Quiz 2026 Python Institute PCAD-31-02: Valid Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Latest Exam Questions

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q136-Q141):

NEW QUESTION # 136
Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

Answer: A,D


NEW QUESTION # 137
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

Answer: C


NEW QUESTION # 138
Which element is essential to justify a conclusion drawn from a dataset?

Answer: A


NEW QUESTION # 139
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

Answer: A


NEW QUESTION # 140
What is the primary purpose of using annotations in a data visualization?

Answer: B


NEW QUESTION # 141
......

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