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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SQL and Database Integration	10%	- Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Executing queries and retrieving results 3. Error handling and best practices - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 2: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Overfitting, underfitting and generalization 2. Hyperparameter tuning basics 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
Topic 3: Data Acquisition and Preprocessing	30%	- 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 integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 3. Indexing, filtering, sorting and grouping
Topic 4: Data Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Audience-focused presentation 3. Choosing appropriate chart types - 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
Topic 5: Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Statistical significance and interpretation 2. Probability concepts and distributions 3. Hypothesis testing and confidence intervals - Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Frequency distributions and percentiles 2. Measures of central tendency and dispersion 3. Correlation and covariance analysis

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q83-Q88):

NEW QUESTION # 83
When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

A. str
B. datetime.datetime
C. time.struct_time
D. float

Answer: B

NEW QUESTION # 84
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. is operator for memory-level comparison
B. == operator for checking value equivalence
C. str() function to convert data formats
D. id() function to retrieve unique object identifiers

Answer: A,D

NEW QUESTION # 85
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

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

Answer: D

NEW QUESTION # 86
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Customizing tick labels
B. Adding descriptive axis labels
C. Disabling grid lines in all cases
D. Avoiding color entirely

Answer: A,B

NEW QUESTION # 87
What is the correct SQL statement to permanently remove a table named customers from a database?

A. DELETE TABLE customers;
B. REMOVE TABLE customers;
C. TRUNCATE TABLE customers;
D. DROP TABLE customers;

Answer: D

NEW QUESTION # 88
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