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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Prepare the Data | 18% | - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection - Clean, transform, and normalize datasets |
| Build the Model | 20% | - Compare and select best performing models - Select appropriate ML algorithms - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) |
IBM Watson Data Scientist v1 Sample Questions:
When implementing cross-validation, which of the following is NOT a common approach?
- A. K-Fold Cross-Validation
- B. Leave-One-Out Cross-Validation (LOOCV)
- C. Stratified K-Fold Cross-Validation for imbalanced datasets
- D. Using the entire dataset as both the training and the test set in each iteration
Correct Answer: D 🗳️
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Transforming data exclusively in cloud environments
- B. Extracting the least amount of data for simplicity
- C. Ensuring data quality and consistency throughout the process
- D. Loading data into a single, centralized database for analysis
Correct Answer: C 🗳️
Which of the following is true regarding cross-validation?
- A. It helps in identifying the model's performance variability across different data splits.
- B. It decreases the variability of the model performance estimation.
- C. It should be avoided as it leads to overfitting.
- D. It involves training the model on the entire dataset at once.
Correct Answer: A,B 🗳️
In the context of model selection, explainability refers to:
- A. The complexity of the algorithm used to build the model.
- B. The model's ability to operate without any data.
- C. The ease with which humans can understand how the model makes decisions.
- D. How colorful and visually appealing the model's output is.
Correct Answer: C 🗳️
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?
- A. Splitting based on the order of data collection
- B. Using only the majority class for splitting
- C. Random split without considering the target variable
- D. Stratified split
Correct Answer: D 🗳️
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