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Showing posts with the label BI/Tableau Developer Role Deloitte Interview Question

DevOps Consultant Interview Questions at MNC

DevOps Consultant Interview Questions and Answers: Insights from  Experience Recently, Someone had the opportunity to interview for a DevOps Consultant role. The session lasted 45 minutes and covered various aspects of my 3-year experience, tools, technologies, and best practices. Here’s how I tackled the questions:  1. Walk me through your profile? I highlighted my journey from the basics of DevOps to working on advanced tools and technologies. I emphasized: My hands-on experience with CI/CD pipelines. Proficiency in tools like Jenkins, Docker, Kubernetes, Terraform, Ansible, and Prometheus. Key projects, challenges faced, and my contributions to optimizing DevOps processes. 2. What are the tools and technologies you have worked on? I listed the tools with context: CI/CD : Jenkins, GitHub Actions. Containerization : Docker, Kubernetes, Helm. Infrastructure as Code (IaC) : Terraform, CloudFormation. Monitoring : Prometheus, Grafana, Loki. Security : SonarQube, Trivy for image...

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BI/Tableau Developer Role Deloitte Interview Question (with proof)

Recent Deloitte Interview Question for a Power BI/Tableau Developer Role How I Would Answer:  Interview Question: How would you analyze data, gather requirements, and use different tools to deliver insights? In this blog, I’ll walk you through my thought process using a practical example—a scenario where a clothing company wants to analyze why their shirt sales dropped last month. 1️⃣ Understand the Business Goal The first step is to clearly define the objective. Example: The goal is to understand the reasons behind a decline in shirt sales and recommend strategies to improve sales performance. 2️⃣ Identify Key Metrics Defining the right metrics is crucial for actionable insights. Example: Total shirt sales Customer purchases by region and day Return rates or refunds Sales trends by product category 3️⃣ Collect Relevant Data Gathering comprehensive data ensures the analysis is thorough. Example: Use SQL to query the company’s sales database for shirt sales by region, date, and pro...

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