Artificial Intelligence & ML for Competitive Exam Prep

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Artificial Intelligence & ML

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Artificial Intelligence & ML MCQ & Objective Questions

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving fields that play a crucial role in modern technology and education. Understanding these concepts is essential for students preparing for exams, as they frequently appear in various formats, including MCQs and objective questions. Practicing AI and ML MCQs helps students reinforce their knowledge, identify important questions, and enhance their exam preparation strategies.

What You Will Practise Here

Fundamentals of Artificial Intelligence and Machine Learning
Key algorithms used in AI and ML, such as decision trees and neural networks
Applications of AI in real-world scenarios
Important definitions and terminologies in AI and ML
Understanding data preprocessing and feature selection
Evaluation metrics for machine learning models
Common AI and ML frameworks and tools

Exam Relevance

Artificial Intelligence and Machine Learning are significant topics in various educational boards, including CBSE and State Boards, as well as competitive exams like NEET and JEE. Questions often focus on theoretical concepts, practical applications, and algorithmic understanding. Students can expect to encounter multiple-choice questions that assess their grasp of key principles, making it vital to practice with objective questions to excel in these assessments.

Common Mistakes Students Make

Confusing AI with ML and failing to understand their differences
Overlooking the importance of data quality in machine learning
Misinterpreting evaluation metrics and their implications
Neglecting to review key algorithms and their applications
Struggling with complex diagrams and flowcharts related to AI processes

FAQs

Question: What are some common applications of Artificial Intelligence?
Answer: AI is used in various fields, including healthcare for diagnosis, finance for fraud detection, and customer service through chatbots.

Question: How can I improve my understanding of Machine Learning concepts?
Answer: Regular practice with MCQs and objective questions, along with studying key theories and algorithms, can significantly enhance your understanding.

Start solving practice MCQs on Artificial Intelligence and ML today to test your understanding and boost your confidence for upcoming exams. Remember, consistent practice is the key to success!

