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Updated: Sep 26, 2026

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Python Institute PCAD-31-02 Exam Overview:

Certification Vendor:Python Institute
Exam Name:Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
Exam Number:PCAD-31-02
Certificate Validity Period:No expiration (lifetime validity)
Exam Format:Fill in the Blank, Multiple Select, Multiple Choice, Code Completion
Available Languages:English
Exam Duration:65 minutes
Passing Score:70%
Related Certifications:PCEP - Certified Entry-Level Python Programmer
PCAP - Certified Associate in Python Programming
Real Exam Qty:40
Exam Price:$295 USD
Sample Questions:Python Institute PCAD-31-02 Sample Questions
Exam Way:Online proctored exam through Pearson VUE testing centers
Pre Condition:PCEP certification is recommended but not required. Basic Python programming knowledge is expected.
Official Syllabus URL:https://pythoninstitute.org/certifications/pcad

Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Correlation analysis
  • 2. Data distribution analysis
  • 3. Descriptive statistics computation
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Results interpretation and presentation
  • 2. Problem definition
  • 3. Analysis and modeling
  • 4. Data exploration and cleaning
Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Lists, tuples, dictionaries, sets
  • 3. Numbers, strings, booleans
- File Operations
  • 1. Writing to files
  • 2. Reading from files (text, CSV)
  • 3. Context managers (with statement)
- Control Flow and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Loops (for, while)
  • 3. Return values and scope
  • 4. Function definitions and parameters
Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Data analysis concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
- Data Collection and Preparation
  • 1. Data import/export operations
  • 2. Data cleaning and preprocessing basics
  • 3. Data sources and acquisition methods
Working with Data Using Python Libraries30%- Pandas Library
  • 1. Data selection and filtering
  • 2. Handling missing data
  • 3. Series and DataFrame structures
  • 4. DataFrame operations (merge, join, concat)
  • 5. GroupBy operations
- Data Visualization
  • 1. Customizing plots
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Matplotlib basics
  • 4. Seaborn introduction
- NumPy Fundamentals
  • 1. Array indexing and slicing
  • 2. NumPy arrays and operations
  • 3. Basic statistical functions
  • 4. Vectorized operations

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

Question #1

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

  • A. plt.title()
  • B. plt.xlabel()
  • C. plt.legend()
  • D. plt.annotate()
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #2

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
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Question #3

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.rename()
  • B. df.replace()
  • C. df.sort_values()
  • D. df.dropna()
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

What may occur if a model is evaluated using the same data it was trained on?

  • A. Underfitting
  • B. Overfitting
  • C. Data leakage
  • D. Overgeneralization
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #5

What does the standard deviation of a dataset represent in the context of statistical analysis?

  • A. The most frequently occurring value
  • B. The average squared distance from the mean
  • C. The number of outliers in the dataset
  • D. The sum of all values in the dataset
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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