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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Prepare the Data | 18% | - Use Watson tools for data preparation - Handle missing values and outliers - Feature engineering and selection - Clean, transform, and normalize datasets |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Build the Model | 20% | - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio |
IBM Watson Data Scientist v1 Sample Questions:
1. A model's performance is not solely dependent on its accuracy but also on:
A) Metrics like precision, recall, and F1 score
B) The choice of programming language
C) The number of features selected
D) The color of the visualization charts
2. Given the Confusion matrix below, which is the formula for specificity?
A) TP/(FN + TP)
B) (TP + TN)/(FN + FP + TN + TP)
C) TN/(TN + FP)
D) TP/(FP + TP)
3. What is the primary purpose of hyperparameter tuning in machine learning models?
A) To increase the number of features in the dataset automatically
B) To reduce the training time of the model to an absolute minimum
C) To ensure the model uses all available computational resources
D) To adjust the model's complexity to improve its performance on unseen data
4. In Cognos Analytics, which two features distinguish stories from dashboards?
A) Stories are text-based and do not contain visualizations.
B) Stories provide a narrative over time.
C) Stories can be embedded in websites or documents.
D) Stories convey a conclusion.
E) Stories automatically load different filters for different users.
5. Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?
A) A project manager focusing exclusively on deployment
B) A business starting with data collection before understanding the problem
C) A company following a strict top-down approach for all decisions
D) A team iterating between different stages as needed based on project feedback
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B,D | Question # 5 Answer: D |
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