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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Deploying models as REST APIs - Watson Assistant integration - Monitoring deployed models |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Project management and collaboration - Data governance and cataloging - Data asset management |
| Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components - Data collection, preparation, and exploration |
IBM Watson Data Scientist v1 Sample Questions:
1. When would you use AutoAI to select algorithms for your model?
A) Only when working with small datasets due to processing limitations.
B) When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
C) When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
D) When the model requirements are extremely specific and no standard algorithm fits.
2. Cloud Pak for Data's integration with Spark allows users to:
A) Perform complex computations on small datasets only
B) Leverage distributed computing for processing large datasets efficiently
C) Avoid using any form of data processing or analysis
D) Use Spark exclusively for data visualization purposes
3. How do you determine which tool to use based on algorithm requirements and expertise?
A) Choose the newest tools on the market for the most up-to-date features.
B) Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
C) Always use the most complex tool to ensure the model's accuracy.
D) Consider the tool's compatibility with the algorithm requirements and the team's expertise.
4. In defining a business problem, what is essential to align with the stakeholders?
A) Data sources
B) Business objectives
C) Technical requirements
D) Project milestones
5. Which statistical method reduces the number of attributes by lumping highly correlated attributes together?
A) Binning
B) Long Short Term Memory Network (LSTM)
C) Principal Component Analysis (PCA)
D) Synthetic Minority Over-sampling Technique (SMOTE)
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: C |
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