- Exam Code: PCAD-31-02
- Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
- Certification Provider: Python Institute
- Corresponding Certification:Python Institute Certification
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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Exploratory Data Analysis | ||
| Topic 2: Data Acquisition and Pre-Processing | 29.2% | |
| Topic 3: Data Visualization and Communication | ||
| Topic 4: SQL and Data Management | ||
| Topic 5: Statistical Methods and Modeling |
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
Why might using a pie chart to represent category proportions be less effective than a bar chart?
- A. Bar charts are limited to one variable only
- B. Human perception is less accurate at judging angles than lengths
- C. Pie charts display individual data points more clearly
- D. Pie charts cannot be created using Seaborn
Correct Answer: B 🗳️
Which technique is considered a primary defense against SQL injection when executing database queries from Python?
- A. Encrypting the database connection
- B. Using parameterized queries
- C. Closing the database cursor after each query
- D. Escaping quotes manually in SQL strings
Correct Answer: B 🗳️
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)
- A. They require labeled training data
- B. They automatically detect outliers
- C. They work only with numerical features
- D. They are useful for regression and classification tasks
Correct Answer: A,D 🗳️
Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)
- A. Sorting columns alphabetically
- B. Defining and enforcing schema constraints
- C. Automatically converting all text to uppercase
- D. Using assertions to enforce domain-specific rules
Correct Answer: B,D 🗳️
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 fillna() with a constant value
- B. Use drop() to remove null rows
- C. Use dropna() with axis=1
- D. Apply interpolate() method
Correct Answer: A 🗳️
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