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...
Meesho PySpark Interview Questions for Data Engineers in 2025 Preparing for a PySpark interview? Let’s tackle some commonly asked questions, along with practical answers and insights to ace your next Data Engineering interview at Meesho or any top-tier tech company. 1. Explain how caching and persistence work in PySpark. When would you use cache() versus persist() and what are their performance implications? Answer : Caching : Stores data in memory (default) for faster retrieval. Use cache() when you need to reuse a DataFrame or RDD multiple times in a session without specifying storage levels. Example: python df.cache() df.count() # Triggers caching Persistence : Allows you to specify storage levels (e.g., memory, disk, or a combination). Use persist() when memory is limited, and you want a fallback to disk storage. Example: python from pyspark import StorageLevel df.persist(StorageLevel.MEMORY_AND_DISK) df.count() # Triggers persistence Performance Implications : cache() is ...