Tiger Analytics Data Engineer Interview Questions, Process, and Tips

In the first round they ask some technical mcq and gave some coding questions in python and SQL.there were less aptitude questions.

Q1. Just asking simple string based questions and some difficult sql questions .it was 1hr section.
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Q2. Groupby having, joins like inner outer left right
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Q3. Most frequent word in a sentence ?
Ans. 
The most frequent word in a sentence can be found by counting the occurrence of each word and selecting the one with the highest count.
• Split the sentence into words using whitespace as delimiter

• Create a dictionary to store the count of each word

• Iterate through the words and update the count in the dictionary

• Find the word with the highest count in the dictionary
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Q4. Given some tables and extract the values between some values and fileter by some values
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Q1. SQL problem window function
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Q2. SQL code like join and scenerio
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Q1. Design round for adf pipeline
Ans. 
Designing an ADF pipeline for data processing
• Identify data sources and destinations

• Define data transformations and processing steps

• Consider scheduling and monitoring requirements

• Utilize ADF activities like Copy Data, Data Flow, and Databricks

• Implement error handling and logging mechanisms
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Q2. Azure synapses and adf adb
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Q1. Expected ctc and current ctc negotiations
Ans. 
Discussing expected and current salary for negotiation purposes.
• Be honest about your current salary and provide a realistic expectation for your desired salary.

• Highlight your skills and experience that justify your desired salary.

• Be open to negotiation and willing to discuss other benefits besides salary.

• Research industry standards and salary ranges for similar positions to support your negotiation.

• Focus on the value y...read more
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Q2. Relocation and remote work until ofc open for Pune location
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It was good and comparatively moderate.

Q1. What is indexing in SQl
Ans. 
Indexing in SQL is a technique to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
• Indexes are created on columns in a database table to speed up the retrieval of data.

• They work similar to the index in a book, allowing the database to quickly find the rows that match a certain value.

• Indexes can be created using single or multiple columns.

• Examples: CREATE INDEX ind
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I was asked 2 coding problems in pyspark, after that a discussion round followed.

Q1. Explain databricks dlt, and when will you use batch vs streaming?
Ans. 
Databricks DLT is a unified data management platform for batch and streaming processing.
• Databricks DLT (Delta Lake Table) is a storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Batch processing is used when data is collected over a period of time and processed in large chunks, while streaming processing is used for real-time data processing.

• Use batch processing for historical data analy...read more
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Q2. What is the difference between deltalake and delta warehouse
Ans. 
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, while Delta Warehouse is a cloud-based data warehouse service.
• Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Delta Warehouse is a cloud-based data warehouse service that provides scalable storage and analytics capabilities.

• Delta Lake is more fo...read more
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