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| Section | Weight | Objectives |
|---|---|---|
| Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Visualization best practices - Interactive dashboards and reports |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data asset management - Project management and collaboration - Data governance and cataloging |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework |
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization - Model deployment and monitoring |
| Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Watson Discovery overview - Monitoring deployed models - Deploying models as REST APIs |
In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
Correct Answer: C 🗳️
Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
Correct Answer: D 🗳️
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?
Correct Answer: A 🗳️
Which statement describes bagging?
Correct Answer: C 🗳️
When comparing models to choose the best one, which factor is least likely to be considered?
Correct Answer: A 🗳️
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