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| Section | Weight | Objectives |
|---|
| Data Acquisition and Pre-Processing | 29.2% | |
| SQL and Data Management | | |
| Statistical Methods and Modeling | | |
| Exploratory Data Analysis | | |
| Data Visualization and Communication | | |
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q106-Q111):
NEW QUESTION # 106
What is the primary purpose of using annotations in a data visualization?
- A. To highlight important data points and provide contextual information
- B. To enhance interactivity in static plots
- C. To ensure faster rendering in large datasets
- D. To replace the need for axes
Answer: A
NEW QUESTION # 107
What result will the following Pandas expression return: df['Age'].notnull().all()?
- A. Returns the number of NULL values in 'Age'
- B. Returns True if all values in 'Age' are not NULL
- C. Returns True if all values in 'Age' are NULL
- D. Returns True if at least one value in 'Age' is not NULL
Answer: B
NEW QUESTION # 108
Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?
- A. TypeError
- B. IOError
- C. RuntimeError
- D. ValueError
Answer: D
NEW QUESTION # 109
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?
- A. Use visualizations alone without titles or captions
- B. Include concise textual summaries that highlight the chart's message
- C. Repeat all numerical data in both the chart and text
- D. Avoid summarizing the data verbally
Answer: B
NEW QUESTION # 110
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. Replacing with NULL
- C. Filling with random values
- D. Dropping all columns
Answer: A
NEW QUESTION # 111
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