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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: Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Topic 3: Build the Model | 20% | - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning |
| Topic 4: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 5: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection |
| Topic 6: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 7: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Topic 8: Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
IBM Watson Data Scientist v1 Sample Questions:
1. Automating data processing and model deployment with jobs in Watson Studio helps to:
A) Enhance the reproducibility and efficiency of model deployments
B) Limit the ability to update models based on new data
C) Reduce the scalability of deployed solutions
D) Increase the need for manual intervention in the model lifecycle
2. Which of the following is true regarding cross-validation?
A) It should be avoided as it leads to overfitting.
B) It helps in identifying the model's performance variability across different data splits.
C) It involves training the model on the entire dataset at once.
D) It decreases the variability of the model performance estimation.
3. Which statement best differentiates machine learning from deep learning?
A) Deep learning algorithms require less data to learn.
B) Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
C) Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
D) Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
4. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
A) Extending the timeline of the project indefinitely
B) Maximizing the budget before the product launch
C) Testing hypotheses with the smallest investment of time and resources
D) Waiting for all possible features to be developed before release
5. Which of the following is true about the AUC measure in the context of classification models?
A) It measures the model's accuracy using a single threshold.
B) It represents the degree of separability between classes.
C) It is less useful when the classes are highly imbalanced.
D) It indicates the number of false positives.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B,D | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: B |
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