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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Machine Learning Methods | - Unsupervised learning - Supervised learning - Model evaluation and validation |
| Data Preparation and Analysis | - Data cleaning and preprocessing - Exploratory data analysis - Feature engineering basics |
| IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
IBM Watson Data Scientist v1 Sample Questions:
In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?
- A. Because only one type of data source can be used in any deployment
- B. To limit the deployment to only use local file storage
- C. To effectively integrate and manage data from various sources for analysis and model training
- D. To ensure that all data sources are manually processed
Correct Answer: C 🗳️
The first step in performing exploratory data analysis (EDA) typically involves:
- A. Connecting to as many data sources as possible
- B. Choosing a color palette for data visualization
- C. Determining the hypothesis for the analysis
- D. Selecting a random sample of data to analyze
Correct Answer: C 🗳️
Which of the following is true about the AUC measure in the context of classification models?
- A. It indicates the number of false positives.
- B. It represents the degree of separability between classes.
- C. It is less useful when the classes are highly imbalanced.
- D. It measures the model's accuracy using a single threshold.
Correct Answer: B 🗳️
Which analytic technique is NOT typically used to address business requirements?
- A. Decision trees
- B. Clustering
- C. Regression analysis
- D. Proofreading
Correct Answer: D 🗳️
When implementing cross-validation, which of the following is NOT a common approach?
- A. K-Fold Cross-Validation
- B. Using the entire dataset as both the training and the test set in each iteration
- C. Stratified K-Fold Cross-Validation for imbalanced datasets
- D. Leave-One-Out Cross-Validation (LOOCV)
Correct Answer: B 🗳️
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