Tools in Data Science End Term: 31 August 2025, Set QDD1 (May 2025 term)

When using uv for Python package management, which command sequence correctly creates a new project with a specific Python version and installs dependencies?

In Git workflow for collaborative data science projects, what is the most appropriate sequence for integrating feature changes?

When designing error handling in Python data processing pipelines, which approach generally offers the most robust and maintainable solution for unexpected runtime errors?

A data scientist needs to identify unique treatment combinations in a clinical trial dataset with 50,000 rows. Which pandas approach would be most memory-efficient for large datasets?

A developer debugging an API integration notices intermittent 429 status codes. Which Chrome DevTools approach would best help identify the rate limiting pattern?

For a geospatial analysis project involving buffer operations, spatial joins, and interactive mapping, which combination of libraries provides the most comprehensive solution?

When cleaning a dataset with company names like "Microsoft Corp", "Microsoft Corporation", "MSFT", which OpenRefine technique would be most effective?

When designing a SQLite database for time-series sensor data with millions of records, which indexing strategy optimizes both query performance and storage efficiency?

A data analyst needs to identify customers with purchase patterns similar to high-value segments. Which pandas operation most efficiently computes customer similarity?

You are working with a very large CSV file that is too big to fit into memory. Which pandas method is the best way to read and process the file without running out of memory?

A developer optimizing database-heavy applications needs to identify query bottlenecks and connection issues. Which monitoring approach provides comprehensive insights?

Scenario 1: AI-Powered Research Assistant Development
Dr. Kumar is developing an AI research assistant for environmental science students. The system needs to process academic papers, generate summaries, and answer domain specific questions while encouraging critical thinking.
Based on the above data, answer the given subquestions.

The research assistant uses a system prompt to guide its behavior. Which system prompt design principle is most important for educational effectiveness?

Scenario 1: AI-Powered Research Assistant Development
Dr. Kumar is developing an AI research assistant for environmental science students. The system needs to process academic papers, generate summaries, and answer domain specific questions while encouraging critical thinking.
Based on the above data, answer the given subquestions.

When students submit complex research queries, which factors most significantly impact the LLM's operational costs and response quality? (Select all that apply)

Scenario 1: AI-Powered Research Assistant Development
Dr. Kumar is developing an AI research assistant for environmental science students. The system needs to process academic papers, generate summaries, and answer domain specific questions while encouraging critical thinking.
Based on the above data, answer the given subquestions.

Which prompt engineering technique would generate the most pedagogically valuable response for environmental research?

Scenario 2: Smart City Traffic Management System
A metropolitan city is implementing a real-time traffic management system that processes data from IoT sensors, traffic cameras, and GPS devices to optimize traffic flow across 500+ intersections.
System Architecture:
● IoT sensors at intersections (Lat/Long coordinates provided)
● Central processing hub: (28.6139^(o) N, 77.2090^(o) E)
● Real-time data streams: traffic density, weather conditions, accident reports
● ML models for traffic prediction and route optimization
Based on the above data, answer the given subquestions.

The development team uses Git for version control of traffic algorithms. When a critical traffic routing bug is discovered in production, which Git workflow ensures rapid deployment of fixes while maintaining code integrity?

Scenario 2: Smart City Traffic Management System
A metropolitan city is implementing a real-time traffic management system that processes data from IoT sensors, traffic cameras, and GPS devices to optimize traffic flow across 500+ intersections.
System Architecture:
● IoT sensors at intersections (Lat/Long coordinates provided)
● Central processing hub: (28.6139^(o) N, 77.2090^(o) E)
● Real-time data streams: traffic density, weather conditions, accident reports
● ML models for traffic prediction and route optimization
Based on the above data, answer the given subquestions.

Traffic engineers need to analyze historical data patterns. Which command-line approach efficiently processes large log files to extract peak traffic periods and route preferences?

Scenario 2: Smart City Traffic Management System
A metropolitan city is implementing a real-time traffic management system that processes data from IoT sensors, traffic cameras, and GPS devices to optimize traffic flow across 500+ intersections.
System Architecture:
● IoT sensors at intersections (Lat/Long coordinates provided)
● Central processing hub: (28.6139^(o) N, 77.2090^(o) E)
● Real-time data streams: traffic density, weather conditions, accident reports
● ML models for traffic prediction and route optimization
Based on the above data, answer the given subquestions.

For deploying the traffic management system across distributed edge computing nodes at intersections, which containerization strategy optimizes resource utilization and ensures consistent performance?

Scenario 2: Smart City Traffic Management System
A metropolitan city is implementing a real-time traffic management system that processes data from IoT sensors, traffic cameras, and GPS devices to optimize traffic flow across 500+ intersections.
System Architecture:
● IoT sensors at intersections (Lat/Long coordinates provided)
● Central processing hub: (28.6139^(o) N, 77.2090^(o) E)
● Real-time data streams: traffic density, weather conditions, accident reports
● ML models for traffic prediction and route optimization
Based on the above data, answer the given subquestions.

