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Updated: Sep 12, 2026
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IBM C1000-154 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Exam Format: | Multiple Choice, Multiple Response |
| Exam Duration: | 90 minutes |
| Related Certifications: | IBM Applied AI Professional Certificate IBM Data Science Professional Certificate |
| Passing Score: | 63% |
| Real Exam Qty: | 61 |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Available Languages: | English |
| 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 |
IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Monitoring deployed models - Deploying models as REST APIs - Watson Discovery overview |
| Topic 2: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data asset management - AutoAI and automatic model building - Project management and collaboration - Data governance and cataloging |
| Topic 3: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Topic 4: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Model deployment and monitoring - Feature engineering and selection - Model training, evaluation, and optimization |
| Topic 5: Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
IBM Watson Data Scientist v1 Sample Questions:
In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
- A. Testing hypotheses with the smallest investment of time and resources
- B. Maximizing the budget before the product launch
- C. Extending the timeline of the project indefinitely
- D. Waiting for all possible features to be developed before release
Correct Answer: A 🗳️
Which hyperparameter is NOT commonly adjusted in a deep learning model?
- A. Activation function
- B. Number of layers
- C. Learning rate
- D. The color of the model's output
Correct Answer: D 🗳️
What is a key disadvantage of using Grid Search for hyperparameter tuning?
- A. It is unable to handle discrete parameters
- B. It is too quick and may miss out on evaluating some hyperparameters
- C. It requires no prior knowledge of the hyperparameters
- D. It can be computationally expensive and time-consuming due to its exhaustive nature
Correct Answer: D 🗳️
When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
- A. Utilizing pandas to clean and transform data
- B. Ignoring missing values in the dataset
- C. Avoiding the use of visualizations to understand data
- D. Only analyzing datasets with less than 100 rows
Correct Answer: A 🗳️
Cloud Pak for Data's integration with Spark allows users to:
- A. Leverage distributed computing for processing large datasets efficiently
- B. Avoid using any form of data processing or analysis
- C. Perform complex computations on small datasets only
- D. Use Spark exclusively for data visualization purposes
Correct Answer: A 🗳️
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