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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations |
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Perform hyperparameter tuning - Compare and select best performing models |
| Prepare the Data | 18% | - Handle missing values and outliers - Clean, transform, and normalize datasets - Feature engineering and selection - Use Watson tools for data preparation |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Deploy models as APIs in Watson - Monitor model performance post-deployment |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints - Translate business requirements into data science objectives |
IBM Watson Data Scientist v1 Sample Questions:
1. When would you use AutoAI to select algorithms for your model?
A) Only when working with small datasets due to processing limitations.
B) When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
C) When the model requirements are extremely specific and no standard algorithm fits.
D) When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
2. Given the Confusion matrix below, which is the formula for specificity?
A) TN/(TN + FP)
B) (TP + TN)/(FN + FP + TN + TP)
C) TP/(FN + TP)
D) TP/(FP + TP)
3. What is a critical consideration when selecting the right model class for a given problem?
A) The availability of high-performance computing resources.
B) The model's ability to produce results quickly, regardless of accuracy.
C) The theoretical complexity of the model, with more complex models always being preferred.
D) The nature of the problem (e.g., classification, regression) and the characteristics of the data.
4. Which of the following is true regarding cross-validation?
A) It involves training the model on the entire dataset at once.
B) It helps in identifying the model's performance variability across different data splits.
C) It should be avoided as it leads to overfitting.
D) It decreases the variability of the model performance estimation.
5. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A) vim
B) bash
C) conda
D) sudo
E) pip
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: B,D | Question # 5 Answer: C,E |
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