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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data asset management - AutoAI and automatic model building - Data governance and cataloging |
| Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration - Deploying models as REST APIs |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
IBM Watson Data Scientist v1 Sample Questions:
1. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
A) Compatibility of the algorithm with the data characteristics and the predictive task.
B) Choosing algorithms that are only based on supervised learning.
C) The popularity of the algorithm in recent academic papers.
D) The algorithm that requires the least amount of data preprocessing.
2. Which of the following is a critical first step in understanding a business problem for data science projects?
A) Deploying the model
B) Choosing the visualization tools
C) Defining the project scope
D) Selecting the machine learning algorithm
3. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
A) Evaluation
B) Data Preparation
C) Model Building
D) Business Understanding
4. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A) sudo
B) pip
C) vim
D) bash
E) conda
5. Which method is NOT traditionally used for collecting data?
A) Scraping data from a webpage
B) Using SQL to fetch data from a data warehouse
C) Predicting future trends with machine learning models
D) Using Python APIs for external data
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B,E | Question # 5 Answer: C |
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