Which technique involves leveraging a pre-trained model on a new, but related task?
- Transfer Learning
- Unsupervised Learning
- Reinforcement Learning
- Ensemble Learning
Transfer Learning is the technique that involves leveraging a pre-trained model on a new, but related task. It allows you to take advantage of knowledge acquired from one domain and apply it to a different but related problem, which can save time and resources in training deep learning models.
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.
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.
While preparing data for a machine learning model, you realize that the 'Height' column has some missing values. Upon closer inspection, you find that these missing values often correspond to records where the 'Age' column has values less than 1 year. What might be a reasonable way to handle these missing values?
- Impute missing values with the mean height
- Impute missing values with 0
- Leave missing values as they are
- Impute missing values based on 'Age'
In this case, it might be reasonable to leave missing values as they are. Imputing with the mean height or 0 may introduce bias, and imputing based on 'Age' should be done carefully, as infants may have different height characteristics than adults. Depending on the context and dataset size, leaving the missing values untouched might be the best choice.