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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Modeling and Machine Learning Basics	20%	- 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. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 3. Accuracy, precision, recall, F1-score
Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Measures of central tendency and dispersion 2. Correlation and covariance analysis 3. Frequency distributions and percentiles
Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Data standardization and transformation 3. Quality assurance and validation techniques - 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. Indexing, filtering, sorting and grouping 2. Data structures: Series, DataFrame, ndarray 3. Data reshaping and aggregation
SQL and Database Integration	10%	- 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. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results
Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Customization and styling 3. Heatmaps, pair plots and correlation matrices

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q110-Q115):

NEW QUESTION # 110
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use df.sort_values() to sort both columns before merging
B. Use .isin() to compare the foreign key column against the parent key column
C. Use df.merge() with how='outer'
D. Use df.dropna() on the foreign key column

Answer: B

NEW QUESTION # 111
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A. Dropping all columns
B. Filling with random values
C. Replacing with NULL
D. Imputation using mean or median

Answer: D

NEW QUESTION # 112
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A. numpy.outer()
B. array1 * array2
C. numpy.matrix()
D. array1.dot(array2)

Answer: B

NEW QUESTION # 113
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)

A. Sanitize input by replacing dangerous characters with asterisks
B. Limit database user permissions to only necessary operations
C. Construct queries using f-strings for readability
D. Store credentials securely using environment variables

Answer: B,D

NEW QUESTION # 114
In supervised learning, what is the primary goal when training a model on labeled data?

A. To cluster data points into similar groups
B. To analyze unstructured data formats like text
C. To learn a function that maps inputs to known outputs
D. To discover hidden patterns without outputs

Answer: C

NEW QUESTION # 115
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