After deploying a Gradient Boosting model, you observe that its performance deteriorates after some time. What might be a potential step to address this?
- Re-train the model with additional data
- Increase the learning rate
- Reduce the model complexity
- Regularly update the model with new data
To address the performance deterioration of a deployed Gradient Boosting model, it's crucial to regularly update the model with new data (option D). Data drift is common, and updating the model ensures it adapts to the changing environment. While re-training with additional data (option A) may help, regularly updating the model with new data is more sustainable. Increasing the learning rate (option B) or reducing model complexity (option C) may not be effective in addressing performance deterioration over time.
In time series analysis, what is a sequence of data points measured at successive points in time called?
- Time steps
- Data snapshots
- Data vectors
- Time series data
In time series analysis, a sequence of data points measured at successive points in time is called "time series data." This data structure is used to analyze and forecast trends, patterns, and dependencies over time. It's fundamental in fields like finance, economics, and climate science.
In the context of neural networks, what does the term "backpropagation" refer to?
- Training a model using historical data
- Forward pass computation
- Adjusting the learning rate
- Updating model weights
"Backpropagation" in neural networks refers to the process of updating the model's weights based on the computed errors during the forward pass. It's a key step in training neural networks and involves minimizing the loss function.
You're building a system that needs to store vast amounts of unstructured data, like user posts, images, and comments. Which type of database would be the best fit for this use case?
- Relational Database
- Document Database
- Graph Database
- Key-Value Store
A document database, like MongoDB, is well-suited for storing unstructured data with variable schemas, making it an ideal choice for use cases involving user posts, images, and comments.
Which algorithm is inspired by the structure and functional aspects of biological neural networks?
- K-Means Clustering
- Naive Bayes
- Support Vector Machine
- Artificial Neural Network
The algorithm inspired by biological neural networks is the Artificial Neural Network (ANN). ANNs consist of interconnected artificial neurons that attempt to simulate the structure and function of the human brain, making them suitable for various tasks like pattern recognition.
Which method facilitates the deployment of multiple models, where traffic is routed to different models based on specific conditions?
- A/B testing
- Model ensembling
- Model serving
- Canary deployment
Model serving is the method that allows you to deploy multiple models and route traffic to them based on specific conditions. It plays a critical role in managing different model versions and serving the right model for different use cases.
You are working on a project where you need to predict the next word in a sentence. Which type of neural network architecture would be most suitable for this task?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM) Network
- Generative Adversarial Network (GAN)
Predicting the next word in a sentence is a sequential data problem, making it suitable for recurrent neural networks. LSTMs are particularly effective for this task as they can capture long-term dependencies in the data, which is essential for predicting words in a sentence.
In the realm of Data Science, the library _______ in Python is widely used for data manipulation and cleaning.
- TensorFlow
- Pandas
- Matplotlib
- Scikit-learn
Pandas is a popular Python library for data manipulation and cleaning. It provides data structures and functions for working with structured data, making it a valuable tool in data science, which makes option B the correct answer.
The method where data values are shifted and rescaled to range between 0 and 1 is called _______.
- Data Normalization
- Data Imputation
- Data Resampling
- Data Transformation
The method of shifting and rescaling data values to range between 0 and 1 is known as "data normalization." This is commonly used in machine learning to ensure that all features have the same scale, preventing certain features from dominating others.
The _______ typically works closely with business stakeholders to understand their requirements and translate them into data-driven insights.
- Data Scientist
- Data Analyst
- Data Engineer
- Business Analyst
Data Scientists often work closely with business stakeholders to understand their requirements and translate them into data-driven insights. They use statistical and analytical techniques to derive insights that support decision-making.