IBM : C1000-154 Questions & Answers
Last Updated: Sep 12, 2026
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration - IBM Watson ecosystem and components |
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data governance and cataloging - AutoAI and automatic model building - Data asset management |
| Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Watson Discovery overview - Watson Assistant integration - Monitoring deployed models |
IBM Watson Data Scientist v1 Sample Questions:
Which method is NOT traditionally used for collecting data?
- A. Scraping data from a webpage
- B. Using Python APIs for external data
- C. Using SQL to fetch data from a data warehouse
- D. Predicting future trends with machine learning models
When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
- A. The popularity of the algorithm in recent academic papers.
- B. Choosing algorithms that are only based on supervised learning.
- C. Compatibility of the algorithm with the data characteristics and the predictive task.
- D. The algorithm that requires the least amount of data preprocessing.
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. Classification
- B. Regression
- C. Clustering
- D. Dimensionality Reduction
The first step in performing exploratory data analysis (EDA) typically involves:
- A. Connecting to as many data sources as possible
- B. Choosing a color palette for data visualization
- C. Determining the hypothesis for the analysis
- D. Selecting a random sample of data to analyze
Key metrics for a solution should be defined based on:
- A. The number of available data scientists
- B. The specific objectives and desired outcomes of the project
- C. The most recent technological trends
- D. The personal preferences of the project stakeholders
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