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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data governance and cataloging - Data asset management - Project management and collaboration |
| Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration - Deploying models as REST APIs |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Supervised and unsupervised learning concepts - Model deployment and monitoring |
IBM Watson Data Scientist v1 Sample Questions:
1. Understanding how to use libraries in Python within a deployment environment is essential for:
A) Deploying models that are incompatible with the deployment environment
B) Ensuring that all models are developed without any external libraries
C) Increasing the complexity and maintenance cost of the deployed solution
D) Leveraging specific functionalities for data analysis, manipulation, and model building
2. F1-score is particularly useful when:
A) The data is completely balanced.
B) You need a balance between precision and recall.
C) The dataset size is extremely large.
D) Only the model's accuracy matters.
3. When implementing cross-validation, which of the following is NOT a common approach?
A) Using the entire dataset as both the training and the test set in each iteration
B) Leave-One-Out Cross-Validation (LOOCV)
C) Stratified K-Fold Cross-Validation for imbalanced datasets
D) K-Fold Cross-Validation
4. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?
A) Support Vector Machines
B) K-Means Clustering
C) Linear Regression
D) Decision Trees
5. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
A) Recall Operation Curve
B) Random Output Curve
C) Regression Operation Characteristic
D) Receiver Operating Characteristic
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: D |
