Exam PCAD-31-02 Cram Questions, PCAD-31-02 Latest Exam Review

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q74-Q79):

NEW QUESTION # 74
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

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

Answer: A

NEW QUESTION # 75
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. Lacks predefined data schema
D. Highly tabular and query-friendly

Answer: B,C

NEW QUESTION # 76
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 = ':id'", {'id': user_id})
D. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)

Answer: B

NEW QUESTION # 77
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Load CSV directly with re.compile()
B. Access data using requests.get() and parse it with .json()
C. Open Excel files using matplotlib
D. Use the requests module to retrieve JSON responses

Answer: B,D

NEW QUESTION # 78
Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

A. df['col1', 'col2']
B. df.iloc[0:5, 'col2']
C. df.loc[:, 'col1']
D. df.loc[2:7, ['col1', 'col2']]

Answer: C,D

NEW QUESTION # 79
......

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