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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Preparation and Analysis | - Feature engineering basics - Data cleaning and preprocessing - Exploratory data analysis |
| IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
| Machine Learning Methods | - Model evaluation and validation - Supervised learning - Unsupervised learning |
| Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
IBM Watson Data Scientist v1 Sample Questions:
How do you determine which tool to use based on algorithm requirements and expertise?
- A. Consider the tool's compatibility with the algorithm requirements and the team's expertise.
- B. Always use the most complex tool to ensure the model's accuracy.
- C. Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
- D. Choose the newest tools on the market for the most up-to-date features.
Correct Answer: A 🗳️
Which of the following is true regarding cross-validation?
- A. It should be avoided as it leads to overfitting.
- B. It helps in identifying the model's performance variability across different data splits.
- C. It decreases the variability of the model performance estimation.
- D. It involves training the model on the entire dataset at once.
Correct Answer: B,C 🗳️
Why is it important to create data splits that are reproducible?
- A. To allow for larger test sets for more comprehensive testing
- B. To guarantee that the model will perform with 100% accuracy on unseen data
- C. To ensure that each model run can be exactly replicated for verification and comparison
- D. To use more data for testing than for training
Correct Answer: C 🗳️
Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?
- A. Dimensionality Reduction
- B. Clustering
- C. Classification
- D. Regression
Correct Answer: D 🗳️
When comparing models to choose the best one, which factor is least likely to be considered?
- A. The color scheme of the model's output visualizations
- B. The performance of the model on validation data
- C. The explainability of the model's predictions
- D. The complexity of the model
Correct Answer: A 🗳️
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