Which NLP task involves determining the emotional tone behind a series of words?
- Sentiment Analysis
- Named Entity Recognition
- Part-of-Speech Tagging
- Machine Translation
Sentiment Analysis is an NLP task that involves determining the emotional tone or sentiment behind a series of words, often classifying it as positive, negative, or neutral. It's crucial for understanding public opinion and customer feedback.
Which type of network architecture is primarily used for image classification tasks in deep learning?
- Recurrent Neural Network (RNN)
- Convolutional Neural Network
- Long Short-Term Memory (LSTM)
- Feedforward Neural Network
Convolutional Neural Networks (CNNs) are specifically designed for image classification tasks. They use convolutional layers to capture spatial hierarchies in the input data, making them highly effective for image recognition and analysis.
An e-commerce platform is trying to predict the amount a user would spend in the next month based on their past purchases. Which type of learning and algorithm would be most suitable for this?
- Supervised Learning with Linear Regression
- Unsupervised Learning with Principal Component Analysis
- Reinforcement Learning with Deep Q-Networks
- Semi-Supervised Learning with K-Nearest Neighbors
Supervised Learning with Linear Regression is appropriate for predicting a continuous target variable (spending amount) based on historical data. Unsupervised learning is not suitable for prediction tasks, reinforcement learning is for sequential decisions, and semi-supervised learning combines labeled and unlabeled data.
In the Data Science Life Cycle, which step involves defining the objectives and understanding the problem statement?
- Data Preparation
- Data Analysis
- Problem Formulation
- Model Deployment
The initial step in the Data Science Life Cycle is problem formulation. In this step, the objectives are defined, and the problem statement is understood. It sets the direction for the entire data science project.
In Matplotlib, the foundation for all visualizations is the _______ object, which provides the canvas where plots are drawn.
- Figure
- Canvas
- Plot
- Chart
Matplotlib uses the "Figure" object as the foundational canvas for all visualizations. It serves as the top-level container for plots, allowing you to add multiple subplots and customize various aspects of the visualizations.
The technique where spatial transformations are applied to input images to boost the performance and versatility of models is called _______ in computer vision.
- Edge Detection
- Data Augmentation
- Optical Flow
- Feature Extraction
Data augmentation involves applying spatial transformations to input images, such as rotation, flipping, or cropping, to increase the diversity of the training data. This technique enhances model generalization and performance.
Which NLP model captures the context of words by representing them as vectors?
- Word2Vec
- Regular Expressions
- Decision Trees
- Linear Regression
Word2Vec is a widely used NLP model that captures word context by representing words as vectors in a continuous space. It preserves the semantic meaning of words, making it a powerful tool for various NLP tasks like word embeddings and text analysis. The other options are not NLP models and do not capture word context in the same way.
The term "Data Science" is an interdisciplinary field that uses various methods and techniques from which of the following domains?
- Computer Science and Mathematics
- History and Art
- Literature and Geography
- Music and Philosophy
Data Science draws from Computer Science and Mathematics to develop analytical and computational techniques for data analysis. This interdisciplinary approach is essential for solving complex data-related problems.
In a Convolutional Neural Network (CNN), what operation involves reducing the spatial dimensions of the input?
- Pooling (subsampling)
- Convolution
- Batch Normalization
- Activation Function
Pooling (subsampling) is used in CNNs to reduce the spatial dimensions of the input, allowing the network to focus on the most relevant features. It helps control the computational complexity and overfitting.
How does Spark achieve faster data processing compared to traditional MapReduce?
- By using in-memory processing
- By executing tasks sequentially
- By running on a single machine
- By using persistent storage for intermediate data
Apache Spark achieves faster data processing by using in-memory processing. Unlike traditional MapReduce, which writes intermediate results to disk, Spark caches intermediate data in memory, reducing I/O operations and speeding up data processing significantly. This in-memory processing is one of Spark's key features for performance optimization.