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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Watson Assistant integration - Watson Discovery overview - Deploying models as REST APIs |
| Topic 2: 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 3: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization - Model deployment and monitoring - Feature engineering and selection |
| Topic 4: 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 5: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
IBM Watson Data Scientist v1 Sample Questions:
1. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
A) Maximizing the budget before the product launch
B) Extending the timeline of the project indefinitely
C) Waiting for all possible features to be developed before release
D) Testing hypotheses with the smallest investment of time and resources
2. When managing data with Cloud Pak for Data Services, what is a common task?
A) Avoiding the automation of data processing tasks
B) Using the same data management approach for all types of data, regardless of size or format
C) Ignoring data security and compliance requirements
D) Reading data from and writing data to Watson Studio
3. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A) It is primarily concerned with the technical deployment of solutions.
B) It focuses on maintaining and operating solutions at scale.
C) It emphasizes understanding user outcomes and business needs.
D) It involves the rapid building of prototypes to validate ideas.
4. Which method is NOT traditionally used for collecting data?
A) Using Python APIs for external data
B) Scraping data from a webpage
C) Predicting future trends with machine learning models
D) Using SQL to fetch data from a data warehouse
5. Which analytic technique is NOT typically used to address business requirements?
A) Clustering
B) Proofreading
C) Decision trees
D) Regression analysis
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: B |
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