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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Topic 3: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Feature engineering and selection - Use Watson tools for data preparation - Handle missing values and outliers |
| Topic 4: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 5: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 6: Build the Model | 20% | - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Topic 7: Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Topic 8: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
IBM Watson Data Scientist v1 Sample Questions:
1. What is a benefit of creating data pipelines to automate the model lifecycle?
A) It provides a structured approach to processing, validating, and deploying models
B) Encourages a one-size-fits-all approach to model development
C) It necessitates frequent manual updates and checks
D) Reduces the need for understanding the underlying data
2. 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
3. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A) Grid Search
B) Binary Search
C) Sequential Search
D) Random Search
4. 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 team iterating between different stages as needed based on project feedback
C) A business starting with data collection before understanding the problem
D) A project manager focusing exclusively on deployment
5. 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) Regression Operation Characteristic
B) Receiver Operating Characteristic
C) Recall Operation Curve
D) Random Output Curve
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: B |
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