1. What type of data issue should be addressed before using it in a machine learning model?
A: Missing values or incomplete data entries
2. What is one of the main tasks during the monitoring and maintenance phase?
A: Retraining the model as needed
3. Which of the following best describes the objective of model evaluation?
A: Test the model to ensure performance
4. What does the model engineering stage typically involve?
A: Building and training the machine learning model
5. What is the key consideration when deciding whether to use machine learning for a business problem?
A: Whether the problem involves complex rules that are difficult to define manually
6. How can the use of machine learning affect a project’s timeline?
A: The model training and data preparation phases can add significant time
7. During which stage would you assess the scope and feasibility of a machine learning project?
A: Planning
8. Which of the following is the first stage of the machine learning lifecycle?
A: Planning
9. Which stage involves building and training the model?
A: Model Engineering
10. What is the key activity during the model deployment stage?
A: Integrating the model into production environments
11. Which phase in a machine learning project typically requires the most time?
A: Data preparation and cleaning
12. What is the primary goal of the data preparation stage?
A: Collect, clean, and process data
13. A team is focused on developing and optimizing various machine learning algorithms, fine-tuning hyperparameters, and performing experiment tracking to find the best-performing model. Which stage are they engaged in?
A: Model Engineering – Build and train the model, track experiments.
14. Following the deployment of an ML model, the engineering team sets up tools to track its performance over time and ensures that it continues to make accurate predictions as new data flows in. What phase is this team working in?
A: Monitoring and Maintenance – Continuously monitor performance and update as needed.
15. A data science team spends weeks cleaning a large dataset, removing duplicates, and handling missing values to make the data ready for analysis. Which stage are they in?
A: Data Preparation – Collect, clean, and process data.
16. Which question is critical to address early during machine learning model deployment?
A: How will the model be retrained with new data?
17. In which stage is the model’s performance tested to ensure it meets set standards?
A: Model Evaluation
18. After weeks of training multiple models, the team runs performance tests and compares the results with baseline metrics to determine if the models meet the project’s objectives. Which stage does this describe?
A: Model Evaluation – Test and ensure model performance meets standards.
19. Which of the following scenarios is most suitable for a machine learning solution?
A: Predicting stock market trends using historical data
20. Which stage focuses on tracking and updating the model’s performance in production?
A: Monitoring and Maintenance
21. A company has completed model testing and is now embedding the model into its web platform for real-time customer predictions. Which stage are they currently in?
A: Model Deployment – Integrate models into production environments.
22. A team is working on identifying the key metrics and feasibility of an ML project before deciding to move forward. They are also defining the success criteria for the project. Which stage are they in?
A: Planning – Assess scope, feasibility, and metrics.