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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation and Analysis | - Data cleaning and preprocessing - Feature engineering basics - Exploratory data analysis |
| Topic 2: Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Topic 3: Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
| Topic 4: Machine Learning Methods | - Supervised learning - Unsupervised learning - Model evaluation and validation |
| Topic 5: IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
IBM Watson Data Scientist v1 Sample Questions:
1. In classification models, which of the following metrics is NOT directly derived from the confusion matrix?
A) Precision
B) F1-score
C) Recall
D) Mean Absolute Error (MAE)
2. 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) Classification
B) Dimensionality Reduction
C) Clustering
D) Regression
3. Assessing the feasibility of a solution(s) often requires evaluating:
A) Preferred communication channels of the project manager
B) Market competition only
C) The color scheme of the user interface
D) Technical feasibility, cost, and time constraints
4. What is a key disadvantage of using Grid Search for hyperparameter tuning?
A) It is too quick and may miss out on evaluating some hyperparameters
B) It can be computationally expensive and time-consuming due to its exhaustive nature
C) It is unable to handle discrete parameters
D) It requires no prior knowledge of the hyperparameters
5. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
A) Precision
B) F1-score
C) Recall
D) Accuracy
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: B |
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