Cloud ML Services Clustering Methods: K-means, Hierarchical Clustering Methods: K-means, Hierarchical - Advanced Concepts Clustering Methods: K-means, Hierarchical - Applications Clustering Methods: K-means, Hierarchical - Case Studies Clustering Methods: K-means, Hierarchical - Competitive Exam Level Clustering Methods: K-means, Hierarchical - Higher Difficulty Problems Clustering Methods: K-means, Hierarchical - Numerical Applications Clustering Methods: K-means, Hierarchical - Problem Set Clustering Methods: K-means, Hierarchical - Real World Applications CNNs and Deep Learning Basics Decision Trees and Random Forests Decision Trees and Random Forests - Advanced Concepts Decision Trees and Random Forests - Applications Decision Trees and Random Forests - Case Studies Decision Trees and Random Forests - Competitive Exam Level Decision Trees and Random Forests - Higher Difficulty Problems Decision Trees and Random Forests - Numerical Applications Decision Trees and Random Forests - Problem Set Decision Trees and Random Forests - Real World Applications Evaluation Metrics Evaluation Metrics - Advanced Concepts Evaluation Metrics - Applications Evaluation Metrics - Case Studies Evaluation Metrics - Competitive Exam Level Evaluation Metrics - Higher Difficulty Problems Evaluation Metrics - Numerical Applications Evaluation Metrics - Problem Set Evaluation Metrics - Real World Applications Feature Engineering and Model Selection Feature Engineering and Model Selection - Advanced Concepts Feature Engineering and Model Selection - Applications Feature Engineering and Model Selection - Case Studies Feature Engineering and Model Selection - Competitive Exam Level Feature Engineering and Model Selection - Higher Difficulty Problems Feature Engineering and Model Selection - Numerical Applications Feature Engineering and Model Selection - Problem Set Feature Engineering and Model Selection - Real World Applications Linear Regression and Evaluation Linear Regression and Evaluation - Advanced Concepts Linear Regression and Evaluation - Applications Linear Regression and Evaluation - Case Studies Linear Regression and Evaluation - Competitive Exam Level Linear Regression and Evaluation - Higher Difficulty Problems Linear Regression and Evaluation - Numerical Applications Linear Regression and Evaluation - Problem Set Linear Regression and Evaluation - Real World Applications ML Model Deployment - MLOps Model Deployment Basics Model Deployment Basics - Advanced Concepts Model Deployment Basics - Applications Model Deployment Basics - Case Studies Model Deployment Basics - Competitive Exam Level Model Deployment Basics - Higher Difficulty Problems Model Deployment Basics - Numerical Applications Model Deployment Basics - Problem Set Model Deployment Basics - Real World Applications Neural Networks Fundamentals Neural Networks Fundamentals - Advanced Concepts Neural Networks Fundamentals - Applications Neural Networks Fundamentals - Case Studies Neural Networks Fundamentals - Competitive Exam Level Neural Networks Fundamentals - Higher Difficulty Problems Neural Networks Fundamentals - Numerical Applications Neural Networks Fundamentals - Problem Set Neural Networks Fundamentals - Real World Applications NLP - Tokenization, Embeddings Reinforcement Learning Intro RNNs and LSTMs Supervised Learning: Regression and Classification Supervised Learning: Regression and Classification - Advanced Concepts Supervised Learning: Regression and Classification - Applications Supervised Learning: Regression and Classification - Case Studies Supervised Learning: Regression and Classification - Competitive Exam Level Supervised Learning: Regression and Classification - Higher Difficulty Problems Supervised Learning: Regression and Classification - Numerical Applications Supervised Learning: Regression and Classification - Problem Set Supervised Learning: Regression and Classification - Real World Applications Support Vector Machines Overview Support Vector Machines Overview - Advanced Concepts Support Vector Machines Overview - Applications Support Vector Machines Overview - Case Studies Support Vector Machines Overview - Competitive Exam Level Support Vector Machines Overview - Higher Difficulty Problems Support Vector Machines Overview - Numerical Applications Support Vector Machines Overview - Problem Set Support Vector Machines Overview - Real World Applications Unsupervised Learning: Clustering Unsupervised Learning: Clustering - Advanced Concepts Unsupervised Learning: Clustering - Applications Unsupervised Learning: Clustering - Case Studies Unsupervised Learning: Clustering - Competitive Exam Level Unsupervised Learning: Clustering - Higher Difficulty Problems Unsupervised Learning: Clustering - Numerical Applications Unsupervised Learning: Clustering - Problem Set Unsupervised Learning: Clustering - Real World Applications

Q. Which of the following is NOT a common method for deploying machine learning models?

A. REST API
B. Batch processing
C. Embedded systems
D. Data warehousing

Solution

Data warehousing is not a common method for deploying machine learning models; instead, models are typically deployed via REST APIs, batch processing, or embedded systems.

Correct Answer: D — Data warehousing

Q. Which of the following is NOT a common method for monitoring deployed models?

A. Performance metrics tracking
B. User feedback collection
C. Data versioning
D. Real-time prediction logging

Solution

Data versioning is not a monitoring method; it is a practice used to manage changes in datasets over time.

Correct Answer: C — Data versioning

Q. Which of the following is NOT a common technique for feature scaling?

A. Min-Max Scaling
B. Standardization
C. Log Transformation
D. Feature Selection

Solution

Feature selection is the process of selecting a subset of relevant features, while Min-Max Scaling, Standardization, and Log Transformation are techniques for feature scaling.

Correct Answer: D — Feature Selection

Q. Which of the following is NOT a common technique for feature selection?

A. Recursive Feature Elimination
B. Principal Component Analysis
C. Random Forest Importance
D. Gradient Descent

Solution

Gradient Descent is an optimization algorithm, not a feature selection technique.

