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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis |
| Build the Model | 20% | - Perform hyperparameter tuning - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Evaluate the Model | 15% | - Identify bias and overfitting - Assess classification/regression metrics - Validate model generalizability |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Feature engineering and selection - Handle missing values and outliers |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
IBM Watson Data Scientist v1 Sample Questions:
1. Which hyperparameter is NOT commonly adjusted in a deep learning model?
A) Number of layers
B) Learning rate
C) Activation function
D) The color of the model's output
2. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A) It focuses on maintaining and operating solutions at scale.
B) It involves the rapid building of prototypes to validate ideas.
C) It is primarily concerned with the technical deployment of solutions.
D) It emphasizes understanding user outcomes and business needs.
3. The first step in performing exploratory data analysis (EDA) typically involves:
A) Connecting to as many data sources as possible
B) Choosing a color palette for data visualization
C) Selecting a random sample of data to analyze
D) Determining the hypothesis for the analysis
4. Which of the following is a key feature of Watson Knowledge Catalog (WKC) for identifying appropriate data sources?
A) Real-time messaging
B) Cloud storage optimization
C) Data discovery and categorization
D) Automated code compilation
5. In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?
A) All machine learning tools are essentially the same, making the selection process trivial.
B) It is legally required to use only certain tools for specific types of data.
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.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
