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Python Institute PCAD-31-02 Actual Tests : Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

PCAD-31-02 actual test
  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Nov 13, 2026
  • Q & A: 146 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Choosing appropriate chart types
  • 3. Color, layout and clarity
Topic 2: SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Executing queries and retrieving results
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Topic 3: Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Correlation and covariance analysis
  • 3. Frequency distributions and percentiles
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
Topic 4: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
Topic 5: Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score

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

Question #1

Which operations are valid when working with Pandas Series?
(Choose two)

  • A. Using .columns to rename
  • B. Merging by index using merge()
  • C. Arithmetic vector operations
  • D. Applying NumPy universal functions
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

Question #2

Which characteristics are commonly associated with cloud-based data storage solutions?

  • A. Requires dedicated local hardware
  • B. High availability and redundancy
  • C. Elastic scalability
  • D. Physical proximity to user machines
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #3

What is a key assumption of linear regression that distinguishes it from logistic regression?

  • A. The target variable is unordered categorical
  • B. Predictors must be independent
  • C. The relationship between predictors and the outcome is linear
  • D. Output values must be binary
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #4

Which statements correctly describe differences between linear and logistic regression?
(Choose two)

  • A. Logistic regression uses the logit function to model relationships
  • B. Linear regression applies only to classification tasks
  • C. Linear regression requires target variables to be binary
  • D. Logistic regression outputs probabilities, while linear regression predicts continuous values
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Question #5

What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

  • A. (4, 5, 6)
  • B. 5.0
  • C. 15
  • D. Error
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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