The Smart Cube Data Engineer Interview Questions and Answers for Freshers

Q1. Past Exp and Resume
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Take home coding test with 3 SQL and 3 Python questions. Asked to solve only SQL questions but I solved them all.

Q1. 3 in-person interviews with general discussions about resume and past projects
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Q1. Hard DP level DSA question
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Q1. How good are you with numbers
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Q1. Vlookup, pivot table, sumif, average, graphs
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Q1. Do you know the role and responsibility of this position
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It was section wise, MCQ, then coding questions

Q1. Questions related to tableau
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Q2. Can you describe your project experience?
Ans. 
I have experience working on projects involving data pipeline development, ETL processes, and data analysis.
• Developed data pipelines using tools like Apache Spark and Airflow

• Implemented ETL processes to extract, transform, and load data from various sources

• Performed data analysis to derive insights and support decision-making

• Worked on optimizing data storage and retrieval for improved performance
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Q1. Power BI Difference between ALL() and ALLSELECTED()
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ALL() ignores all filters in the query context, while ALLSELECTED() ignores only filters on columns in the visual.
• ALL() removes all filters from the specified column or table.

• ALLSELECTED() removes filters from the specified column or table, but keeps filters on other columns in the visual.

• Example: ALL('Table') would remove all filters on the 'Table' in the query context.

• Example: ALLSELECTED('Column') would remove filte...read more
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Q2. Excel Difference between COUNT() and COUNTA()
Ans. 
COUNT() counts only numeric values, while COUNTA() counts all non-empty cells.
• COUNT() counts only cells with numerical values.

• COUNTA() counts all non-empty cells, including text and errors.

• Example: COUNT(A1:A5) will count only cells with numbers, while COUNTA(A1:A5) will count all non-empty cells.
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Q1. Resume based questions like explain the projects that you have done.
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Q2. Sample dataset questions
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Q1. Data warehouse concepts
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Q1. Basics of python, SQL and pyspark
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Q1. Questions on Python, SQL
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PySpark questions to solve

Q1. Architectural questions
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Q1. What makes you believe you are a good fit for this position?
Ans. 
I have a strong background in data analysis, with experience in utilizing various analytical tools and techniques to derive insights from data.
• I have a degree in Statistics and have completed multiple data analysis projects during my academic career.

• I am proficient in using programming languages such as Python and R for data analysis.

• I have experience working with SQL databases and extracting data for analysis.

• I have s...read more
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Q1. What are SQL window functions and how are they used in database queries?
Ans. 
SQL window functions are used to perform calculations across a set of table rows related to the current row.
• Window functions operate on a set of rows related to the current row, known as a window.

• They can be used to calculate running totals, ranks, averages, and more.

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), SUM(), AVG(), and LEAD().
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