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| Section | Weight | Objectives |
|---|
| SQL and Data Management | | |
| Exploratory Data Analysis | | |
| Statistical Methods and Modeling | | |
| Data Acquisition and Pre-Processing | 29.2% | |
| Data Visualization and Communication | | |
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q26-Q31):
NEW QUESTION # 26
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?
- A. Imputation using mean or median
- B. Filling with random values
- C. Replacing with NULL
- D. Dropping all columns
Answer: A
NEW QUESTION # 27
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?
- A. Data integration
- B. Data binning
- C. Data normalization
- D. Data warehousing
Answer: A
NEW QUESTION # 28
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?
- A. Linear regression
- B. Logistic regression
- C. Polynomial regression
- D. Decision tree regression
Answer: B
NEW QUESTION # 29
Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)
- A. Always install packages system-wide for accessibility
- B. Use virtual environments to isolate project dependencies
- C. Use PIP to install and uninstall third-party libraries as needed
- D. Regularly update all packages using pip update all
Answer: B,C
NEW QUESTION # 30
What is the best approach when comparing SQL NULL values with Python data during conditional checks?
- A. Use Python's None and SQL's IS NULL clause
- B. Replace NULL with zero before importing
- C. Use == None directly in SQL queries
- D. Convert all NULL to string before comparison
Answer: A
NEW QUESTION # 31
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