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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Deploying models as REST APIs - Watson Discovery overview - Watson Assistant integration |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data governance and cataloging - AutoAI and automatic model building - Data asset management |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Supervised and unsupervised learning concepts - Model deployment and monitoring |
| Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework |
IBM Watson Data Scientist v1 Sample Questions:
1. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) Keras
B) PyTorch
C) TensorFlow
D) Pandas
2. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A) conda
B) pip
C) vim
D) sudo
E) bash
3. The first step in performing exploratory data analysis (EDA) typically involves:
A) Determining the hypothesis for the analysis
B) Connecting to as many data sources as possible
C) Selecting a random sample of data to analyze
D) Choosing a color palette for data visualization
4. In the context of model selection, explainability refers to:
A) How colorful and visually appealing the model's output is.
B) The model's ability to operate without any data.
C) The ease with which humans can understand how the model makes decisions.
D) The complexity of the algorithm used to build the model.
5. Which statistical method reduces the number of attributes by lumping highly correlated attributes together?
A) Binning
B) Long Short Term Memory Network (LSTM)
C) Synthetic Minority Over-sampling Technique (SMOTE)
D) Principal Component Analysis (PCA)
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A,B | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: D |
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