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.

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.

Which of the following is not typically a layer in a CNN?

  • Convolutional Layer
  • Fully Connected Layer
  • Recurrent Layer
  • Pooling Layer
Recurrent Layers are not typically used in Convolutional Neural Networks. They are more common in Recurrent Neural Networks (RNNs) and are used for sequential data processing, unlike CNNs, which are designed for grid-like data.

The operation in CNNs that combines the outputs of neuron clusters and produces a single output for the cluster is known as _______.

  • Activation Function
  • Pooling
  • Convolutions
  • Fully Connected
In CNNs, the operation that combines the outputs of neuron clusters and produces a single output for the cluster is called "Pooling." Pooling reduces the spatial dimensions of the feature maps, making them smaller and more computationally efficient while retaining important features.

A healthcare organization stores patient records in a database. Each record contains structured fields like name, age, and diagnosis. Additionally, there are scanned documents and notes from doctors. Which term best describes the type of data in this healthcare database?

  • Structured data
  • Semi-structured data
  • Unstructured data
  • Big data
The healthcare database contains a mix of structured data (name, age, diagnosis) and semi-structured data (scanned documents and doctor's notes). Semi-structured data includes elements with partial structure, like documents, which can be tagged or indexed for better retrieval.

When a model performs well on training data but poorly on unseen data, what issue might it be facing?

  • Overfitting
  • Underfitting
  • Data leakage
  • Bias-variance tradeoff
The model is likely facing the issue of overfitting. Overfitting occurs when the model learns the training data too well, including noise, resulting in excellent performance on the training set but poor generalization to unseen data. It's an example of a high-variance problem in the bias-variance tradeoff. To address overfitting, techniques like regularization and more data are often used.

Which type of database is ideal for handling hierarchical data and provides better scalability, MongoDB or MySQL?

  • MongoDB
  • MySQL
  • Both MongoDB and MySQL
  • Neither MongoDB nor MySQL
MongoDB is a NoSQL database that is ideal for handling hierarchical data and provides better scalability for unstructured data. MongoDB uses BSON (Binary JSON) format, which makes it a good choice for applications that require flexibility and scalability in dealing with complex data structures.