gate data science and ai
gate mock test ml ai
1. What is meant by Artificial Intelligence?
a) Artificial intelligence is defined as a field aiming to make humans more intelligent.
b) Artificial intelligence is defined as a field aiming to improve security.
c) Artificial intelligence is defined as a field aiming to mine the data.
d) Artificial intelligence is defined as a field aiming to develop intelligent machines.
The correct answer is option d, artificial intelligence is defined as a field aiming to develop intelligent machines. It is basically the development of intelligent systems that can work and reach similar to human beings.
2. What is the main aim of Artificial Intelligence?
a) To solve real-world issues
b) To explain different sorts of intelligence
c) To solve artificial problems
d) To obtain information about scientific causes
The correct answer is option b. the main aim of artificial intelligence is considered to explain different sorts of intelligence around the world.
3. ____ is the informed search method.
a) Memory Bound Heuristic Search
b) A * Search
c) Best First Search
d) All of the above
The correct answer is option d. All the above-mentioned options are the informed search methods that are used in Artificial Intelligence.
4. What is the primary characteristic of supervised learning?
a) No labeled data is required.
b) The model learns from labeled data.
c) The model doesn't require any input features.
d) It is used only for regression tasks.
B. The model learns from labeled data.
Explanation: In supervised learning, the model learns from a labeled dataset, where each data point has an associated label or target value. The primary characteristic is that it requires labeled data for training. The model learns to make predictions based on the input features and the corresponding target values.
5. Which of the following is a common example of a supervised learning task?
a) Image segmentation
b) Anomaly detection
d) Sentiment analysis
D. Sentiment analysis
Explanation: Sentiment analysis is a typical example of a supervised learning task. In this task, the model is trained on a dataset of text samples with corresponding sentiment labels (e.g., positive, negative, or neutral). The model learns to classify text into sentiment categories based on the labeled data.
6. In a classification problem with two classes (binary classification), how many output nodes are typically used in a supervised neural network?
c) Depends on the number of features
Explanation: In binary classification, there are typically two classes or categories to predict, such as yes/no or spam/ham. In a neural network for binary classification, a single output node is often used, where the output represents the probability or confidence score of belonging to one of the two classes.
7. Which evaluation metric is commonly used for assessing the performance of a classification model when dealing with imbalanced datasets?
c) Mean Absolute Error (MAE)
d) R-squared (R²)
Explanation: The F1-score is a common evaluation metric for classification models, particularly when dealing with imbalanced datasets. It combines precision and recall, making it a robust choice when there is an unequal distribution of classes. It provides a balanced measure of a model's accuracy in such cases.
8. What is the primary goal of unsupervised learning?
a) Predicting future outcomes
b) Finding patterns and relationships in data
c) Classifying data into predefined categories
d) Labeling data with ground truth values
B. Finding patterns and relationships in data
Explanation: The primary goal of unsupervised learning is to find patterns, structures, or relationships in data without the use of labeled target values. It seeks to discover hidden structures, clusters, or associations within the dataset.
9. Which unsupervised learning technique is used for dimensionality reduction while preserving the most important information in the data?
c) Principal Component Analysis (PCA)
d) Decision Trees
C. Principal Component Analysis (PCA)
Explanation: PCA (Principal Component Analysis) is an unsupervised learning technique used for dimensionality reduction. It identifies the principal components (linear combinations of features) that capture the most variance in the data, allowing for dimensionality reduction while preserving the essential information.
10. Which of the following clustering algorithms is based on the idea of partitioning data points into clusters such that each data point belongs to the cluster with the nearest mean?
c) Hierarchical Clustering
Explanation: K-Means is a popular clustering algorithm that partitions data points into clusters based on their proximity to the cluster's mean (centroid). It aims to minimize the within-cluster variance, assigning each data point to the cluster with the nearest mean.
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