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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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Guidewire Claim Insights in Databricks and Tableau - How many claims are there?

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 In this blog post, sharing guidewire claims how many claims got created per city  and per state basics Databricks it is available here :  Claim happened from most US State Insights data analytics Template V1.0 - Databricks SQL Sample code  select AX.CITY, count( AX.CLAIM_NUMBER) as TOTAL_CLAIMS_PER_CITY from ( select distinct C.CLAIMNUMBER as CLAIM_NUMBER, ST. NAME as STATE, ADDR.CITY from guidewire_raw_db.cc_claim C left join guidewire_raw_db.cc_contact CONT on C.INSUREDDENORMID = CONT. ID left join guidewire_raw_db.cc_address ADDR on CONT.PRIMARYADDRESSID = ADDR. ID left join guidewire_raw_db.cctl_state ST on ADDR.STATE = ST. ID ) AX group by AX.CITY order by 2 desc From these SQL Query Resultsets Feed into Tableau Public Edition This Tableau Public view Available here  https://public.tableau.com/views/Guidewire_Insurance_Claims_Insights_one/InsuranceClaimsPerCity?:language=en-US&:sid=&:redirect=auth&:display_count=n&...
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Guidewire Self-Managed Data Integration with Business context

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 In Guidewire, We have majorly four centers: 1. Contact Manager 2. Policy Center 3. Billing Center 4. Claim Center Business Context wise follow the same order to remember that business & data integration ways it is best approach, While loading sample data with individual instances load sample data in the same order 1,2,3,4; Once after loading the sample data stop the server and then after do the integration between multiple centers in both directions  1. Contact Manager to All remaining xCenters and vice versa, reverse directions also possible 2. Policy center to integrate with Billingcenter and Claimcenter as well as viceversa 3. Billingcenter & claimcenter there is not possible to integrate !! Coming to Business context how to easily recall above sequence    1. Contact Manager:   It is used to create diverse contacts broadly into Person / Company types; Contact Manager with AddressbookUID, PublicIDs are available, any business contact is im...
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GenAI LLM for Faster Translation Product Feature - Architecute and Framework

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  As part of LLM KRL ( LLM Know Respect Language ) model this is the product feature choices fastest way to translate and comprehend !! Enabling Human Communication Multi Cultural and Multi Language support, still native people feel free to talk/ consult and communicate with respectful and fastest word exchanges !! Designing an LLM (Large Language Model) and GenAI (Generative AI) architecture for faster translations involves a combination of strategies to optimize model selection, training, inference, and deployment. Below is a step-by-step architecture design that includes the latest techniques for scalable and efficient translation. 1. High-Level Architecture Key Components: Data Preprocessing : Text cleaning, tokenization, and language alignment. Model Backbone : Efficient transformer-based models (e.g., MarianMT, BLOOM, or distilled versions of GPT). Training Optimization : Mixed precision, parameter-efficient fine-tuning (PEFT), and low-rank adaptation (LoRA). Inference ...
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Enhancing Quantum Leadership with individual clairvoyance emotions - Part of Neuroscience & People Research for Future Human Leadership Agenda

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 The concept of clairvoyance or heightened perception, especially in the context of exploring parallel realities of human emotional feelings , delves into both metaphysical and psychological realms. While there's no scientific confirmation of parallel emotional realities, experimental and conceptual approaches can help us explore this idea. Here’s an organized framework for understanding and experimenting with clairvoyant feelings in the context of parallel emotional realities : 1. Clairvoyance and Emotional Perception Clairvoyance is often described as the ability to gain insight beyond ordinary sensory experiences, sometimes perceived as tapping into parallel or higher-dimensional realities. When linked to emotions, it involves: Heightened Emotional Empathy : Experiencing others’ feelings as if they were your own. Detecting unspoken or subconscious emotional states in others. Simultaneous Emotional States : Feeling conflicting or parallel emotions at the same time, such as joy ...
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People Research & NeuroScience Research - Life time of Human Emotions

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 Below is a comprehensive list of feelings and emotions that humans can experience over a lifetime, categorized by their nature. This list includes both primary emotions and nuanced ones that emerge from combinations or cultural influences. 1. Basic Emotions (Paul Ekman’s Six) Happiness : Joy, contentment, amusement, satisfaction, delight, euphoria, bliss. Sadness : Grief, sorrow, loneliness, disappointment, melancholy, heartbreak, despair. Anger : Frustration, rage, annoyance, resentment, irritation, indignation, fury. Fear : Anxiety, dread, panic, nervousness, apprehension, terror, worry. Disgust : Revulsion, contempt, disdain, aversion, loathing. Surprise : Shock, amazement, wonder, astonishment. 2. Positive Emotions Love : Affection, adoration, passion, infatuation, tenderness, devotion. Gratitude : Appreciation, thankfulness, indebtedness. Hope : Optimism, anticipation, inspiration. Compassion : Empathy, sympathy, kindness, altruism. Pride : Achievement, sel...
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Introduction - GenAI Data Analytics using Python

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Introduction to Python and Data Analytics: Details This module provides a foundation in Python programming and introduces participants to the field of data analytics. It's designed for beginners with no prior experience in Python or analytics. --- Objective Familiarize participants with Python programming basics. Introduce the core concepts of data analytics. Enable participants to explore, clean, and prepare datasets. --- Content Outline 1. What is Data Analytics? What is Data Analytics? Data analytics is the process of examining raw data to identify trends, patterns, and insights to aid in decision-making. It involves using statistical techniques, tools, and software to transform data into actionable knowledge. --- Key Components of Data Analytics 1. Data Collection Gathering data from various sources like databases, APIs, sensors, and surveys. 2. Data Cleaning Preparing and correcting the data for analysis by handling missing values, removing duplicates, and correcting inconsist...
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