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Python Institute PCAD-31-02 Questions & Answers - in .pdf

PCAD-31-02 pdf
  • Total Q&A: 146
  • Update: Aug 27, 2026
  • Price: $59.99
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  • Vendor: Python Institute
  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
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PCAD-31-02 Study Guide
  • Total Q&A: 146
  • Update: Aug 27, 2026
  • Price: $59.99
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  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
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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
Exam Number:PCAD-31-02
Exam Price:$195 USD
Real Exam Qty:48
Passing Score:75%
Certificate Validity Period:6 years
Available Languages:French, German, Japanese, Spanish, English
Exam Format:Single-select questions, Multiple-select questions
Exam Duration:60 minutes
Related Certifications:PCED – Certified Associate in Python Engineering
PCEP – Certified Entry-Level Python Programmer
PCAP – Certified Associate in Python Programming
Recommended Training:Edube Interactive Learning Platform
PCAD Official Syllabus and Resources
Exam Registration:Python Institute Official Registration
Sample Questions:Python Institute PCAD-31-02 Sample Questions
Exam Way:Online proctored or onsite at authorized test centers
Pre Condition:No mandatory prerequisites; recommended: PCAP – Certified Associate in Python Programming or equivalent knowledge, basic understanding of statistics and data concepts
Official Syllabus URL:https://pythoninstitute.org/pcad

Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Error handling and best practices
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Quality assurance and validation techniques
  • 3. Handling missing, duplicate and invalid values
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Frequency distributions and percentiles
  • 3. Measures of central tendency and dispersion
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation

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

Question 1

What is a major challenge in aggregating data from multiple sources?
Response:

A. Overfitting in predictive models
B. Excessive disk space usage
C. Duplicate records and format inconsistencies
D. Increased algorithmic complexity


Question 2

Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Closing the database cursor after each query
B. Escaping quotes manually in SQL strings
C. Using parameterized queries
D. Encrypting the database connection


Question 3

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

A. Overfitting
B. Data leakage
C. Overgeneralization
D. Underfitting


Question 4

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

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


Question 5

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


Solutions:

Question 1
Answer: C
Question 2
Answer: C
Question 3
Answer: A
Question 4
Answer: C
Question 5
Answer: C

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