When optimizing traffic routes for emergency vehicles during peak hours, which algorithmic approach best balances computational efficiency with real-time requirements?

Scenario 2: Smart City Traffic Management System
A metropolitan city is implementing a real-time traffic management system that processes data from IoT sensors, traffic cameras, and GPS devices to optimize traffic flow across 500+ intersections.
System Architecture:
● IoT sensors at intersections (Lat/Long coordinates provided)
● Central processing hub: (28.6139^(o) N, 77.2090^(o) E)
● Real-time data streams: traffic density, weather conditions, accident reports
● ML models for traffic prediction and route optimization
Based on the above data, answer the given subquestions.

The system calculates distances between intersections for route optimization. What is the primary computational advantage of implementing the Haversine formula in the traffic management context?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

Which of the following Apache log entries correctly represents a successful mobile checkout transaction that occurred during the peak analysis window (12:00–15:59)?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

To extract essential fields for the analysis "mobile checkout transactions between 12:00-15:59 with successful status codes", which data elements are required?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

To identify checkout transactions occurring between 12:00 and 15:59, which Python condition correctly validates the time range?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

To identify mobile traffic from the UserAgent string, which Python approach provides the most reliable mobile device detection?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

For determining successful transactions, which condition correctly identifies HTTP status codes indicating success?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

If the transaction count appears unexpectedly high, which systematic debugging approach would identify the root cause?

Scenario 3: E-commerce Platform Log Analysis
You're analyzing Apache access logs from a major e-commerce platform. The logs contain customer browsing patterns, purchase behaviors, and system performance metrics during Black Friday sales.
Log Format Specification:

Analysis Requirements:
● Filter purchases (POST to /checkout/) during peak hours (12:00-15:59)
● Identify mobile vs desktop traffic patterns
● Track successful transactions (status codes 200-299)
● Analyze product category performance
Based on the above data, answer the given subquestions.

When processing very large Apache log files (10GB+), which of the following Python approaches offers the best memory efficiency?

Scenario 4: Academic Research RAG System with Multi-Modal Analysis
A research university implements an advanced RAG system that processes academic papers, datasets, code repositories, and experimental results to support interdisciplinary research across STEM fields.
Advanced Multi-Modal RAG Architecture:
● Text Processing: Research papers, grants, technical documentation
● Code Analysis: GitHub repositories, computational notebooks, algorithm implementations ● Data Integration: Experimental datasets, simulation results, sensor data
● Visual Processing: Figures, charts, experimental images, technical diagrams
● Semantic Linking: Cross-reference relationships between concepts, methods, and findings Based on the above data, answer the given subquestions.

What is the primary advantage of multi-modal RAG systems over text-only approaches in academic research contexts?

Scenario 4: Academic Research RAG System with Multi-Modal Analysis
A research university implements an advanced RAG system that processes academic papers, datasets, code repositories, and experimental results to support interdisciplinary research across STEM fields.
Advanced Multi-Modal RAG Architecture:
● Text Processing: Research papers, grants, technical documentation
● Code Analysis: GitHub repositories, computational notebooks, algorithm implementations ● Data Integration: Experimental datasets, simulation results, sensor data
● Visual Processing: Figures, charts, experimental images, technical diagrams
● Semantic Linking: Cross-reference relationships between concepts, methods, and findings Based on the above data, answer the given subquestions.

Which system prompt design best supports interdisciplinary research collaboration and knowledge synthesis?

Scenario 4: Academic Research RAG System with Multi-Modal Analysis
A research university implements an advanced RAG system that processes academic papers, datasets, code repositories, and experimental results to support interdisciplinary research across STEM fields.
Advanced Multi-Modal RAG Architecture:
● Text Processing: Research papers, grants, technical documentation
● Code Analysis: GitHub repositories, computational notebooks, algorithm implementations ● Data Integration: Experimental datasets, simulation results, sensor data
● Visual Processing: Figures, charts, experimental images, technical diagrams
● Semantic Linking: Cross-reference relationships between concepts, methods, and findings Based on the above data, answer the given subquestions.

A researcher asks: "How can machine learning techniques be applied to optimize renewable energy grid integration, and what interdisciplinary approaches show promise?" Which response demonstrates the most effective research synthesis approach?

Scenario 4: Academic Research RAG System with Multi-Modal Analysis
A research university implements an advanced RAG system that processes academic papers, datasets, code repositories, and experimental results to support interdisciplinary research across STEM fields.
Advanced Multi-Modal RAG Architecture:
● Text Processing: Research papers, grants, technical documentation
● Code Analysis: GitHub repositories, computational notebooks, algorithm implementations ● Data Integration: Experimental datasets, simulation results, sensor data
● Visual Processing: Figures, charts, experimental images, technical diagrams
● Semantic Linking: Cross-reference relationships between concepts, methods, and findings Based on the above data, answer the given subquestions.

Which processing sequence correctly describes the multi-modal RAG system's comprehensive research synthesis workflow?