The process of combining multiple levels of categorical variables based on frequency or other criteria into a single level is known as category _______.
- Binning
- Merging
- Encoding
- Reduction
Combining multiple levels of categorical variables into a single level based on frequency or other criteria is known as "category merging" or "level merging." This simplifies the categorical variable, reduces complexity, and can improve the efficiency of certain models.
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.
In deep learning, the technique used to skip one or more layers by connecting non-adjacent layers is called _______.
- Dropout
- Batch Normalization
- Skip Connections
- Pooling
In deep learning, the technique used to skip one or more layers by connecting non-adjacent layers is called "Skip Connections." Skip connections allow the model to bypass one or more layers and facilitate the flow of information from one layer to another, helping in the training of deep neural networks.
A retailer wants to forecast the sales of a product for the next six months based on the past three years of monthly sales data. Which time series forecasting model might be most appropriate given the presence of annual seasonality in the sales data?
- Exponential Smoothing
- ARIMA (AutoRegressive Integrated Moving Average)
- Linear Regression
- Moving Average
ARIMA is a suitable time series forecasting model when dealing with data that exhibits annual seasonality, as it can capture both the trend and seasonality components in the data. Exponential Smoothing, Linear Regression, and Moving Average are not as effective for modeling seasonal data.
Which of the following tools is typically used to manage and query relational databases in Data Science?
- Excel
- Hadoop
- SQL (Structured Query Language)
- Tableau
SQL (Structured Query Language) is a standard tool used for managing and querying relational databases. Data scientists frequently use SQL to extract, manipulate, and analyze data from these databases, making it an essential skill for working with structured data.