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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Topic 2: Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Topic 3: Data Preparation and Analysis | - Feature engineering basics - Exploratory data analysis - Data cleaning and preprocessing |
| Topic 4: IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
| Topic 5: Machine Learning Methods | - Unsupervised learning - Supervised learning - Model evaluation and validation |
IBM Watson Data Scientist v1 Sample Questions:
What is data leakage in the context of model training?
- A. Loss of data during the splitting process
- B. When data from outside the training dataset is accidentally included in the training process
- C. A situation where the test data is not available
- D. Leakage of sensitive information due to poor data handling practices
Correct Answer: B 🗳️
Given the Confusion matrix below, which is the formula for specificity?
- A. TP/(FN + TP)
- B. TP/(FP + TP)
- C. (TP + TN)/(FN + FP + TN + TP)
- D. TN/(TN + FP)
Correct Answer: D 🗳️
When comparing models to choose the best one, which factor is least likely to be considered?
- A. The performance of the model on validation data
- B. The color scheme of the model's output visualizations
- C. The complexity of the model
- D. The explainability of the model's predictions
Correct Answer: B 🗳️
In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?
- A. To ensure that all data sources are manually processed
- B. Because only one type of data source can be used in any deployment
- C. To effectively integrate and manage data from various sources for analysis and model training
- D. To limit the deployment to only use local file storage
Correct Answer: C 🗳️
When anticipating additional data sources that might be relevant, what is a crucial factor to consider?
- A. The data source's popularity on social media
- B. The relevance of the data source to the business problem
- C. The graphical interface of the data source
- D. The color scheme of the data visualization
Correct Answer: B 🗳️
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