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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Watson Discovery overview - Watson Assistant integration - Monitoring deployed models |
| Topic 2: 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 3: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration - IBM Watson ecosystem and components |
| Topic 4: Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Topic 5: Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data governance and cataloging - Project management and collaboration - Data asset management |
IBM Watson Data Scientist v1 Sample Questions:
Understanding how to use libraries in Python within a deployment environment is essential for:
- A. Increasing the complexity and maintenance cost of the deployed solution
- B. Deploying models that are incompatible with the deployment environment
- C. Leveraging specific functionalities for data analysis, manipulation, and model building
- D. Ensuring that all models are developed without any external libraries
Correct Answer: C 🗳️
When helping businesses articulate and define problems, what is an essential first step?
- A. Identifying potential data sources
- B. Establishing a clear problem statement
- C. Selecting the analytical techniques
- D. Defining key performance indicators (KPIs)
Correct Answer: B 🗳️
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
- A. Data Understanding
- B. Business Understanding
- C. Modeling
- D. Data Preparation
Correct Answer: D 🗳️
Which statistical method reduces the number of attributes by lumping highly correlated attributes together?
- A. Long Short Term Memory Network (LSTM)
- B. Binning
- C. Synthetic Minority Over-sampling Technique (SMOTE)
- D. Principal Component Analysis (PCA)
Correct Answer: D 🗳️
A model's performance is not solely dependent on its accuracy but also on:
- A. The color of the visualization charts
- B. Metrics like precision, recall, and F1 score
- C. The number of features selected
- D. The choice of programming language
Correct Answer: B 🗳️
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