Exponentia.ai ETL Talend Developer Interview Questions and Answers

They give you SQL assignment to complete based on that select for technical interview

Q1. What are window functions in SQL
Ans. 
Window functions in SQL are used to perform calculations across a set of table rows related to the current row.
• Window functions are used to calculate values based on a set of rows related to the current row.

• They allow you to perform calculations without grouping the rows into a single output row.

• Examples of window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().
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Q2. Difference between group by and distinct
Ans. 
Group by is used to group rows that have the same values into summary rows, while distinct is used to remove duplicate rows from a result set.
• Group by is used with aggregate functions like COUNT, SUM, AVG, etc.

• Distinct is used to retrieve unique values from a column or set of columns.

• Group by is used to perform operations on groups of rows, while distinct is used to filter out duplicate rows.

• Group by is used in conjunc...read more
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Q1. Sql questions and python
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Q1. About last projects
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Q1. The HR representative will seek to understand your experience and its alignment with the role.
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Q2. Salary expectations, Offers in hand
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Q1. What are the key features and functionalities of Snowflake?
Ans. 
Snowflake is a cloud-based data warehousing platform known for its scalability, performance, and ease of use.
• Snowflake uses a unique architecture called multi-cluster, which separates storage and compute resources for better scalability and performance.

• It supports both structured and semi-structured data, allowing users to work with various data types.

• Snowflake offers features like automatic scaling, data sharing, and ...read more
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Q2. The interviewer asked several complex SQL questions that utilized analytical functions.
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Q3. I was asked to write some syntax-level code for Jinja statements in dbt.
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Q1. The interviewer, who was part of the senior management, inquired about dbt, its features, and my experience with dbt thus far.
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Q2. The interviewer reviewed my resume and inquired about the challenges I encountered in my previous roles and the methods I used to resolve them.
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Q1. Salary negotiations, Bonus discussions
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I cannot recall the platform, but it featured a combination of Python and SQL questions, with the majority being focused on SQL.

Q1. SQL basics like Indexes, Clustering, Keys, Data warehousing concepts, Normalization and Denormalization etc..
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Q2. SQL coding questions
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Q1. SQL questions to solve using different types of joins
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Q2. Questions based on project and data quality checks
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Q1. Behavioral questions
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Q2. Culture fit
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Basic Pyspark/python question with some mcqs

Q1. Project overview
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Q2. Pyspark, python , sql related questions.
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Q1. Basic technical question on spark and one python Dasa question and one sql
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Q2. Sql + python + spark + Adf + Databricks
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Q1. Why you want to join even though you have offers from good mnc
Ans. 
I am passionate about data engineering and believe this company offers unique opportunities for growth and learning.
• Exciting projects and challenges at this company

• Opportunities for growth and learning in data engineering

• Alignment with company values and culture

• Potential for career advancement and development

• Interest in the industry or specific technologies used by the company
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Q2. Salary expectations and work culture
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A general round having mix of maths,aptitude,english,puzzle and quiz. level was easy

Q1. Tell about yourself
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Q2. What step would you take in this situatiion
Ans. 
I would analyze the situation, identify the root cause, and propose a solution based on data-driven insights.
• Analyze the data to understand the problem

• Identify the root cause of the issue

• Propose a solution based on data-driven insights
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Basic sql online assessment with some queries and mcq

Q1. Explain project
Ans. 
Developed a data pipeline to ingest, process, and analyze customer behavior data for targeted marketing campaigns.
• Designed and implemented ETL processes to extract data from various sources

• Utilized Apache Spark for data processing and analysis

• Built machine learning models to predict customer behavior

• Collaborated with marketing team to optimize campaign strategies
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Q2. Pipeline optimization
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Q1. Behavioral question
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