Examcollection PCAD-31-02 Vce - PCAD-31-02 Exam

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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. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization
Topic 2: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - 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. Probability concepts and distributions 3. Hypothesis testing and confidence intervals
Topic 3: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Data structures: Series, DataFrame, ndarray 3. Indexing, filtering, sorting and grouping - Data collection, integration and storage 1. Data collection methods and sources 2. Data formats and storage systems 3. Data integration and merging
Topic 4: SQL and Database Integration	10%	- SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Error handling and best practices 2. Executing queries and retrieving results 3. Connecting to SQLite, MySQL or PostgreSQL - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 5: Data Visualization and Communication	15%	- Visualization principles and best practices 1. Choosing appropriate chart types 2. Audience-focused presentation 3. Color, layout and clarity - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Heatmaps, pair plots and correlation matrices 3. Customization and styling - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q98-Q103):

NEW QUESTION # 98
Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)

A. Using string formatting to build SQL queries dynamically
B. Wrapping SQL commands in try-except blocks
C. Employing parameterized statements for user-supplied data
D. Using string concatenation to insert variables into SQL

Answer: B,C

NEW QUESTION # 99
Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?

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

Answer: C

NEW QUESTION # 100
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A. Replace NULL with zero before importing
B. Use == None directly in SQL queries
C. Convert all NULL to string before comparison
D. Use Python's None and SQL's IS NULL clause

Answer: D

NEW QUESTION # 101
What is the correct SQL statement to permanently remove a table named customers from a database?

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

Answer: C

NEW QUESTION # 102
What is the primary purpose of using annotations in a data visualization?

A. To replace the need for axes
B. To enhance interactivity in static plots
C. To ensure faster rendering in large datasets
D. To highlight important data points and provide contextual information

Answer: D

NEW QUESTION # 103
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