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| Section | Weight | Objectives |
|---|
| SQL and Data Management | | |
| Data Visualization and Communication | | |
| Exploratory Data Analysis | | |
| Data Acquisition and Pre-Processing | 29.2% | |
| Statistical Methods and Modeling | | |
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q53-Q58):
NEW QUESTION # 53
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)
- A. Encourages code reuse through inheritance and modularity
- B. Enables procedural programming only through decorators
- C. Allows the use of lambda for procedural logic
- D. Supports clearer abstraction by modeling real-world entities
Answer: A,D
NEW QUESTION # 54
Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)
- A. Always convert all columns to strings
- B. Use .info() to understand dataset structure
- C. Replace missing values with hardcoded zeros without analysis
- D. Use .describe() for summary statistics
Answer: B,D
NEW QUESTION # 55
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?
- A. plt.xlabel()
- B. plt.annotate()
- C. plt.legend()
- D. plt.title()
Answer: B
NEW QUESTION # 56
What is the primary role of the pandas library in the Python data science ecosystem?
- A. Manipulating labeled data structures
- B. Visualizing large datasets
- C. Reducing execution time of compiled code
- D. Performing statistical tests
Answer: A
NEW QUESTION # 57
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?
- A. ORDER BY
- B. GROUP BY
- C. HAVING
- D. WHERE
Answer: D
NEW QUESTION # 58
......
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