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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components - Data collection, preparation, and exploration |
| Topic 2: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring |
| Topic 3: Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Deploying models as REST APIs - Watson Discovery overview - Watson Assistant integration |
| Topic 4: Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Interactive dashboards and reports - Visualization best practices |
| Topic 5: Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data asset management - Data governance and cataloging - Project management and collaboration |
IBM Watson Data Scientist v1 Sample Questions:
Which of the following is true about the AUC measure in the context of classification models?
- A. It is less useful when the classes are highly imbalanced.
- B. It indicates the number of false positives.
- C. It measures the model's accuracy using a single threshold.
- D. It represents the degree of separability between classes.
Correct Answer: D 🗳️
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 in parallel and aggregating their predictions to select the final prediction.
- 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 and using their output as features into a final model.
Correct Answer: B 🗳️
In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
- A. Avoiding the use of APIs for integration with applications
- B. The ability to update or retire models based on performance metrics
- C. Deploying all models simultaneously regardless of use case
- D. Ensuring there is no logging or monitoring of model performance
Correct Answer: B 🗳️
F1-score is particularly useful when:
- A. The data is completely balanced.
- B. You need a balance between precision and recall.
- C. Only the model's accuracy matters.
- D. The dataset size is extremely large.
Correct Answer: B 🗳️
In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?
- A. All machine learning tools are essentially the same, making the selection process trivial.
- B. It is legally required to use only certain tools for specific types of data.
- C. Tools with the most features should always be selected to ensure model complexity.
- D. Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.
Correct Answer: D 🗳️
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