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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: SQL and Data Management | ||
| Topic 2: Data Acquisition and Pre-Processing | 29.2% | |
| Topic 3: Statistical Methods and Modeling | ||
| Topic 4: Exploratory Data Analysis | ||
| Topic 5: Data Visualization and Communication |
1. Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?
A) filter() function with map
B) while loop with try block
C) for loop with if condition
D) lambda expression with reduce
2. Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)
A) Use raw code outputs for clarity
B) Add context through narrative or titles
C) Use jargon-rich annotations
D) Focus on trend and summary metrics
3. Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?
A) To ensure the loss function is minimized
B) To reduce the number of iterations during training
C) To optimize the model's training accuracy
D) To evaluate the model's performance on unseen data
4. What distinguishes structured data from unstructured data in the context of data modeling and storage?
A) Unstructured data cannot be stored or analyzed
B) Unstructured data contains only numeric values
C) Structured data cannot be stored in databases
D) Structured data follows a consistent schema or format
5. Which practices are considered part of professional Python scripting standards?
(Choose two)
A) Including in-line comments and docstrings
B) Avoiding use of modules and libraries
C) Avoiding error handling to keep code clean
D) Using consistent indentation and naming conventions
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A,D |
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