InterviewZilla’s Post

Top 40 Databricks Interview Questions and Answers- Basic to Advanced

📊 Ready to Ace Your Data Engineer Interview? 🚀 Asking the right questions is key. Check out our latest blog post for 3 essential questions to ask during your interview. Navigate your data journey with confidence and find your perfect fit with Emerald Resource Group! #DataEngineer #InterviewTips #EmeraldResourceGroup Link: https://lnkd.in/e5t383_d

🔰If you are preparing for Product-based companies. Companies often look for the logical ability of the candidates by problem-solving skills. 🔰DSA is the key aspect that you should be focusing on if you are looking for product-based companies. 🔰Here are some of the important questions that are repeated and you should check out before the Data Engineering interviews. 🔰PFA the document. 🍀 follow Nizamuddin M, for more interview tips. #bigdata #data #interview #interviewprep #interviewtips #dataengineer #bigdataengineer #azure #aws #help #growth #dsa #spark #hive #gcp #productbasedcompany #logicalthinking #problemsolvingskills

Interviewing and hiring for a technical role is hard. First you need the hard skills. Then you need to be able to communicate complex ideas clearly. Finally, you need to be able to help your organization get results that aren't always clearly defined when you're interviewing. This article from the Coderpad blog was a great example of this challenge from the hiring managers perspective. https://lnkd.in/gQs2NzG7

📣 Attention Data Engineering Aspirants! 📣 Are you feeling overwhelmed by interview prep? Unsure where to start with data structures and algorithms? Worried you're not good enough? We've got you covered! 🛡️ Introducing our 'Data Engineering Interview Series' - your guide to confidently tackling each section of the data engineering interview process. In our first post, we're diving into Data Structures and Algorithms: - Learn the essential data structures - Explore commonly asked questions - Gain the confidence to ace your interview No need for endless LeetCode grinding - we've distilled the key concepts you need to succeed! Ready to level up your interview skills? Check out our full post and start practicing today: https://lnkd.in/eA98Qx6Q Have questions or need clarification? Don't hesitate to reach out in the comments below! #dataengineering #interviewprep #techcareers #codingskills #jobsearch

🚀 Dive into the world of ElasticSearch with InterviewZilla! 🚀 We are excited to present our latest resource: "ElasticSearch Interview Questions and Answers." Whether you're a job seeker preparing for your next big opportunity or a hiring manager looking to gauge a candidate's expertise, this guide is your go-to tool for mastering ElasticSearch interviews. Discover key questions, detailed answers, and expert insights to help you shine in your next interview. 🔗 https://lnkd.in/gWUd2aBW Follow InterviewZilla for cutting-edge tech updates and invaluable career insights. #elasticsearch #interviewprep #techInterviews #interviewzilla #careergrowth #techjobs #hiring #bigdata #dataengineer Ready to level up? Let’s make your interview prep seamless and successful! 🌟

Hey, Here is Amazon Interview Question solution. Given the reviews table, write a query to retrieve the average star rating for each product, grouped by month. The output should display the month as a numerical value, product ID, and average star rating rounded to two decimals. Sort the output first by month and then by product ID. Reviews Example Input: review_id user_id submit_date product_id stars 6171 123 06/08/2022 00:00:00 50001 4 7802 265 06/10/2022 00:00:00 69852 4 5293 362 06/18/2022 00:00:00 50001 3 6352 192 07/26/2022 00:00:00 69852 3 4517 981 07/05/2022 00:00:00 69852 2 Example Output: mth product avg_stars 6 50001 3.50 6 69852 4.00 7 69852 2.50 Amazon Interview Questions: It is easy to decode the solution when you gaze into the data and think of the problem - Divide the problem into small pieces. - Ask yourself What you need to do to achieve the desired output, in other words, reverse engineer the question. for eg: To have Avg, you need to do GROUP. SELECT  EXTRACT(MONTH FROM submit_date) as mth, product_id, ROUND(AVG(stars),2) as avg_star FROM reviews GROUP BY EXTRACT(MONTH FROM submit_date),product_id ORDER BY EXTRACT(MONTH FROM submit_date),product_id Happy Saturday folks #datathinker #problemsolver #businessintelligence