Correct Answer: D — Gradient Descent

Q. Which of the following is NOT a common technique in feature engineering?

A. Normalization
B. One-hot encoding
C. Cross-validation
D. Polynomial features

Solution

Cross-validation is a model evaluation technique, not a feature engineering technique.

Correct Answer: C — Cross-validation

Q. Which of the following is NOT a common technique in feature selection?

A. Recursive Feature Elimination
B. Principal Component Analysis
C. Random Forest Importance
D. Gradient Descent

Solution

Gradient Descent is an optimization algorithm, not a feature selection technique.

Correct Answer: D — Gradient Descent

Q. Which of the following is NOT a common use case for clustering?

A. Market segmentation
B. Anomaly detection
C. Image classification
D. Social network analysis

Solution

Image classification is typically a supervised learning task, while clustering is used for market segmentation, anomaly detection, and social network analysis.

Correct Answer: C — Image classification

Q. Which of the following is NOT a deployment strategy for machine learning models?

A. Blue-Green Deployment
B. Canary Release
C. A/B Testing
D. Data Augmentation

Solution

Data Augmentation is a technique used during training, not a deployment strategy.

Correct Answer: D — Data Augmentation

Q. Which of the following is NOT a deployment strategy?

A. Blue-green deployment
B. Canary deployment
C. Shadow deployment
D. Data augmentation

Solution

Data augmentation is a technique used during model training to increase the diversity of the training dataset, not a deployment strategy.

Correct Answer: D — Data augmentation

Q. Which of the following is NOT a feature engineering technique?

A. Binning
B. Feature Extraction
C. Data Augmentation
D. Gradient Descent

Solution

Gradient Descent is an optimization algorithm, not a feature engineering technique.

Correct Answer: D — Gradient Descent

Q. Which of the following is NOT a kernel function used in Support Vector Machines?

A. Linear kernel
B. Polynomial kernel
C. Radial Basis Function (RBF) kernel
D. Logistic kernel

Solution

The Logistic kernel is not commonly used in Support Vector Machines; the other three are standard kernel functions.

Correct Answer: D — Logistic kernel

Q. Which of the following is NOT a key component of MLOps?

A. Continuous integration
B. Model monitoring
C. Data labeling
D. Version control

Solution

Data labeling is a part of the data preparation process, not a key component of MLOps.

Correct Answer: C — Data labeling

Q. Which of the following is NOT a limitation of linear regression?

A. Assumes a linear relationship
B. Sensitive to outliers
C. Can only handle numerical data
D. Can model complex relationships

Solution

Linear regression cannot model complex relationships, which is a limitation, but it can handle numerical data and is sensitive to outliers.

Correct Answer: D — Can model complex relationships

Q. Which of the following is NOT a method of feature extraction?

A. TF-IDF
B. Bag of Words
C. One-Hot Encoding
D. Linear Regression

Solution

Linear Regression is a modeling technique, not a feature extraction method.

Correct Answer: D — Linear Regression

Q. Which of the following is NOT a method of feature selection?

A. Recursive feature elimination
B. Lasso regression
C. Principal component analysis
D. Random forest feature importance

Solution

Principal component analysis (PCA) is a dimensionality reduction technique, not a feature selection method.

Correct Answer: C — Principal component analysis

Q. Which of the following is NOT a method of linkage in hierarchical clustering?

A. Single linkage
B. Complete linkage
C. Average linkage
D. Random linkage

Solution

Random linkage is not a recognized method of linkage in hierarchical clustering; the common methods include single, complete, and average linkage.

Correct Answer: D — Random linkage

Q. Which of the following is NOT a step in the K-means clustering algorithm?

A. Assigning data points to the nearest centroid
B. Updating the centroid positions
C. Calculating the silhouette score
D. Choosing the initial centroids

Solution

Calculating the silhouette score is not a step in the K-means algorithm; it is an evaluation metric used after clustering.

