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IBM C1000-154 (IBM Watson Data Scientist v1) Certification Exam is designed to test the knowledge and skills of data scientists in using IBM Watson to analyze and interpret large amounts of data. C1000-154 exam covers a broad range of topics, including data analysis, data visualization, machine learning, and predictive modeling. Candidates who pass the exam will have demonstrated their ability to use IBM Watson to perform advanced data analysis and modeling tasks, making them highly valuable to organizations that rely on data-driven decision-making.
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IBM C1000-154 certification exam covers a wide range of topics related to IBM Watson technologies. Some of the key topics covered in the exam include data preparation, data visualization, machine learning algorithms, model selection, and deployment of predictive models. C1000-154 exam also tests the candidates' knowledge of Python programming language and its libraries such as Pandas, NumPy, and Scikit-learn.
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IBM C1000-154 exam consists of 60 multiple-choice questions that must be answered within a time limit of 90 minutes. The questions are designed to test the candidate's knowledge and understanding of various data science concepts and technologies. To pass the exam, candidates must achieve a minimum score of 65%.
| Section | Weight | Objectives |
|---|---|---|
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Prepare the Data | 18% | - Use Watson tools for data preparation - Clean, transform, and normalize datasets - Feature engineering and selection - Handle missing values and outliers |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Build the Model | 20% | - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
| 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 |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
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