In a Hadoop ecosystem, which tool is primarily used for data ingestion from various sources?

  • HBase
  • Hive
  • Flume
  • Pig
Apache Flume is primarily used in the Hadoop ecosystem for data ingestion from various sources. It is a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of data to Hadoop's storage or other processing components. Flume is essential for handling data ingestion pipelines in Hadoop environments.

In which scenario would Min-Max normalization be a less ideal choice for data scaling?

  • When outliers are present
  • When the data has a normal distribution
  • When the data will be used for regression analysis
  • When interpretability of features is crucial
Min-Max normalization can be sensitive to outliers. If outliers are present in the data, this scaling method can compress the majority of data points into a narrow range, making it less suitable for preserving the information in the presence of outliers. In scenarios where outliers are a concern, alternative scaling methods like Robust Scaling may be preferred.

The process of converting a trained machine learning model into a format that can be used by production systems is called _______.

  • Training
  • Validation
  • Serialization
  • Normalization
Serialization is the process of converting a trained machine learning model into a format that can be used by production systems. It involves saving the model's parameters, architecture, and weights in a portable format so that it can be loaded and utilized for making predictions in real-time applications.

What is the primary challenge associated with training very deep neural networks without any specialized techniques?

  • Overfitting due to small model capacity
  • Exploding gradients
  • Vanishing gradients
  • Slow convergence
The primary challenge of training very deep neural networks without specialized techniques is the vanishing gradient problem. As gradients are back-propagated through numerous layers, they can become extremely small, leading to slow convergence and making it difficult to train deep networks. Vanishing gradients hinder the ability of earlier layers to update their weights effectively.

When scaling features, which method is less influenced by outliers?

  • Standardization (Z-score scaling)
  • Min-Max Scaling
  • Robust Scaling
  • Log Transformation
Robust Scaling is less influenced by outliers because it scales the data based on the interquartile range (IQR) rather than the mean and standard deviation. This makes it a suitable choice when dealing with datasets that contain outliers.

The process of adjusting the weights in a neural network based on the error rate is known as _______.

  • Backpropagation
  • Data Preprocessing
  • Hyperparameter Tuning
  • Reinforcement Learning
Backpropagation is the process of adjusting the weights of a neural network to minimize the error between predicted and actual values. It is a fundamental training algorithm for neural networks, and it involves calculating gradients and updating weights to optimize the network's performance.

In the context of Big Data, which system is designed to provide high availability and fault tolerance by replicating data blocks across multiple nodes?

  • Hadoop Distributed File System (HDFS)
  • Apache Kafka
  • Apache Spark
  • NoSQL databases
The Hadoop Distributed File System (HDFS) is designed for high availability and fault tolerance. It achieves this by replicating data blocks across multiple nodes in a distributed cluster, ensuring data integrity and reliable data storage. This is a fundamental feature of Hadoop's file system.

A self-driving car company has millions of images labeled with either "pedestrian" or "no pedestrian". They want the car to automatically detect pedestrians. Which type of learning and algorithm would be optimal for this task?

  • Supervised Learning with Convolutional Neural Networks
  • Unsupervised Learning with Apriori Algorithm
  • Reinforcement Learning with Monte Carlo Methods
  • Semi-Supervised Learning with DBSCAN
Supervised Learning with Convolutional Neural Networks (CNNs) is the optimal choice for image classification tasks like pedestrian detection. CNNs are designed for such tasks, while the other options are not suitable for image classification. Apriori is used for association rule mining, reinforcement learning for decision-making, and DBSCAN for clustering.

Apache Spark offers an optimized engine that supports _______ computations, enabling faster data analytics.

  • Batch
  • Single-threaded
  • Real-time
  • Static
Apache Spark offers an optimized engine that supports real-time computations. This capability enables faster data analytics by allowing Spark to process data as it arrives, making it suitable for real-time data processing and analytics tasks. This is a key advantage of Spark over traditional batch processing systems.

Which statistical measure represents the middle value in a dataset when it's ordered from least to greatest?

  • Mean
  • Mode
  • Median
  • Range
The median is the middle value in a dataset when it's ordered. It's a measure of central tendency that's not affected by extreme values (outliers). To find the median, you arrange the data in ascending order, and if there's an even number of values, it's the average of the two middle values.