Correct Answer: C — Calculating the silhouette score

Q. Which of the following is NOT a supervised learning algorithm?

A. Support Vector Machines
B. Decision Trees
C. K-Means Clustering
D. Random Forests

Solution

K-Means Clustering is an unsupervised learning algorithm, while the others are supervised learning algorithms.

Correct Answer: C — K-Means Clustering

Q. Which of the following is NOT a type of clustering algorithm?

A. Hierarchical Clustering
B. Density-Based Clustering
C. K-Nearest Neighbors
D. K-Means Clustering

Solution

K-Nearest Neighbors is a classification algorithm, not a clustering algorithm.

Correct Answer: C — K-Nearest Neighbors

Q. Which of the following is NOT a type of hierarchical clustering?

A. Single linkage
B. Complete linkage
C. K-means linkage
D. Average linkage

Solution

K-means linkage is not a type of hierarchical clustering; it refers to the K-means algorithm itself.

Correct Answer: C — K-means linkage

Q. Which of the following is NOT a type of neural network architecture?

A. Convolutional Neural Network
B. Recurrent Neural Network
C. Support Vector Machine
D. Feedforward Neural Network

Solution

Support Vector Machine is a different type of machine learning algorithm, not a neural network architecture.

Correct Answer: C — Support Vector Machine

Q. Which of the following is NOT a type of neural network?

A. Convolutional Neural Network
B. Recurrent Neural Network
C. Support Vector Machine
D. Feedforward Neural Network

Solution

Support Vector Machine (SVM) is a different type of machine learning algorithm, not a neural network.

Correct Answer: C — Support Vector Machine

Q. Which of the following is NOT a type of supervised learning?

A. Classification
B. Regression
C. Clustering
D. Time Series Forecasting

Solution

Clustering is an unsupervised learning technique, while classification and regression are types of supervised learning.

Correct Answer: C — Clustering

Q. Which of the following is NOT a type of SVM?

A. C-SVM
B. Nu-SVM
C. Linear SVM
D. K-Means SVM

Solution

K-Means SVM is not a recognized type of SVM; the other options are valid types of Support Vector Machines.

Correct Answer: D — K-Means SVM

Q. Which of the following is NOT a type of tokenization?

A. Word tokenization
B. Sentence tokenization
C. Character tokenization
D. Phrase tokenization

Solution

Phrase tokenization is not a standard type of tokenization; the common types are word, sentence, and character tokenization.

Correct Answer: D — Phrase tokenization

Q. Which of the following is NOT a typical application of clustering?

A. Market segmentation
B. Document classification
C. Image compression
D. Time series forecasting

Solution

Time series forecasting is not a typical application of clustering; it is more related to supervised learning.

Correct Answer: D — Time series forecasting

Q. Which of the following is NOT a typical application of neural networks?

A. Facial recognition
B. Stock market prediction
C. Basic arithmetic calculations
D. Language translation

Solution

Basic arithmetic calculations are not a typical application of neural networks, which are better suited for complex pattern recognition tasks.

Correct Answer: C — Basic arithmetic calculations

Q. Which of the following is NOT a typical application of SVM?

A. Face detection
B. Spam detection
C. Stock price prediction
D. Handwriting recognition

Solution

While SVM can be used for regression, it is not typically the first choice for stock price prediction.

Correct Answer: C — Stock price prediction

Q. Which of the following is NOT a typical deployment environment for machine learning models?

A. Cloud services
B. Edge devices
C. Local servers
D. Data warehouses

Solution

Data warehouses are primarily used for data storage and analysis, not for deploying machine learning models.

Correct Answer: D — Data warehouses

Q. Which of the following is NOT a typical use case for clustering?

A. Image segmentation
B. Anomaly detection
C. Predicting stock prices
D. Document clustering

Solution

Predicting stock prices is not a typical use case for clustering, as it is a supervised learning task.

Correct Answer: C — Predicting stock prices

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