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IBM C1000-154 : IBM Watson Data Scientist v1
- Exam Code: C1000-154
- Exam Name: IBM Watson Data Scientist v1
- Updated: Oct 06, 2026
About IBM C1000-154 Exam
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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Preparation and Analysis | - Feature engineering basics - Exploratory data analysis - Data cleaning and preprocessing |
| Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
| Machine Learning Methods | - Unsupervised learning - Model evaluation and validation - Supervised learning |
| IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
IBM Watson Data Scientist v1 Sample Questions:
Question #1
The process of aligning on user intents for a solution involves:
- A. Understanding the business model in depth
- B. Focusing on the data management strategies
- C. Determining the technical feasibility exclusively
- D. Identifying and understanding the needs and goals of end-users
Correct Answer: D 🗳️
Question #2
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?
- A. Stratified split
- B. Splitting based on the order of data collection
- C. Random split without considering the target variable
- D. Using only the majority class for splitting
Correct Answer: A 🗳️
Question #3
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. Using Python APIs for external data
- D. Predicting future trends with machine learning models
Correct Answer: D 🗳️
Question #4
Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?
- A. A company following a strict top-down approach for all decisions
- B. A project manager focusing exclusively on deployment
- C. A business starting with data collection before understanding the problem
- D. A team iterating between different stages as needed based on project feedback
Correct Answer: D 🗳️
Question #5
How can data splits be made reproducible in a machine learning experiment?
- A. By partitioning the data manually
- B. By using a consistent random seed when splitting the data
- C. By using a different random seed each time the data is split
- D. By splitting the data in a sequential manner without randomization
Correct Answer: B 🗳️
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