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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 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
Topic 2: Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Heatmaps, pair plots and correlation matrices 3. Customization and styling - Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation
Topic 3: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 3. Indexing, filtering, sorting and grouping - Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation - Data collection, integration and storage 1. Data formats and storage systems 2. Data collection methods and sources 3. Data integration and merging
Topic 4: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Probability concepts and distributions 2. Hypothesis testing and confidence intervals 3. Statistical significance and interpretation - Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics
Topic 5: SQL and Database Integration	10%	- 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 - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q125-Q130):

NEW QUESTION # 125
Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)

A. Automatically converting all text to uppercase
B. Defining and enforcing schema constraints
C. Sorting columns alphabetically
D. Using assertions to enforce domain-specific rules

Answer: B,D

NEW QUESTION # 126
Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?

A. ValueError
B. IOError
C. RuntimeError
D. TypeError

Answer: A

NEW QUESTION # 127
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].fillna()
B. df['id'].duplicated().any()
C. df['id'].nunique()
D. df['id'].isnull().sum()

Answer: B

NEW QUESTION # 128
Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?

A. load_dataset()
B. read_csv(chunksize=...)
C. fetch_chunks()
D. import_csv()

Answer: B

NEW QUESTION # 129
How would you extract the last three rows of a DataFrame df using position-based indexing?

A. df.iloc[-3:]
B. df[-3]
C. df.tail(3)
D. df.loc[-3:]

Answer: A

NEW QUESTION # 130
......

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