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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Build the Model | 20% | - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection |
| Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| 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 |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
IBM Watson Data Scientist v1 Sample Questions:
1. 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 project manager focusing exclusively on deployment
C) A business starting with data collection before understanding the problem
D) A team iterating between different stages as needed based on project feedback
2. What is a key disadvantage of using Grid Search for hyperparameter tuning?
A) It is too quick and may miss out on evaluating some hyperparameters
B) It is unable to handle discrete parameters
C) It can be computationally expensive and time-consuming due to its exhaustive nature
D) It requires no prior knowledge of the hyperparameters
3. Which of the following is NOT a direct benefit of connecting to data sources using Cloud Pak for Data?
A) Streamlining the integration of diverse data sources
B) Facilitating secure and scalable data connectivity
C) Enhancing collaboration across data science and IT teams
D) Automatically generating insights without data analysis
4. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A) Binary Search
B) Grid Search
C) Random Search
D) Sequential Search
5. How can data splits be made reproducible in a machine learning experiment?
A) By using a consistent random seed when splitting the data
B) By partitioning the data manually
C) By using a different random seed each time the data is split
D) By splitting the data in a sequential manner without randomization
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
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