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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
1. Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)
A) Converting all values to integers
B) Removing rows with invalid labels
C) Normalizing using min-max scaling
D) Replacing inconsistent labels with a standardized value
2. Why is it important to adjust data presentations based on the audience's background?
A) To avoid using charts altogether
B) To include as many technical terms as possible
C) To simplify all metrics to percentages only
D) To ensure the data is understood and supports actionable insights
3. Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)
A) Using a mapping function to convert SQL types into Python objects
B) Ignoring data types and treating everything as strings
C) Automatically casting Python variables to SQL column types without validation
D) Defining column types in Python using sqlite3.register_converter()
4. Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)
A) Open Excel files using matplotlib
B) Access data using requests.get() and parse it with .json()
C) Use the requests module to retrieve JSON responses
D) Load CSV directly with re.compile()
5. Which method is commonly used to handle missing numerical data in a dataset?
A) Replacing with text labels
B) Filling with the mean or median
C) Multiplying all missing values by zero
D) Dropping all columns
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: D | Question # 3 Answer: A,D | Question # 4 Answer: B,C | Question # 5 Answer: B |
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