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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - Project management and collaboration - AutoAI and automatic model building - Data asset management |
| Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Deploying models as REST APIs - Watson Assistant integration - Watson Discovery overview |
| Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Interactive dashboards and reports - Communicating findings to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
1. Which metric is commonly used to evaluate the performance of a regression model?
A) Recall
B) Accuracy
C) Mean Squared Error (MSE)
D) Precision
2. What is the primary purpose of partitioning data into training and test sets?
A) To evaluate the model's performance on unseen data
B) To ensure that the model gets exposed to all possible data scenarios during training
C) To maximize the accuracy of the model by using all data for training
D) To increase the computational efficiency of model training
3. In defining a business problem, what is essential to align with the stakeholders?
A) Business objectives
B) Technical requirements
C) Data sources
D) Project milestones
4. Which statement describes bagging?
A) Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
B) Building models and using their output as features into a final model.
C) Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
D) Building models in parallel and aggregating their predictions to select the final prediction.
5. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
A) F1-score
B) Recall
C) Accuracy
D) Precision
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: A |
