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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare the Data | 18% | - Handle missing values and outliers - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Feature engineering and selection |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints - Translate business requirements into data science objectives |
| Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Build the Model | 20% | - Select appropriate ML algorithms - Perform hyperparameter tuning - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Deploy models as APIs in Watson - Monitor model performance post-deployment |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| 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 |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
A. Ensuring there is no logging or monitoring of model performance
B. The ability to update or retire models based on performance metrics
C. Avoiding the use of APIs for integration with applications
D. Deploying all models simultaneously regardless of use case
Question 2
Automating data processing and model deployment with jobs in Watson Studio helps to:
A. Reduce the scalability of deployed solutions
B. Increase the need for manual intervention in the model lifecycle
C. Limit the ability to update models based on new data
D. Enhance the reproducibility and efficiency of model deployments
Question 3
How can data splits be made reproducible in a machine learning experiment?
A. By partitioning the data manually
B. By using a consistent random seed when splitting the data
C. By using a different random seed each time the data is split
D. By splitting the data in a sequential manner without randomization
Question 4
In the context of assessing data quality in Watson Knowledge Catalog (WKC) and Cloud Pak for Data (CPD), what is a primary focus?
A. Analyzing completeness, consistency, and accuracy of data
B. Enhancing the graphical user interface
C. Increasing the volume of data collected
D. Focusing on the data's color scheme
Question 5
Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A. TensorFlow
B. Pandas
C. Keras
D. PyTorch
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: B |
