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IBM C1000-154 Exam Overview:
| Certification Vendor: | IBM |
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Exam Duration: | 90 minutes |
| Passing Score: | 63% |
| Exam Price: | $200 USD |
| Real Exam Qty: | 61 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | IBM Data Science Professional Certificate IBM Applied AI Professional Certificate |
| Exam Format: | Multiple Choice, Multiple Response |
| 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 |
|---|---|---|
| Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Model deployment and monitoring - Feature engineering and selection - Supervised and unsupervised learning concepts |
| Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Deploying models as REST APIs - Watson Assistant integration - Watson Discovery overview |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data asset management - Data governance and cataloging - AutoAI and automatic model building - Project management and collaboration |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Interactive dashboards and reports - Communicating findings to stakeholders |
| Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components - Data collection, preparation, and exploration |
IBM Watson Data Scientist v1 Sample Questions:
1. 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) Continuous monitoring and evaluation of model performance
C) Using the highest number of resources for every model
D) Limiting access to the model to a few select users
2. When managing data with Cloud Pak for Data Services, what is a common task?
A) Using the same data management approach for all types of data, regardless of size or format
B) Reading data from and writing data to Watson Studio
C) Ignoring data security and compliance requirements
D) Avoiding the automation of data processing tasks
3. Why is it important to create data splits that are reproducible?
A) To ensure that each model run can be exactly replicated for verification and comparison
B) To guarantee that the model will perform with 100% accuracy on unseen data
C) To use more data for testing than for training
D) To allow for larger test sets for more comprehensive testing
4. When comparing models to choose the best one, which factor is least likely to be considered?
A) The color scheme of the model's output visualizations
B) The complexity of the model
C) The explainability of the model's predictions
D) The performance of the model on validation data
5. Which of the following is NOT a direct benefit of connecting to data sources using Cloud Pak for Data?
A) Facilitating secure and scalable data connectivity
B) Automatically generating insights without data analysis
C) Streamlining the integration of diverse data sources
D) Enhancing collaboration across data science and IT teams
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
By Atwood
