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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Data Management
Topic 2: Data Visualization and Communication
Topic 3: Data Acquisition and Pre-Processing	29.2%
Topic 4: Statistical Methods and Modeling
Topic 5: Exploratory Data Analysis

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q19-Q24):

NEW QUESTION # 19
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Replace NULL with zero before importing
B. Use == None directly in SQL queries
C. Use Python's None and SQL's IS NULL clause
D. Convert all NULL to string before comparison

Answer: C

NEW QUESTION # 20
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A. Filling with random values
B. Dropping all columns
C. Replacing with NULL
D. Imputation using mean or median

Answer: D

NEW QUESTION # 21
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

A. DataFrame.nunique()
B. DataFrame.remove_duplicates()
C. DataFrame.unique()
D. DataFrame.drop_duplicates()

Answer: B

NEW QUESTION # 22
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

A. Use fillna() with a constant value
B. Use dropna() with axis=1
C. Apply interpolate() method
D. Use drop() to remove null rows

Answer: A

NEW QUESTION # 23
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Avoiding color entirely
B. Adding descriptive axis labels
C. Customizing tick labels
D. Disabling grid lines in all cases

Answer: B,C

NEW QUESTION # 24
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