🚀 Preparing for an interview can be daunting, but having a solid strategy and understanding the questions you might face is key to success! 🌟 ✨ I’ve compiled a list of 27 common interview questions ✨ That can help you anticipate what hiring managers might ask and craft thoughtful, compelling responses. Whether you're a seasoned professional or just starting out, these questions will help you reflect on your experiences, skills, and career goals. Take a look at the attached list, and make sure to practice your answers. Confidence comes from preparation! Good luck, and remember to showcase your unique strengths and potential. You've got this! 💪✨ -💀- LimiTed Resource - https://lnkd.in/gmt4C8VJ Free Course: IBM Data Engineering Professional Certificate. 🔸https://lnkd.in/gbpfbgMw 1. Master Python: https://lnkd.in/gRGTRUXt 2. Learn SQL: https://lnkd.in/gckPeHdG 3. Get hands-on with MySQL: https://lnkd.in/gT2Xdf9i 4. Dive into MongoDB: https://lnkd.in/grx5MGQX 5. Master PySpark: https://lnkd.in/gHQc9kYE 6. Discover Bash, Airflow & Kafka: https://lnkd.in/guX4VJQw 7. Master Git & GitHub: https://lnkd.in/gD_bgh4v 8. Understand CICD basics: https://lnkd.in/g8j3CEam 9. Decode Data Warehousing: https://lnkd.in/gtuXnHW9 11. Understand Data Lakes: https://lnkd.in/gjSC6hei 12. Explore DataBricks: https://lnkd.in/gsz_7mCm 13. Learn Azure Databricks: https://lnkd.in/gX72NYeB 14. Master Snowflake: https://lnkd.in/gH8AWi7i 15. Explore Apache NiFi: https://lnkd.in/gPVcf8Rt [Happy Learning - FREE Drive 100TB+ - https://lnkd.in/gg-V795h] FREE [t.me/NotesGallery1 / t.me/CodingBugs / wa.oia.bio/jobs] <<<<<Follow Vishal Rane for more such content>>>>> #datascience #dataengineering #programming  #computerscience #datascience #machinelearning   #python #deeplearning #technology #SQL #DataScience #FreeCourses #Programming #Python #MachineLearning #JobInterview #CareerTips #InterviewPreparation #LinkedIn

I gave 20+ Data engineering interviews last year and this is what you need to know 📐 Architectural Understanding is Crucial - Know Your Project Inside Out: Understand the "why" and "how" of your architectural decisions. - Scenario-Based Questions: Be ready to explain why you chose specific technologies or file formats over others or how you optimize your spark in a specific use-case, how you handle data quality checks, and the business use-cases you support. 💻 Coding Problems - Data Structures: Practice easy and medium-level Leetcode problems focusing on arrays, strings, etc. - SQL Problems: Be prepared for questions on window functions, joins, and query optimizations. ☁️ Cloud Services Thorough Understanding: Have a solid grasp of data-related cloud services and their features. Key Services: Familiarize yourself with at least one of AWS, Google Cloud, and Azure data services, and understand how to leverage their various features for data engineering tasks. 📊 Data Modeling and System Design - Data Modeling: Be ready to discuss schema types, Slowly Changing Dimensions (SCDs), and their applications. - System Design: Prepare for questions about designing database models or pipeline architecture, including the pros and cons of various technologies and tools used in the pipeline. By preparing in these areas, you'll be well-equipped to ace your data engineering interviews! Good luck! 🌟 ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #DataEngineering #JobInterviews #InterviewTips #Coding #CareerGrowth

Data Engineering Interview Questions which you are looking for your next interview preparation. I've compiled them and sharing with my LinkedIn Fam✨ Link in comments to grab this deal ✅