Interview questions
Preparing for a data engineering interview at Accenture? This page contains 30 real interview questions sourced from verified Accenture interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Accenture data engineering interviews typically focus on SQL, Behavioral, and Spark/Big Data. The interview bar skews toward harder problems (8 hard vs. 7 easy), suggesting emphasis on depth and system-level thinking.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
Write an SQL query to find the second-highest salary from an employee table.
What is the difference between cache() and persist() in Spark? When would you use each?
What is the difference between groupByKey and reduceByKey in Spark?
Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.
Briefly introduce yourself and walk us through your journey as a Data Engineer so far.
Can you explain the difference between OLTP and OLAP?
Describe a time when you had to optimize a slow SQL query. What steps did you take?
Explain the concept of ACID properties in the context of databases.
Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
How do you handle NULL values in SQL? Mention functions like COALESCE and NULLIF.
What is a Common Table Expression (CTE), and when would you use it?
What is the difference between a primary key and a unique key?
What is the difference between WHERE and HAVING clauses in SQL?
Describe the difference between Spark RDDs, DataFrames, and Datasets.
What is the difference between a list and a tuple in Python?
How do you ensure smooth communication between data scientists, business teams, and developers?
Why do you want to join this company?
Why should we hire you for this role?
Explain the difference between Azure Data Factory (ADF) and Databricks.
SQL query to find the second highest salary from each department.
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