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| Section | Weight | Objectives |
|---|
| Data Visualization and Communication | | |
| SQL and Data Management | | |
| Statistical Methods and Modeling | | |
| 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 (Q14-Q19):
NEW QUESTION # 14
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?
- A. std()
- B. sum()
- C. mean()
- D. skew()
Answer: D
NEW QUESTION # 15
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. while loop with try block
- B. lambda expression with reduce
- C. filter() function with map
- D. for loop with if condition
Answer: D
NEW QUESTION # 16
Why might using a pie chart to represent category proportions be less effective than a bar chart?
- A. Pie charts cannot be created using Seaborn
- B. Human perception is less accurate at judging angles than lengths
- C. Bar charts are limited to one variable only
- D. Pie charts display individual data points more clearly
Answer: B
NEW QUESTION # 17
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)
- A. XML
- B. JSON
- C. CSV
- D. TSV
Answer: A,B
NEW QUESTION # 18
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?
- A. Use drop() to remove null rows
- B. Use dropna() with axis=1
- C. Apply interpolate() method
- D. Use fillna() with a constant value
Answer: D
NEW QUESTION # 19
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