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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Topic 2: Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Deploying models as REST APIs - Monitoring deployed models - Watson Assistant integration |
| Topic 3: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - AutoAI and automatic model building - Data asset management - Project management and collaboration |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework |
| Topic 5: Machine Learning and Model Development | 20-25% | - Model deployment and monitoring - Model training, evaluation, and optimization - Feature engineering and selection - Supervised and unsupervised learning concepts |
1. Which of the following is true regarding cross-validation?
A) It involves training the model on the entire dataset at once.
B) It should be avoided as it leads to overfitting.
C) It decreases the variability of the model performance estimation.
D) It helps in identifying the model's performance variability across different data splits.
2. 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) Receiver Operating Characteristic
B) Recall Operation Curve
C) Random Output Curve
D) Regression Operation Characteristic
3. When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
A) Ignoring missing values in the dataset
B) Avoiding the use of visualizations to understand data
C) Only analyzing datasets with less than 100 rows
D) Utilizing pandas to clean and transform data
4. Which feature is NOT available when managing models with Watson Machine Learning?
A) Version control of deployed models
B) Rollback capabilities for model versions
C) Automatic conversion of all models to deep learning models
D) Real-time performance monitoring
5. What is the primary purpose of hyperparameter tuning in machine learning models?
A) To adjust the model's complexity to improve its performance on unseen data
B) To ensure the model uses all available computational resources
C) To increase the number of features in the dataset automatically
D) To reduce the training time of the model to an absolute minimum
Solutions:
| Question # 1 Answer: C,D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: A |
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