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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Error handling and best practices 2. Executing queries and retrieving results 3. Connecting to SQLite, MySQL or PostgreSQL - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Data Acquisition and Preprocessing	30%	- 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 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 Visualization and Communication	15%	- Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Overfitting, underfitting and generalization 3. Hyperparameter tuning basics - Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees
Data Exploration and Statistical Analysis	25%	- Descriptive statistics 1. Frequency distributions and percentiles 2. Correlation and covariance analysis 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. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q101-Q106):

NEW QUESTION # 101
Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

A. df.memory_usage()
B. df.dtypes
C. df.to_csv()
D. df.info()

Answer: B,D

NEW QUESTION # 102
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A. GROUP BY
B. WHERE
C. HAVING
D. ORDER BY

Answer: B

NEW QUESTION # 103
Which characteristics are commonly associated with cloud-based data storage solutions?

A. Requires dedicated local hardware
B. Physical proximity to user machines
C. High availability and redundancy
D. Elastic scalability

Answer: D

NEW QUESTION # 104
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Linear regression requires target variables to be binary
B. Logistic regression outputs probabilities, while linear regression predicts continuous values
C. Linear regression applies only to classification tasks
D. Logistic regression uses the logit function to model relationships

Answer: B,D

NEW QUESTION # 105
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It links Matplotlib with Seaborn's grid styling
B. It overlays multiple plots in the same figure without axes
C. It configures multiple plots in a grid layout within a single figure
D. It creates a 3D surface plot using pandas

Answer: C

NEW QUESTION # 106
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