A company wants to deploy a deep learning model in an environment with limited computational resources. What challenge related to deep learning models might they face, and what potential solution could address it?
- Overfitting due to small training datasets
- High memory and processing demands of deep models
- Lack of labeled data for training deep models
- Slow convergence of deep models due to early stopping or small batch sizes
In a resource-constrained environment, one major challenge is the high memory and processing demands of deep learning models. They can be computationally expensive. A potential solution could be model optimization techniques like quantization, pruning, or using smaller network architectures to reduce memory and processing requirements.
For applications requiring ACID transactions across multiple documents or tables, which database type would you lean towards?
- NoSQL Database
- Relational Database
- In-memory Database
- Columnar Database
In cases where ACID (Atomicity, Consistency, Isolation, Durability) transactions across multiple documents or tables are required, relational databases are typically preferred. They provide strong data consistency and support complex transactions.
A streaming platform is receiving real-time data from various IoT devices. The goal is to process this data on-the-fly and produce instantaneous analytics. Which Big Data technology is best suited for this task?
- Apache Flink
- Apache HBase
- Apache Hive
- Apache Pig
Apache Flink is designed for real-time stream processing and analytics, making it a powerful choice for handling data from IoT devices in real-time and producing instantaneous analytics.
Which 'V' of Big Data refers to the increasing rate at which data is produced and collected?
- Volume
- Velocity
- Variety
- Veracity
The 'V' of Big Data that refers to the increasing rate at which data is produced and collected is "Velocity." It reflects the high speed at which data is generated and the need to process it rapidly for real-time insights and decision-making.
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.
You're working on a real estate dataset where the price of the house is significantly influenced by its age and square footage. To capture this combined effect, what type of new feature could you create?
- Interaction feature
- Categorical feature with age groups
- Time-series feature
- Ordinal feature
To capture the combined effect of age and square footage on house price, you can create an interaction feature. This feature multiplies or combines the two variables to represent their interaction, allowing the model to consider how they jointly affect the target variable. An interaction feature is valuable in regression models.
In a traditional relational database, the data stored in a tabular format is often referred to as _______ data.
- Structured Data
- Unstructured Data
- Semi-Structured Data
- Raw Data
In a traditional relational database, the data is structured and organized in tables with a predefined schema. It's commonly referred to as "Structured Data" because it adheres to a strict structure and schema.
The metric _______ is particularly useful when the cost of false positives is higher than false negatives.
- Precision
- Recall
- F1 Score
- Specificity
The metric "Precision" is particularly useful when the cost of false positives is higher than false negatives. Precision focuses on the accuracy of positive predictions, making it relevant in scenarios where minimizing false positives is critical, such as medical diagnosis or fraud detection.
You are designing a deep learning model for a multi-class classification task with 10 classes. Which activation function and loss function combination would be the most suitable for the output layer?
- Sigmoid activation function with Mean Squared Error (MSE) loss
- Softmax activation function with Cross-Entropy loss
- ReLU activation function with Mean Absolute Error (MAE) loss
- Tanh activation function with Huber loss
For multi-class classification with 10 classes, the most suitable activation function for the output layer is Softmax, and the most suitable loss function is Cross-Entropy. Softmax provides class probabilities, and Cross-Entropy measures the dissimilarity between the predicted probabilities and the true class labels. This combination is widely used in classification tasks.