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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Watson Discovery overview - Monitoring deployed models - Deploying models as REST APIs |
| Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration |
| Machine Learning and Model Development | 20-25% | - Model deployment and monitoring - Model training, evaluation, and optimization - Feature engineering and selection - Supervised and unsupervised learning concepts |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - AutoAI and automatic model building - Project management and collaboration - Data asset management |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
IBM Watson Data Scientist v1 Sample Questions:
1. Which method is NOT traditionally used for collecting data?
A) Using SQL to fetch data from a data warehouse
B) Scraping data from a webpage
C) Predicting future trends with machine learning models
D) Using Python APIs for external data
2. Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?
A) Dimensionality Reduction
B) Clustering
C) Classification
D) Regression
3. Which analytic technique is NOT typically used to address business requirements?
A) Decision trees
B) Regression analysis
C) Clustering
D) Proofreading
4. The first step in performing exploratory data analysis (EDA) typically involves:
A) Choosing a color palette for data visualization
B) Connecting to as many data sources as possible
C) Determining the hypothesis for the analysis
D) Selecting a random sample of data to analyze
5. When comparing models to choose the best one, which factor is least likely to be considered?
A) The complexity of the model
B) The explainability of the model's predictions
C) The color scheme of the model's output visualizations
D) The performance of the model on validation data
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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