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

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

NEW QUESTION # 103
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It overlays multiple plots in the same figure without axes
B. It configures multiple plots in a grid layout within a single figure
C. It links Matplotlib with Seaborn's grid styling
D. It creates a 3D surface plot using pandas

Answer: B

NEW QUESTION # 104
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. str() function to convert data formats
B. == operator for checking value equivalence
C. is operator for memory-level comparison
D. id() function to retrieve unique object identifiers

Answer: C,D

NEW QUESTION # 105
Which characteristics are typically associated with unstructured data formats?
(Choose two)

A. Stored using fixed-width fields and records
B. Requires parsing or transformation before analysis
C. Highly tabular and query-friendly
D. Lacks predefined data schema

Answer: B,D

NEW QUESTION # 106
Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

A. psycopg2
B. pymysql
C. sqlite3
D. sqlalchemy

Answer: C

NEW QUESTION # 107
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
B. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
C. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
D. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})

Answer: A

NEW QUESTION # 108
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