past data to predict future results.
Explanation: ML uses past data to predict future results.
7. Which type of ML uses labeled data?
Answer: a)
Explanation: Supervised learning uses labeled examples to train the model.
Explanation: Supervised learning uses labeled examples to train the model.
8. Which ML type finds hidden patterns without labels?
Answer: b)
Explanation: Unsupervised learning identifies patterns without predefined answers.
Explanation: Unsupervised learning identifies patterns without predefined answers.
9. Training data is used to:
Answer: a)
Explanation: Training data helps the model learn patterns.
Explanation: Training data helps the model learn patterns.
10. ML is widely used in:
Answer: a)
Explanation: Online platforms use ML to recommend products and videos.
Explanation: Online platforms use ML to recommend products and videos.
11. ML helps computers to:
Answer: a)
Explanation: ML enables systems to learn without manual coding.
Explanation: ML enables systems to learn without manual coding.
12. Data in ML can be:
Answer: a)
Explanation: ML works with different forms of digital data.
Explanation: ML works with different forms of digital data.
13. Testing data is used to:
Answer: a)
Explanation: Testing data evaluates how well the model learned.
Explanation: Testing data evaluates how well the model learned.
14. ML models identify:
Answer: a)
Explanation: Recognizing patterns is the core task of ML.
Explanation: Recognizing patterns is the core task of ML.
15. Face recognition uses:
Answer: a)
Explanation: ML analyzes facial features for identification.
Explanation: ML analyzes facial features for identification.
16. Predicting weather with data is:
Answer: a)
Explanation: ML analyzes past weather data for predictions.
Explanation: ML analyzes past weather data for predictions.
17. ML algorithms improve by:
Answer: a)
Explanation: More experience (data) improves performance.
Explanation: More experience (data) improves performance.
18. ML is used in:
Answer: a)
Explanation: ML assists doctors in diagnosis and analysis.
Explanation: ML assists doctors in diagnosis and analysis.
19. ML helps in fraud detection by:
Answer: a)
Explanation: ML identifies suspicious transaction patterns.
Explanation: ML identifies suspicious transaction patterns.
20. The main goal of ML is to:
Answer: a)
Explanation: ML aims to make intelligent decisions and predictions based on data.
Explanation: ML aims to make intelligent decisions and predictions based on data.
