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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Applied Data Analysis Projects	20%	- Data Analysis Workflow 1. Analysis and modeling 2. Problem definition 3. Data exploration and cleaning 4. Results interpretation and presentation - Exploratory Data Analysis (EDA) 1. Data distribution analysis 2. Descriptive statistics computation 3. Correlation analysis 4. Pattern identification
Topic 2: Working with Data Using Python Libraries	30%	- Pandas Library 1. Series and DataFrame structures 2. DataFrame operations (merge, join, concat) 3. Handling missing data 4. GroupBy operations 5. Data selection and filtering - NumPy Fundamentals 1. NumPy arrays and operations 2. Vectorized operations 3. Basic statistical functions 4. Array indexing and slicing - Data Visualization 1. Matplotlib basics 2. Customizing plots 3. Creating basic charts (line, bar, scatter, histogram) 4. Seaborn introduction
Topic 3: Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data sources and acquisition methods 3. Data import/export operations - Introduction to Data Analysis 1. Data analysis process lifecycle 2. Data analysis concepts and terminology 3. Types of data (structured, unstructured, semi-structured)
Topic 4: Python Programming for Data Analysis	30%	- File Operations 1. Reading from files (text, CSV) 2. Context managers (with statement) 3. Writing to files - Python Data Types and Structures 1. Numbers, strings, booleans 2. Lists, tuples, dictionaries, sets 3. Data type conversions - Control Flow and Functions 1. Loops (for, while) 2. Return values and scope 3. Function definitions and parameters 4. Conditional statements (if, elif, else)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

A. Removing gridlines from all sheets
B. Using colored fonts to highlight every cell
C. Converting numeric values into images
D. Adding freeze panes to lock header rows

Answer: D

NEW QUESTION # 48
Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

A. CREATE
B. SELECT
C. UPDATE
D. INSERT

Answer: C

NEW QUESTION # 49
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

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

Answer: C

NEW QUESTION # 50
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A. [10, 15]
B. [25, 100]
C. [100, 225]
D. [25, 100, 225]

Answer: C

NEW QUESTION # 51
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 dropna() with axis=1
B. Use fillna() with a constant value
C. Apply interpolate() method
D. Use drop() to remove null rows

Answer: B

NEW QUESTION # 52
......

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