Quiz 2026 Python Institute PCAD-31-02: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)–Professional Exam Course

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

Answer: B,C


NEW QUESTION # 91
In supervised learning, what is the primary goal when training a model on labeled data?

Answer: A


NEW QUESTION # 92
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

Answer: A,C


NEW QUESTION # 93
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

Answer: A


NEW QUESTION # 94
What is the primary advantage of using web scraping as a data collection method in business analytics?

Answer: A


NEW QUESTION # 95
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