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| Section | Weight | Objectives |
|---|
| SQL and Data Management | | |
| Statistical Methods and Modeling | | |
| Data Visualization and Communication | | |
| Exploratory Data Analysis | | |
| Data Acquisition and Pre-Processing | 29.2% | |
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q80-Q85):
NEW QUESTION # 80
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?
- A. Seaborn automatically saves plots as image files
- B. Seaborn works exclusively with NumPy arrays
- C. Seaborn provides high-level abstractions for attractive and informative graphics
- D. Seaborn requires no data preprocessing for missing values
Answer: C
NEW QUESTION # 81
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)
- A. Adding descriptive axis labels
- B. Customizing tick labels
- C. Disabling grid lines in all cases
- D. Avoiding color entirely
Answer: A,B
NEW QUESTION # 82
Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?
- A. load_dataset()
- B. read_csv(chunksize=...)
- C. import_csv()
- D. fetch_chunks()
Answer: B
NEW QUESTION # 83
Which operations are valid when working with Pandas Series?
(Choose two)
- A. Arithmetic vector operations
- B. Merging by index using merge()
- C. Applying NumPy universal functions
- D. Using .columns to rename
Answer: A,C
NEW QUESTION # 84
What is a best practice when organizing data in a spreadsheet for analysis?
- A. Apply conditional formatting to entire rows only
- B. Use consistent column headers across all sheets
- C. Merge cells to reduce redundancy
- D. Place charts in the middle of data tables
Answer: B
NEW QUESTION # 85
......
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