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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints - Translate business requirements into data science objectives |
| Topic 2: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 3: Build the Model | 20% | - Select appropriate ML algorithms - Compare and select best performing models - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS |
| Topic 4: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Monitor model performance post-deployment - Ensure scalability and reliability |
| Topic 5: Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio |
| Topic 6: Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| Topic 7: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 8: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Feature engineering and selection - Handle missing values and outliers |
IBM Watson Data Scientist v1 Sample Questions:
1. Which of the following is a common issue identified during the preprocessing of data?
A) Overly detailed documentation
B) Presence of missing values
C) Aesthetically unpleasing charts
D) Excessively large file names
2. Why is it important to create data splits that are reproducible?
A) To guarantee that the model will perform with 100% accuracy on unseen data
B) To allow for larger test sets for more comprehensive testing
C) To use more data for testing than for training
D) To ensure that each model run can be exactly replicated for verification and comparison
3. Understanding how to use libraries in Python within a deployment environment is essential for:
A) Deploying models that are incompatible with the deployment environment
B) Ensuring that all models are developed without any external libraries
C) Increasing the complexity and maintenance cost of the deployed solution
D) Leveraging specific functionalities for data analysis, manipulation, and model building
4. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
A) Recall Operation Curve
B) Random Output Curve
C) Regression Operation Characteristic
D) Receiver Operating Characteristic
5. Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?
A) A company following a strict top-down approach for all decisions
B) A business starting with data collection before understanding the problem
C) A project manager focusing exclusively on deployment
D) A team iterating between different stages as needed based on project feedback
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: D |
