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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Model | 15% | - Identify bias and overfitting - Assess classification/regression metrics - Validate model generalizability |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection |
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Compare and select best performing models - Perform hyperparameter tuning |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio |
| Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
IBM Watson Data Scientist v1 Sample Questions:
1. Assessing the feasibility of a solution(s) often requires evaluating:
A) Technical feasibility, cost, and time constraints
B) Preferred communication channels of the project manager
C) Market competition only
D) The color scheme of the user interface
2. Which of the following is true about the AUC measure in the context of classification models?
A) It indicates the number of false positives.
B) It represents the degree of separability between classes.
C) It measures the model's accuracy using a single threshold.
D) It is less useful when the classes are highly imbalanced.
3. When would you use AutoAI to select algorithms for your model?
A) When the model requirements are extremely specific and no standard algorithm fits.
B) When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
C) Only when working with small datasets due to processing limitations.
D) When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
4. What is a key advantage of using supervised learning techniques over unsupervised learning techniques?
A) Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
B) Supervised learning can work without any labeled data.
C) Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
D) Supervised learning algorithms can automatically label data.
5. Which of the following best describes when to use deep learning over traditional machine learning algorithms?
A) When the dataset is small and easily interpretable.
B) When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
C) When computational resources are limited and model interpretability is not a concern.
D) For simple tasks that require straightforward predictive modeling.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |
