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IBM C1000-154 : IBM Watson Data Scientist v1
Exam Code: C1000-154
Exam Name: IBM Watson Data Scientist v1
Updated: Sep 17, 2026
Q & A: 82 Questions and Answers
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Watson Assistant integration - Monitoring deployed models - Deploying models as REST APIs |
| Topic 2: Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data governance and cataloging - Data asset management - Project management and collaboration |
| Topic 3: Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Visualization best practices - Interactive dashboards and reports |
| Topic 4: Machine Learning and Model Development | 20-25% | - Model deployment and monitoring - Feature engineering and selection - Model training, evaluation, and optimization - Supervised and unsupervised learning concepts |
| Topic 5: Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration |
IBM Watson Data Scientist v1 Sample Questions:
Question #1
When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
- A. Only analyzing datasets with less than 100 rows
- B. Avoiding the use of visualizations to understand data
- C. Utilizing pandas to clean and transform data
- D. Ignoring missing values in the dataset
Correct Answer: C 🗳️
Question #2
How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
- A. Data Preparation
- B. Model Building
- C. Business Understanding
- D. Evaluation
Correct Answer: C 🗳️
Question #3
The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
- A. Receiver Operating Characteristic
- B. Recall Operation Curve
- C. Random Output Curve
- D. Regression Operation Characteristic
Correct Answer: A 🗳️
Question #4
When deploying models in Watson Machine Learning, what is essential for ensuring the models perform as expected in production?
- A. Deployment without any security measures
- B. Limiting access to the model to a few select users
- C. Continuous monitoring and evaluation of model performance
- D. Using the highest number of resources for every model
Correct Answer: C 🗳️
Question #5
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. Using only the majority class for splitting
- B. Random split without considering the target variable
- C. Stratified split
- D. Splitting based on the order of data collection
Correct Answer: C 🗳️
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