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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Topic 2: Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Deploying models as REST APIs - Watson Discovery overview - Monitoring deployed models |
| Topic 3: Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - AutoAI and automatic model building - Data governance and cataloging - Data asset management |
| Topic 4: Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Feature engineering and selection - Supervised and unsupervised learning concepts - Model deployment and monitoring |
| Topic 5: 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 determining upskill requirements for a team working on a solution, it is important to consider:
A) The budget allocation for the project
B) The existing skill level of the team and the skills required for the project
C) The geographical location of the team members
D) The preferred programming languages of the team
2. Which analytic technique is NOT typically used to address business requirements?
A) Proofreading
B) Regression analysis
C) Clustering
D) Decision trees
3. The first step in performing exploratory data analysis (EDA) typically involves:
A) Connecting to as many data sources as possible
B) Selecting a random sample of data to analyze
C) Choosing a color palette for data visualization
D) Determining the hypothesis for the analysis
4. What is data leakage in the context of model training?
A) A situation where the test data is not available
B) Leakage of sensitive information due to poor data handling practices
C) When data from outside the training dataset is accidentally included in the training process
D) Loss of data during the splitting process
5. Which hyperparameter is NOT commonly adjusted in a deep learning model?
A) Number of layers
B) Learning rate
C) The color of the model's output
D) Activation function
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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