Updated: Sep 02, 2026
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| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Response |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Passing Score: | 63% |
| Related Certifications: | IBM Data Science Professional Certificate IBM Applied AI Professional Certificate |
| Real Exam Qty: | 61 |
| Sample Questions: | IBM C1000-154 Sample Questions |
| Exam Way: | Online proctored or at authorized testing center |
| Pre Condition: | Recommended: IBM Data Science Professional Certificate or equivalent experience with IBM Cloud and data science tools |
| Official Syllabus URL: | https://www.ibm.com/certify/exam/zse1f0-1000-154 |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration |
| Topic 2: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - Data asset management - AutoAI and automatic model building - Project management and collaboration |
| Topic 3: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Feature engineering and selection - Model deployment and monitoring - Model training, evaluation, and optimization |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration |
| Topic 5: Data Visualization and Storytelling | 15-20% | - Visualization best practices - Interactive dashboards and reports - Communicating findings to stakeholders |
Question 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. The popularity of the algorithm in recent academic papers.
C. The algorithm that requires the least amount of data preprocessing.
D. Choosing algorithms that are only based on supervised learning.
Question 2
A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).
Which metric is a good indicator of the performance of the virtual assistant?
A. Measure escalated calls using A/B testing
B. The Area Under the Curve (AUC)
C. The Root Mean Squared Error (RMSE) of words
D. The F1 score of predicted intents in the Analytics tab
Question 3
What is the primary purpose of hyperparameter tuning in machine learning models?
A. To increase the number of features in the dataset automatically
B. To adjust the model's complexity to improve its performance on unseen data
C. To ensure the model uses all available computational resources
D. To reduce the training time of the model to an absolute minimum
Question 4
How do you determine which tool to use based on algorithm requirements and expertise?
A. Always use the most complex tool to ensure the model's accuracy.
B. Consider the tool's compatibility with the algorithm requirements and the team's expertise.
C. Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
D. Choose the newest tools on the market for the most up-to-date features.
Question 5
What does the term "complexity" in model comparison refer to?
A. The size of the dataset the model can handle
B. The aesthetic appeal of the model's graphical representations
C. The number of hyperparameters that need to be tuned
D. The amount of computational resources required for training and inference
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: D |
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