A. Model Building
B. Evaluation
C. Data Preparation
D. Business Understanding
A. Limit the ability to update models based on new data
B. Enhance the reproducibility and efficiency of model deployments
C. Increase the need for manual intervention in the model lifecycle
D. Reduce the scalability of deployed solutions
A. Use Spark exclusively for data visualization purposes
B. Avoid using any form of data processing or analysis
C. Leverage distributed computing for processing large datasets efficiently
D. Perform complex computations on small datasets only
A. Always use the most complex tool to ensure the model's accuracy.
B. Choose the newest tools on the market for the most up-to-date features.
C. Consider the tool's compatibility with the algorithm requirements and the team's expertise.
D. Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
A. Supervised learning algorithms can automatically label data.
B. Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
C. Supervised learning can work without any labeled data.
D. Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
A. Splitting based on the order of data collection
B. Stratified split
C. Using only the majority class for splitting
D. Random split without considering the target variable
A. Identifying potential data sources
B. Establishing a clear problem statement
C. Defining key performance indicators (KPIs)
D. Selecting the analytical techniques
A. Enhancing collaboration across data science and IT teams
B. Automatically generating insights without data analysis
C. Streamlining the integration of diverse data sources
D. Facilitating secure and scalable data connectivity
A. The number of available data scientists
B. The personal preferences of the project stakeholders
C. The most recent technological trends
D. The specific objectives and desired outcomes of the project
