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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 2: Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints - Translate business requirements into data science objectives |
| Topic 3: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 4: Prepare the Data | 18% | - Use Watson tools for data preparation - Handle missing values and outliers - Clean, transform, and normalize datasets - Feature engineering and selection |
| Topic 5: Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Topic 6: Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis |
| Topic 7: Build the Model | 20% | - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
| Topic 8: 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:
Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?
- A. Clustering
- B. Dimensionality Reduction
- C. Classification
- D. Regression
Correct Answer: D 🗳️
In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
- A. Extending the timeline of the project indefinitely
- B. Testing hypotheses with the smallest investment of time and resources
- C. Waiting for all possible features to be developed before release
- D. Maximizing the budget before the product launch
Correct Answer: B 🗳️
Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
- A. Accuracy
- B. F1-score
- C. Recall
- D. Precision
Correct Answer: B 🗳️
What is the key difference between batch processing and streaming in data processing?
- A. Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
- B. Batch processing involves real-time data processing, whereas streaming does not process data
- C. Streaming is suitable for large, historical datasets, whereas batch processing is for real-time data analysis
- D. Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
Correct Answer: D 🗳️
Understanding how to use libraries in Python within a deployment environment is essential for:
- A. Increasing the complexity and maintenance cost of the deployed solution
- B. Deploying models that are incompatible with the deployment environment
- C. Ensuring that all models are developed without any external libraries
- D. Leveraging specific functionalities for data analysis, manipulation, and model building
Correct Answer: D 🗳️
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