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Creating a Data Warehouse DevOps Engineer @ Amazon Difficulty hard

Describe the steps involved in creating a data warehouse. Include considerations for data modeling, ETL processes, and the technologies you would use.

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Power BI vs Tableau Data Analyst @ Accenture Difficulty medium

Can you compare Power BI and Tableau, especially in terms of their features, usability, and use cases in a business context?

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Understanding Measures, Calculated Columns, and Filters in Power BI Data Analyst @ Accenture Difficulty medium

Can you explain the difference between Measures and Calculated Columns in Power BI, and how filters are used to manipulate data within reports and dashboards?

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Creating a Data Quality Dashboard Data Analyst @ Accenture Difficulty medium

Given a dataset containing customer transaction records in a CSV format, describe how you would create a data quality dashboard to track key metrics like missing values, duplicate entries, and data type mismatches. Specify the tools and technologies you would use.

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Comparing BI Tools and Knowledge of Machine Learning Data Analyst @ Amazon Difficulty medium

What is the difference between Tableau, Power BI, and other BI tools? Do you have any knowledge of Machine Learning?

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Troubleshooting Broken Reports Data Analyst @ Apple Difficulty medium

How would you diagnose and resolve issues when a business intelligence report breaks in a production environment?

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Analysis of Sales Data Using SQL and Power BI Quality Assurance @ IBM Difficulty medium

You are tasked with analyzing sales data for a retail company using SQL and Power BI. The dataset includes tables for sales transactions, products, and customer information. The goal is to create a comprehensive report that provides insights into sales trends, customer segmentation, and product performance.

  1. Data Extraction and Preparation: Outline the SQL queries you would use to extract relevant data from the sales database. Include how you would join tables to gather information on sales transactions, product details, and customer demographics.

  2. Data Analysis in Power BI: Describe the steps you would take in Power BI to visualize and analyze the extracted data. Include the types of visualizations you would create to showcase sales trends over time, customer purchasing behavior, and regional sales performance.

  3. Insights and Recommendations: Based on your analysis, identify key insights such as top-selling products, customer segments with high purchase frequency, and regions with potential growth opportunities. Provide recommendations on strategies to improve sales and customer engagement based on your findings.

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