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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 2: 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 |
| Topic 3: Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Topic 4: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 5: Deploy the Solution | 10% | - Ensure scalability and reliability - Deploy models as APIs in Watson - Monitor model performance post-deployment |
| Topic 6: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Feature engineering and selection - Handle missing values and outliers - Use Watson tools for data preparation |
| Topic 7: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Topic 8: Build the Model | 20% | - Select appropriate ML algorithms - Perform hyperparameter tuning - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
IBM Watson Data Scientist v1 Sample Questions:
When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
- A. Utilizing pandas to clean and transform data
- B. Avoiding the use of visualizations to understand data
- C. Only analyzing datasets with less than 100 rows
- D. Ignoring missing values in the dataset
Correct Answer: A 🗳️
In the context of assessing data quality in Watson Knowledge Catalog (WKC) and Cloud Pak for Data (CPD), what is a primary focus?
- A. Focusing on the data's color scheme
- B. Enhancing the graphical user interface
- C. Increasing the volume of data collected
- D. Analyzing completeness, consistency, and accuracy of data
Correct Answer: D 🗳️
In the context of model selection, explainability refers to:
- A. The complexity of the algorithm used to build the model.
- B. The model's ability to operate without any data.
- C. How colorful and visually appealing the model's output is.
- D. The ease with which humans can understand how the model makes decisions.
Correct Answer: D 🗳️
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
- A. Data Understanding
- B. Business Understanding
- C. Modeling
- D. Data Preparation
Correct Answer: D 🗳️
Which of the following is a key feature of Watson Knowledge Catalog (WKC) for identifying appropriate data sources?
- A. Real-time messaging
- B. Data discovery and categorization
- C. Cloud storage optimization
- D. Automated code compilation
Correct Answer: B 🗳️
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