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MACHINE LEARNING (ML) EXAMINATION QUESTIONS AND CORRECT ANSWER WITH EXPLANATION GRADED A+ STUDY GUIDE

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Escrito en
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MACHINE LEARNING (ML) EXAMINATION QUESTIONS AND CORRECT ANSWER WITH EXPLANATION GRADED A+ STUDY GUIDE

Institución
MACHINE LEARNING
Grado
MACHINE LEARNING

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MACHINE LEARNING (ML) EXAMINATION QUESTIONS
AND CORRECT ANSWER WITH EXPLANATION GRADED
A+ STUDY GUIDE SOUTHERN NEW HAMPSHIRE
UNIVERSITY
1. Machine Learning is a branch of:
A. Artificial Intelligence
B. Operating Systems
C. Networking
D. Hardware design
Answer: A
Rationale: ML is a subset of AI.

2. Machine Learning enables systems to:
A. Learn from data
B. Store files only
C. Design hardware
D. Run OS only
Answer: A
Rationale: ML learns patterns from data.

3. The main goal of ML is to:
A. Improve performance through learning
B. Increase storage
C. Build OS
D. Design databases
Answer: A
Rationale: Learning from experience improves accuracy.

4. Training data is used to:
A. Train models
B. Test hardware
C. Store OS files
D. Run applications
Answer: A
Rationale: Models learn from training data.

,5. Testing data is used to:
A. Evaluate model performance
B. Train model
C. Store database
D. Build OS
Answer: A
Rationale: Measures generalization.

6. A dataset is:
A. Collection of data
B. Single file
C. OS program
D. Hardware device
Answer: A
Rationale: Organized data collection.

7. Feature in ML is:
A. Input variable
B. Output only
C. CPU function
D. OS process
Answer: A
Rationale: Model input variable.

8. Label in ML is:
A. Output variable
B. Input variable
C. Hardware
D. OS function
Answer: A
Rationale: Correct answer value.

9. Supervised learning uses:
A. Labeled data
B. Unlabeled data
C. No data
D. Random data
Answer: A
Rationale: Uses known outputs.

, 10. Unsupervised learning uses:
A. Unlabeled data
B. Labeled data
C. No data
D. Hardware data
Answer: A
Rationale: Finds hidden patterns.

11. Reinforcement learning is based on:
A. Reward and punishment
B. Static data
C. Databases
D. OS rules
Answer: A
Rationale: Learning through feedback.

12. Regression is used for:
A. Predicting continuous values
B. Classification
C. Sorting data
D. Encryption
Answer: A
Rationale: Numeric prediction.

13. Classification is used for:
A. Assigning categories
B. Predicting numbers
C. Storing data
D. Sorting files
Answer: A
Rationale: Label prediction.

14. Clustering is:
A. Grouping similar data
B. Labeling data
C. Encrypting data
D. OS scheduling
Answer: A
Rationale: Unsupervised grouping.

Escuela, estudio y materia

Institución
MACHINE LEARNING
Grado
MACHINE LEARNING

Información del documento

Subido en
22 de junio de 2026
Número de páginas
19
Escrito en
2025/2026
Tipo
Examen
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