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 unsupervised learning, _______ is a method where the objective is to group similar items into sets.
- Principal Component Analysis
- Regression Analysis
- Hierarchical Clustering
- Decision Trees
The correct term is "Hierarchical Clustering." In unsupervised learning, clustering is a method used to group similar items or data points into sets or clusters based on their similarities. Hierarchical clustering is one of the techniques for this purpose. It creates a tree-like structure (dendrogram) to represent the relationships between data points, making it easier to identify groups of similar items.
You're working for a company that generates vast amounts of log data daily. The company wants to analyze this data to gain insights into user behavior and system performance. Which Big Data tool would be most suitable for storing and processing this data efficiently?
- Apache Hadoop
- Apache Spark
- Apache Kafka
- Apache Cassandra
Apache Kafka is a distributed streaming platform that is well-suited for storing and processing large amounts of log data efficiently, making it a top choice for real-time data streaming and analysis.
What is a potential consequence of biased algorithms in AI systems?
- Improved accuracy
- Enhanced user trust
- Unfair or discriminatory outcomes
- Faster data processing
Biased algorithms can lead to unfair or discriminatory outcomes, as they may favor certain groups over others. This can have significant ethical and legal implications, causing harm to individuals and undermining trust in AI systems.
In CNNs, the layers that preserve the spatial relationships between pixels by learning image features through small squares of input data are called _______ layers.
- Pooling
- Convolution
- Fully Connected
- Batch Normalization
In CNNs, the layers that preserve the spatial relationships between pixels by learning image features through small squares of input data are called "Convolution" layers. These layers apply convolutional operations to extract features from the input data, preserving the local spatial relationships in the image.
Which technology is NOT typically associated with real-time data processing?
- Apache Kafka
- Apache Spark
- Hadoop MapReduce
- MySQL
While Apache Kafka, Apache Spark, and Hadoop MapReduce are often used for real-time or near-real-time data processing, MySQL is a traditional relational database system that is not designed for real-time processing.
The _______ layer in a neural network is responsible for combining features across the input data, often used in CNNs.
- Input
- Hidden
- Output
- Convolutional
The blank should be filled with "Convolutional." Convolutional layers are used in Convolutional Neural Networks (CNNs) to combine features across input data by applying convolution operations. This is essential for tasks like image recognition.
In the context of model deployment, _______ is the process of ensuring the model's predictions remain consistent and accurate over time.
- Monitoring
- Training
- ETL
- Visualization
Model monitoring is the process of continuously tracking the performance and behavior of a deployed machine learning model. It involves checking for deviations, evaluating predictions against real-world data, and ensuring that the model remains accurate and reliable over time. Monitoring is crucial for maintaining model quality in production.
In Data Science, _______ is the process of cleaning and structuring the data to make it suitable for analysis.
- Data Mining
- Data Integration
- Data Wrangling
- Data Ingestion
In Data Science, data wrangling is the process of cleaning and structuring data to prepare it for analysis. This includes tasks such as handling missing values, transforming data, and dealing with inconsistencies.