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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Methods | - Supervised learning - Model evaluation and validation - Unsupervised learning |
| IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
| Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Data Preparation and Analysis | - Exploratory data analysis - Data cleaning and preprocessing - Feature engineering basics |
| Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
1. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
A) Waiting for all possible features to be developed before release
B) Maximizing the budget before the product launch
C) Testing hypotheses with the smallest investment of time and resources
D) Extending the timeline of the project indefinitely
2. In Cognos Analytics, which two features distinguish stories from dashboards?
A) Stories automatically load different filters for different users.
B) Stories can be embedded in websites or documents.
C) Stories are text-based and do not contain visualizations.
D) Stories convey a conclusion.
E) Stories provide a narrative over time.
3. 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) Data Preparation
B) Data Understanding
C) Business Understanding
D) Modeling
4. When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
A) Utilizing pandas to clean and transform data
B) Only analyzing datasets with less than 100 rows
C) Ignoring missing values in the dataset
D) Avoiding the use of visualizations to understand data
5. When helping businesses articulate and define problems, what is an essential first step?
A) Defining key performance indicators (KPIs)
B) Establishing a clear problem statement
C) Selecting the analytical techniques
D) Identifying potential data sources
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D,E | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
