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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: Prepare the Data	18%	- Clean, transform, and normalize datasets - Handle missing values and outliers - Feature engineering and selection - Use Watson tools for data preparation
Topic 3: Governance and Compliance	5%	- Model governance and lineage tracking - Data security and privacy regulations
Topic 4: Build the Model	20%	- Select appropriate ML algorithms - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS - Compare and select best performing models
Topic 5: Deploy the Solution	10%	- Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson
Topic 6: Evaluate the Model	15%	- Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability
Topic 7: Understand the Business Problem	12%	- Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints
Topic 8: 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 the context of building models, why is it important to select a tool based on algorithm requirements and expertise?

A. All machine learning tools are essentially the same, making the selection process trivial.
B. It is legally required to use only certain tools for specific types of data.
C. Tools with the most features should always be selected to ensure model complexity.
D. Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Question #2

When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?