What is the primary goal of tokenization in NLP?

  • Removing stop words
  • Splitting text into words
  • Extracting named entities
  • Translating text to other languages
The primary goal of tokenization in NLP is to split text into words or tokens. This process is essential for various NLP tasks such as text analysis, language modeling, and information retrieval. Tokenization helps in breaking down text into meaningful units for analysis.

For models with a large number of layers, which technique helps in improving the internal covariate shift and accelerates the training?

  • Stochastic Gradient Descent (SGD) with a small learning rate
  • Batch Normalization
  • L1 Regularization
  • DropConnect
Batch Normalization is a technique used to improve the training of deep neural networks. It addresses the internal covariate shift problem by normalizing the activations of each layer. This helps in accelerating training and allows for the use of higher learning rates without the risk of divergence. It also aids in better gradient flow.

In the context of AI ethics, what is the primary concern of "interpretability"?

  • Ensuring AI is always right
  • Making AI faster
  • Understanding how AI makes decisions
  • Controlling the cost of AI deployment
"Interpretability" in AI ethics is about understanding how AI systems make decisions. It's crucial for accountability, transparency, and identifying and addressing potential biases in AI algorithms. AI being right or fast is important but not the primary concern in this context.

You are responsible for ensuring that the data in your company's data warehouse is consistent, reliable, and easily accessible. Recently, there have been complaints about data discrepancies. Which stage in the ETL process should you primarily focus on to resolve these issues?

  • Extraction
  • Transformation
  • Loading
  • Data Ingestion
The Transformation stage is where data discrepancies are often addressed. During transformation, data is cleaned, normalized, and validated to ensure consistency and reliability. This stage is critical for data quality and consistency in the data warehouse. Extraction involves collecting data, Loading is about data loading into the warehouse, and Data Ingestion is the process of bringing data into the system.

A common method to combat the vanishing gradient problem in RNNs is to use _______.

  • Long Short-Term Memory (LSTM)
  • Decision Trees
  • K-Means Clustering
  • Principal Component Analysis
To address the vanishing gradient problem in RNNs, one common technique is to use Long Short-Term Memory (LSTM) networks. LSTMs are a type of RNN that helps mitigate the vanishing gradient problem by preserving and updating information over long sequences. LSTMs are designed to capture long-term dependencies and are more effective than traditional RNNs for tasks where data from distant time steps is important.

In a task involving the classification of hand-written digits, the model is failing to capture intricate patterns in the data. Adding more layers seems to exacerbate the problem due to a certain issue in training deep networks. What is this issue likely called?

  • Overfitting
  • Vanishing Gradient
  • Underfitting
  • Exploding Gradient
The issue where adding more layers to a deep neural network exacerbates the training problem due to diminishing gradients is called "Vanishing Gradient." It occurs when gradients become too small during backpropagation, making it challenging for deep networks to learn intricate patterns in the data.

The _______ is a measure of the relationship between two variables and ranges between -1 and 1.

  • P-value
  • Correlation coefficient
  • Standard error
  • Regression
The measure of the relationship between two variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation), is known as the "correlation coefficient." It quantifies the strength and direction of the linear relationship between variables.

How do federated learning approaches differ from traditional machine learning in terms of data handling?

  • Federated learning doesn't use data
  • Federated learning relies on centralized data storage
  • Federated learning trains models on decentralized data
  • Traditional machine learning trains models on a single dataset
Federated learning trains machine learning models on decentralized data sources without transferring them to a central server. This approach is privacy-preserving and efficient. In contrast, traditional machine learning typically trains models on a single, centralized dataset, which may raise data privacy concerns.

For graph processing in a distributed environment, Apache Spark provides the _______ library.

  • GraphX
  • HBase
  • Pig
  • Storm
Apache Spark provides the "GraphX" library for graph processing in a distributed environment. GraphX is a part of the Spark ecosystem and is used for graph analytics and computation. It's a powerful tool for analyzing graph data.

In computer vision, what process involves converting an image into an array of pixel values?

  • Segmentation
  • Feature Extraction
  • Pre-processing
  • Quantization
Pre-processing in computer vision typically includes steps like resizing, filtering, and transforming an image. It's during this phase that an image is converted into an array of pixel values, making it ready for subsequent analysis and feature extraction.