Python Institute PCAD-31-02 Simulation Questions - Test PCAD-31-02 Engine

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Executing queries and retrieving results
  • 3. Error handling and best practices
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization
- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Heatmaps, pair plots and correlation matrices

>> Python Institute PCAD-31-02 Simulation Questions <<

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q48-Q53):

NEW QUESTION # 48
What is a major challenge in aggregating data from multiple sources?
Response:

Answer: D


NEW QUESTION # 49
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

Answer: B


NEW QUESTION # 50
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

Answer: A,B


NEW QUESTION # 51
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

Answer: B,D


NEW QUESTION # 52
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: C


NEW QUESTION # 53
......

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