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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q103-Q108):

NEW QUESTION # 103
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 drop() to remove null rows
B. Use dropna() with axis=1
C. Apply interpolate() method
D. Use fillna() with a constant value

Answer: D

NEW QUESTION # 104
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

A. match-case statements
B. while loops
C. decorators
D. list comprehensions

Answer: B,D

NEW QUESTION # 105
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.legend()
B. plt.xlabel()
C. plt.annotate()
D. plt.title()

Answer: C

NEW QUESTION # 106
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Linear regression requires target variables to be binary
B. Logistic regression outputs probabilities, while linear regression predicts continuous values
C. Linear regression applies only to classification tasks
D. Logistic regression uses the logit function to model relationships

Answer: B,D

NEW QUESTION # 107
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.replace()
B. df.dropna()
C. df.rename()
D. df.sort_values()

Answer: A

NEW QUESTION # 108
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