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| Section | Weight | Objectives |
|---|
| Data Acquisition and Pre-Processing | 29.2% | |
| Statistical Methods and Modeling | | |
| Exploratory Data Analysis | | |
| SQL and Data Management | | |
| Data Visualization and Communication | | |
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q28-Q33):
NEW QUESTION # 28
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?
- A. matplotlib
- B. NumPy
- C. pandas
- D. seaborn
Answer: B
NEW QUESTION # 29
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)
- A. DELETE
- B. SELECT
- C. UPDATE
- D. INSERT
Answer: C,D
NEW QUESTION # 30
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)
- A. Allows the use of lambda for procedural logic
- B. Enables procedural programming only through decorators
- C. Encourages code reuse through inheritance and modularity
- D. Supports clearer abstraction by modeling real-world entities
Answer: C,D
NEW QUESTION # 31
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)
- A. Limit database user permissions to only necessary operations
- B. Construct queries using f-strings for readability
- C. Store credentials securely using environment variables
- D. Sanitize input by replacing dangerous characters with asterisks
Answer: A,C
NEW QUESTION # 32
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?
- A. Basic descriptive statistics such as count, mean, and quartiles
- B. A normalized version of the data column
- C. A histogram of the data distribution
- D. Only the standard deviation of the data
Answer: A
NEW QUESTION # 33
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