Before deploying a model into production in the Data Science Life Cycle, it's essential to have a _______ phase to test the model's real-world performance.
- Training phase
- Deployment phase
- Testing phase
- Validation phase
Before deploying a model into production, it's crucial to have a testing phase to evaluate the model's real-world performance. This phase assesses how the model performs on unseen data to ensure its reliability and effectiveness.
You're analyzing a dataset with the heights of individuals. While the mean height is 165 cm, you notice a few heights recorded as 500 cm. These values are likely:
- Data entry errors
- Outliers
- Missing data
- Measurement errors
The heights recorded as 500 cm are likely outliers in the dataset. Outliers are data points that significantly differ from the majority of the data and may indicate measurement errors or anomalies. It's important to identify and handle outliers appropriately during data analysis.
In time series forecasting, which method captures both trend and seasonality in the data?
- Moving Average
- Exponential Smoothing
- ARIMA (AutoRegressive Integrated Moving Average)
- Exponential Moving Average
ARIMA (AutoRegressive Integrated Moving Average) captures both trend and seasonality in time series data. It combines autoregressive, differencing, and moving average components to model complex time series patterns, making it a powerful method for forecasting data with seasonal and trend components.
Which Python library is specifically designed for statistical data visualization and is built on top of Matplotlib?
- Seaborn
- Pandas
- Numpy
- Scikit-learn
Seaborn is a Python library built on top of Matplotlib, designed for statistical data visualization. It provides a high-level interface for creating informative and attractive statistical graphics, making it a valuable tool for data analysis and visualization.
In a convolutional neural network (CNN), which type of layer is responsible for reducing the spatial dimensions of the input?
- Convolutional Layer
- Pooling Layer
- Fully Connected Layer
- Batch Normalization Layer
The Pooling Layer in a CNN is responsible for reducing the spatial dimensions of the input. This layer downsamples the feature maps, which helps in retaining important features and reducing computational complexity.
Which component of the Hadoop ecosystem is primarily used for distributed data storage?
- HDFS (Hadoop Distributed File System)
- Apache Spark
- MapReduce
- Hive
HDFS (Hadoop Distributed File System) is the primary component in the Hadoop ecosystem for distributed data storage. It is designed to store large files across multiple machines and provides data durability and fault tolerance.
Text data from social media platforms, such as tweets or Facebook posts, is an example of which type of data?
- Structured data
- Semi-structured data
- Unstructured data
- Binary data
Text data from social media platforms is typically unstructured. It doesn't have a fixed format or schema. It may include text, images, videos, and other content without a well-defined structure, making it unstructured data.
The _______ step in the Data Science Life Cycle is crucial for understanding how the final model will be integrated and used in the real world.
- Data Exploration
- Data Preprocessing
- Model Deployment
- Data Visualization
The "Model Deployment" step in the Data Science Life Cycle is essential for taking the data science model from development to production. It involves integrating the model into real-world applications, making it a crucial phase.
XML and JSON data formats, which can have a hierarchical structure, are examples of which type of data?
- Unstructured Data
- Semi-Structured Data
- Structured Data
- NoSQL Data
XML and JSON are examples of semi-structured data. Semi-structured data is characterized by a hierarchical structure and flexible schemas, making it a middle ground between structured and unstructured data. It is commonly used in various data exchange and storage scenarios.
A tech company wants to run A/B tests on two versions of a machine learning model. What approach can be used to ensure smooth routing of user requests to the correct model version?
- Randomly assign users to model versions
- Use a feature flag system
- Rely on user self-selection
- Use IP-based routing
To ensure smooth routing of user requests to the correct model version in A/B tests, a feature flag system (option B) is commonly used. This approach allows controlled and dynamic switching of users between model versions. Randomly assigning users (option A) may not provide the desired control. Relying on user self-selection (option C) may lead to biased results, and IP-based routing (option D) lacks the flexibility and control of a feature flag system for A/B testing.