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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Handle missing values and outliers - Feature engineering and selection |
| 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 |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| 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 selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
A) Choosing algorithms that are only based on supervised learning.
B) Compatibility of the algorithm with the data characteristics and the predictive task.
C) The algorithm that requires the least amount of data preprocessing.
D) The popularity of the algorithm in recent academic papers.
2. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A) Choosing the most aesthetically pleasing user interface
B) Selecting the programming language with the least number of keywords
C) Determining the computational power and memory requirements for the deployed solution
D) Focusing exclusively on the cost of storage
3. When comparing models to choose the best one, which factor is least likely to be considered?
A) The explainability of the model's predictions
B) The complexity of the model
C) The color scheme of the model's output visualizations
D) The performance of the model on validation data
4. In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?
A) It is legally required to use only certain tools for specific types of data.
B) All machine learning tools are essentially the same, making the selection process trivial.
C) Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.
D) Tools with the most features should always be selected to ensure model complexity.
5. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) PyTorch
B) TensorFlow
C) Keras
D) Pandas
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
