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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 |
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
| Machine Learning Methods | - Unsupervised learning - Supervised learning - Model evaluation and validation |
| Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
IBM Watson Data Scientist v1 Sample Questions:
1. What is data leakage in the context of model training?
A) When data from outside the training dataset is accidentally included in the training process
B) Leakage of sensitive information due to poor data handling practices
C) A situation where the test data is not available
D) Loss of data during the splitting process
2. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
A) Compatibility of the algorithm with the data characteristics and the predictive task.
B) The popularity of the algorithm in recent academic papers.
C) Choosing algorithms that are only based on supervised learning.
D) The algorithm that requires the least amount of data preprocessing.
3. In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
A) It ensures that the model is trained on the maximum amount of data possible
B) It guarantees that the model will perform with 100% accuracy on unseen data
C) It increases the computational complexity without improving model performance
D) It allows for the model to be validated and tested on different subsets of data to check its generalization ability
4. How do you determine which tool to use based on algorithm requirements and expertise?
A) Always use the most complex tool to ensure the model's accuracy.
B) Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
C) Choose the newest tools on the market for the most up-to-date features.
D) Consider the tool's compatibility with the algorithm requirements and the team's expertise.
5. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
A) Order
B) Key
C) Condition
D) Filter
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |
