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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q120-Q125):

NEW QUESTION # 120
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

A. Applying logarithmic transformation to skewed data
B. Replacing misspellings with a canonical category label
C. Trimming whitespace from string values
D. Scaling numeric features to a fixed range

Answer: B,C

NEW QUESTION # 121
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

A. Include concise textual summaries that highlight the chart's message
B. Repeat all numerical data in both the chart and text
C. Avoid summarizing the data verbally
D. Use visualizations alone without titles or captions

Answer: A

NEW QUESTION # 122
Which operations are valid when working with Pandas Series?
(Choose two)

A. Using .columns to rename
B. Merging by index using merge()
C. Arithmetic vector operations
D. Applying NumPy universal functions

Answer: C,D

NEW QUESTION # 123
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A. WHERE
B. HAVING
C. GROUP BY
D. ORDER BY

Answer: A

NEW QUESTION # 124
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 NULL
C. Returns True if all values in 'Age' are not NULL
D. Returns True if at least one value in 'Age' is not NULL

Answer: C

NEW QUESTION # 125
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