DataMetica Azure Data Engineer Interview Questions and Answers

Q1. Sql based question to get result in json format
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General Aptitude test

Q1. On SQL functions
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Q2. Python coding questions
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Coding round with a python question and SQL query

Q1. Technical question and a coding question in python, medium level
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1hour,Time speed distance

1hour,sql,python,algebra,Average

Q1. Components of Databricks
Ans. 
Databricks is a unified data analytics platform that includes components like Databricks Workspace, Databricks Runtime, and Databricks Delta.
• Databricks Workspace: Collaborative environment for data science and engineering teams.

• Databricks Runtime: Optimized Apache Spark cluster for data processing.

• Databricks Delta: Unified data management system for data lakes.
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Q2. How to read a json file
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To read a JSON file, use a programming language's built-in functions or libraries to parse the file and extract the data.
• Use a programming language like Python, Java, or JavaScript to read the JSON file.

• Import libraries like json in Python or json-simple in Java to parse the JSON data.

• Use functions like json.load() in Python to load the JSON file and convert it into a dictionary or object.

• Access the data in the JSON fi...read more
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Q3. Second highest salary SQL
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To find the second highest salary in SQL, use the MAX function with a subquery or the LIMIT clause.
• Use the MAX function with a subquery to find the highest salary first, then use a WHERE clause to exclude it and find the second highest salary.

• Alternatively, use the LIMIT clause to select the second highest salary directly.

• Make sure to handle cases where there may be ties for the highest salary.
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Q4. How to configure the spark while creating the cluster
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Spark cluster configuration involves setting up memory, cores, and other parameters for optimal performance.
• Specify the number of executors and executor memory

• Set the number of cores per executor

• Adjust the driver memory based on the application requirements

• Configure shuffle partitions for efficient data processing

• Enable dynamic allocation for better resource utilization
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Q1. NLP based question
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Q1. Project history
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Q2. Tech stack used
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Our tech stack includes Python, SQL, Apache Spark, Hadoop, AWS, and Docker.
• Python is used for data processing and analysis

• SQL is used for querying databases

• Apache Spark is used for big data processing

• Hadoop is used for distributed storage and processing

• AWS is used for cloud infrastructure

• Docker is used for containerization
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Q1. Handled team or not?
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Q2. Project experience
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Q1. Spark and airflow related questions
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Q2. AWS services and it's related questions
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