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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Feature engineering and selection - Model training, evaluation, and optimization - Model deployment and monitoring |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data asset management - AutoAI and automatic model building - Data governance and cataloging |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework |
| Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Watson Assistant integration - Deploying models as REST APIs - Monitoring deployed models |
IBM Watson Data Scientist v1 Sample Questions:
1. When managing data with Cloud Pak for Data Services, what is a common task?
A) Reading data from and writing data to Watson Studio
B) Ignoring data security and compliance requirements
C) Using the same data management approach for all types of data, regardless of size or format
D) Avoiding the automation of data processing tasks
2. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A) pip
B) bash
C) conda
D) sudo
E) vim
3. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) Keras
B) TensorFlow
C) PyTorch
D) Pandas
4. F1-score is particularly useful when:
A) You need a balance between precision and recall.
B) Only the model's accuracy matters.
C) The data is completely balanced.
D) The dataset size is extremely large.
5. Understanding how to use libraries in Python within a deployment environment is essential for:
A) Leveraging specific functionalities for data analysis, manipulation, and model building
B) Increasing the complexity and maintenance cost of the deployed solution
C) Deploying models that are incompatible with the deployment environment
D) Ensuring that all models are developed without any external libraries
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A |
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