SQL Queries Code Interviews QA 50 plus

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Here are SQL-focused interview questions with only the relevant SQL code: 1. Find the second highest salary from an Employee table. SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees); Using ROW_NUMBER(): WITH RankedSalaries AS (   SELECT Salary, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS Rank   FROM Employees ) SELECT Salary AS SecondHighestSalary FROM RankedSalaries WHERE Rank = 2; --- 2. Write a query to calculate a running total of sales. SELECT   OrderID,   OrderDate,   Amount,   SUM(Amount) OVER (ORDER BY OrderDate) AS RunningTotal FROM Orders; --- 3. Retrieve customers who placed no orders using a LEFT JOIN. SELECT c.CustomerID, c.CustomerName FROM Customers c LEFT JOIN Orders o ON c.CustomerID = o.CustomerID WHERE o.OrderID IS NULL; --- 4. Write a query to find the top 3 highest salaries. SELECT DISTINCT Salary FROM Employees ORDER BY Salary DESC LIMIT 3; Using DENSE_RANK(): WIT...
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SpaceTech Insurance Dataset Creation and Agile Workflow Planning

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Source Code here in kaggle: SpaceTech_Insurance_Agile_Strories_Dataset V1.0 

When we have private agile user stories, we can able to refine the COT / TOT framework related prompt responses more and lot refined and easier eye catching solutions.

Concept Overview: SpaceTech Insurance Dataset Creation and Agile Workflow Planning

Business Context

SpaceTech insurance is an emerging field addressing risks associated with space exploration, satellite launches, and orbital operations. Insurance companies require robust datasets and workflows to assess risks, underwrite policies, manage claims, and ensure regulatory compliance. By creating an agile dataset and workflow planning approach, insurers can streamline operations, improve decision-making, and adapt to the dynamic nature of the SpaceTech industry.

Business Research

Market Demand:

Increased reliance on satellites for communication, GPS, and Earth observation. Growing investments in private space exploration companies like SpaceX, Blue Origin, and others. Rising frequency of space-related risks like space debris and launch failures. Key Stakeholders:

Insurers: Need accurate risk models and underwriting frameworks. SpaceTech Companies: Seek customized insurance solutions. Regulators: Require compliance with treaties like the Outer Space Treaty. Challenges:

Lack of historical data for emerging risks. High costs of space missions, leading to complex policy structures. International compliance with space laws and treaties.

Business Analysis

Risk Identification:

Space Debris: Probability and impact of collisions with operational satellites. Launch Failures: Historical success rates of launch vehicles. Environmental Risks: Solar storms or space weather affecting satellites. Workflow Analysis:

Underwriting: Processes to calculate premiums based on identified risks. Claims Management: Steps to verify claims, especially after catastrophic events. Compliance: Integration of international space law data. Technology Gaps:

Lack of real-time data analytics for orbital events. Manual processes for claims verification in high-tech environments.

Business Analytics

Data Sources:

Satellite telemetry and orbital data. Historical records of launches and incidents. Space weather forecasting tools. Predictive Models:

Use AI/ML for predicting satellite lifespan, probability of collisions, and launch success rates. Develop scenario analysis for catastrophic failures. KPI Metrics:

Risk-adjusted premium calculations. Claim resolution time. Compliance adherence rate.

Technical Details and Analytics

Dataset Design:

Features: Risk type (debris, launch failure, etc.). Mission cost and ROI (Return on Investment). Compliance metrics (e.g., adherence to treaties). Structure: Relational databases for underwriting and claims. Big data platforms for real-time satellite data. Analytics Tools:

Predictive Analytics: Tools like Azure Machine Learning and Databricks. Visualization: Power BI for presenting insights. Compliance Analytics: Integration with legal datasets (e.g., international treaties). System Architecture:

Centralized data lake for storing telemetry and mission data. APIs to integrate with SpaceTech companies for real-time updates. Blockchain for secure claim processing and compliance logging.

Outcomes for Future Possibilities

Improved Risk Models:

Enhanced datasets will allow insurers to assess new risks (e.g., asteroid mining missions). Real-time risk assessments through predictive models. Automation of Workflows:

Smart contracts for claims management using blockchain. Automated premium adjustments based on real-time mission data. New Insurance Products:

Usage-Based Insurance (UBI) for satellites. Insurance for space tourism and private astronauts. Policies for mega-constellations of satellites (e.g., Starlink). Enhanced Stakeholder Collaboration:

Shared datasets between insurers, regulators, and SpaceTech companies for transparency. Global compliance databases to ease international operations. Sustainability Initiatives:

Risk modeling to incentivize sustainable practices (e.g., de-orbiting defunct satellites). Policies promoting the development of space debris mitigation technologies. Market Leadership:

Insurers leveraging advanced analytics will gain a competitive edge in the SpaceTech insurance market. Establishment of industry standards for dataset use and workflow design. This detailed approach ensures that SpaceTech insurance evolves into a robust, data-driven domain capable of addressing current and future challenges in space exploration and commerce.

Sample code for CSV file generation





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