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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 2: Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Topic 3: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 4: Build the Model | 20% | - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Topic 5: Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Topic 6: Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
| Topic 7: Prepare the Data | 18% | - Use Watson tools for data preparation - Feature engineering and selection - Clean, transform, and normalize datasets - Handle missing values and outliers |
| Topic 8: 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:
In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?
- A. To limit the deployment to only use local file storage
- B. To ensure that all data sources are manually processed
- C. Because only one type of data source can be used in any deployment
- D. To effectively integrate and manage data from various sources for analysis and model training
Correct Answer: D 🗳️
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Extracting the least amount of data for simplicity
- B. Ensuring data quality and consistency throughout the process
- C. Loading data into a single, centralized database for analysis
- D. Transforming data exclusively in cloud environments
Correct Answer: B 🗳️
When determining upskill requirements for a team working on a solution, it is important to consider:
- A. The preferred programming languages of the team
- B. The geographical location of the team members
- C. The existing skill level of the team and the skills required for the project
- D. The budget allocation for the project
Correct Answer: C 🗳️
In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
- A. The ability to update or retire models based on performance metrics
- B. Ensuring there is no logging or monitoring of model performance
- C. Avoiding the use of APIs for integration with applications
- D. Deploying all models simultaneously regardless of use case
Correct Answer: A 🗳️
What is the primary purpose of partitioning data into training and test sets?
- A. To ensure that the model gets exposed to all possible data scenarios during training
- B. To evaluate the model's performance on unseen data
- C. To maximize the accuracy of the model by using all data for training
- D. To increase the computational efficiency of model training
Correct Answer: B 🗳️
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