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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation and Analysis | - Feature engineering basics - Exploratory data analysis - Data cleaning and preprocessing |
| Topic 2: Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Topic 3: IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
| Topic 4: Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
| Topic 5: Machine Learning Methods | - Model evaluation and validation - Unsupervised learning - Supervised learning |
IBM Watson Data Scientist v1 Sample Questions:
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
- A. Modeling
- B. Data Preparation
- C. Business Understanding
- D. Data Understanding
Correct Answer: B 🗳️
Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
- A. Scatter plot
- B. Heat map
- C. Stem-and-leaf plot
- D. Box plot
- E. Histogram
Correct Answer: A,B 🗳️
Which of the following is a common issue identified during the preprocessing of data?
- A. Presence of missing values
- B. Overly detailed documentation
- C. Excessively large file names
- D. Aesthetically unpleasing charts
Correct Answer: A 🗳️
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. Regression
- B. Clustering
- C. Classification
- D. Dimensionality Reduction
Correct Answer: A 🗳️
In the context of model selection, explainability refers to:
- A. How colorful and visually appealing the model's output is.
- B. The complexity of the algorithm used to build the model.
- C. The ease with which humans can understand how the model makes decisions.
- D. The model's ability to operate without any data.
Correct Answer: C 🗳️
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