A healthcare dataset contains a column for 'Age' and another for 'Blood Pressure'. If you want to ensure both features contribute equally to the distance metric in a k-NN algorithm, what should you do?
- Standardize both 'Age' and 'Blood Pressure'
- Normalize both 'Age' and 'Blood Pressure'
- Use Euclidean distance as the metric
- Give more weight to 'Blood Pressure'
To ensure that both 'Age' and 'Blood Pressure' contribute equally to the distance metric in a k-NN algorithm, you should standardize both features. Standardization scales the features to have a mean of 0 and a standard deviation of 1, preventing one from dominating the distance calculation. Normalization may not achieve this balance, and changing the distance metric or giving more weight to one feature can bias the results.
In transfer learning, what is the process of updating the weights of the pre-trained model with new data called?
- Feature Engineering
- Fine-Tuning
- Data Augmentation
- Model Stacking
In transfer learning, fine-tuning is the process of updating the weights of a pre-trained model with new data. This allows the model to adapt to the specific characteristics of the new data while leveraging the knowledge learned from the pre-training on a different but related task.
The _______ framework in Hadoop allows for distributed processing of large datasets across clusters.
- HBase
- HDFS
- YARN
- Pig
The YARN (Yet Another Resource Negotiator) framework in Hadoop is responsible for managing resources and enabling the distributed processing of large datasets across clusters. It allows efficient resource utilization and job scheduling, making it a critical component in Hadoop's ecosystem.
Which of the following is NOT a typical concern when deploying a machine learning model to production?
- Model performance degradation
- Data privacy and security
- Scalability issues
- Data preprocessing
Data preprocessing (Option D) is a crucial concern when deploying machine learning models to production. It involves preparing and transforming data to ensure it's compatible with the model, which is a common concern during deployment.
Your organization wants to move away from traditional batch processing of data and is looking for a tool that can offer in-memory processing for faster analytics. Which Big Data framework would you recommend?
- Apache Storm
- Apache Hadoop
- Apache HBase
- Apache Spark
Apache Spark provides in-memory processing capabilities, allowing for faster analytics compared to traditional batch processing. It's an excellent choice when speed and real-time data processing are priorities.
Which of the following best describes the main activity of a Data Analyst?
- Building predictive models
- Writing complex code
- Generating insights from data
- Designing databases
Data Analysts primarily focus on generating insights from data. They use statistical and analytical techniques to draw meaningful conclusions and communicate their findings to support decision-making.
In a confusion matrix, the value representing correctly predicted positive instances is called the _______.
- True Positive
- False Positive
- True Negative
- False Negative
In a confusion matrix, the value representing correctly predicted positive instances is called "True Positive." This refers to the cases where the model correctly identified positive instances in the dataset. Understanding True Positives is essential for assessing the model's performance, especially in classification tasks.
In which data visualization tool can you create interactive dashboards and stories for better business insights?
- Matplotlib
- Tableau
- ggplot2
- Power BI
Power BI is a data visualization tool that enables users to create interactive dashboards and stories to gain better business insights. It offers a wide range of features for data analysis, visualization, and reporting, making it a popular choice for business intelligence.
For a company looking to understand the sentiment of their product reviews using natural language processing (NLP), which role would be most suited to undertake this task?
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- NLP Engineer
Data Scientists are well-suited for tasks like understanding sentiment through NLP. They have the skills to leverage machine learning and NLP techniques to extract insights from text data. They can develop models to analyze product reviews and assess sentiment.
For machine learning model deployment in a production environment, which tool or language is often integrated due to its performance and scalability?
- Python
- R
- Java
- Kubernetes
Java is often integrated into production environments for machine learning model deployment due to its performance and scalability. Java is known for its speed, robustness, and suitability for large-scale applications. It is commonly used to build APIs and services for serving machine learning models in real-time production systems. Python and R are often used in model development, but Java is favored for deployment. Kubernetes is an orchestration tool.