Tesco Data Engineer Interview Questions and Answers

Q1. Wxaplinnyour project
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Q2. How would you build a pipeline to connect http source and bring data in adls
Ans. 
Build a pipeline to connect http source and bring data in adls
• Set up a data ingestion tool like Apache NiFi or Azure Data Factory to pull data from the http source

• Transform the data as needed using tools like Apache Spark or Azure Databricks

• Store the data in Azure Data Lake Storage (ADLS) for further processing and analysis
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Q3. Dimension and fact tabel difference
Ans. 
Dimension tables store descriptive attributes while fact tables store quantitative data.
• Dimension tables contain attributes that describe the business entities, such as customer, product, or time.

• Fact tables contain quantitative data or metrics that are typically numeric and additive, such as sales revenue or quantity sold.

• Dimension tables are denormalized and have a one-to-many relationship with fact tables.

• Fact table...read more
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Q4. Write slq query to get employee having 2manager
Ans. 
Use a self join to find employees with 2 managers in SQL query.
• Use a self join on the employee table to join the table with itself.

• Count the number of distinct managers for each employee.

• Filter the results to only include employees with 2 managers.
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Q5. Get second highest salary usingSQL
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Q1. Have you worked in databricks
Ans. 
Yes, I have worked in Databricks for data engineering projects.
• I have experience using Databricks for data processing and analysis.

• I have utilized Databricks for building and optimizing data pipelines.

• I have worked with Databricks notebooks for coding and collaboration.

• I have used Databricks clusters for scalable data processing.

• I have integrated Databricks with other tools like Apache Spark for big data processing.
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Q1. About pyspark questions
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Q2. About etl tools
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Q1. Clueless and very rude interviewer. The troglodyte asked only from my past experience and everything not included either in the job description or my resume.
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Q1. Python coding, SQL theoretical
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Q2. SPARK working question
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Sql questions and python logics

Q1. About sql and python
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Q2. Hadoop and kinit
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Q1. About partitions and bucketing Spark optimization Question from hadoop All questions were scenario based
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Q1. I was asked medium Data structure questions mainly on Arrays and strings.
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Q1. This round was basically on big data concepts, interviewer asked about Hive, Hbase, Spark and SQL.
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Q1. This was a managerial round mostly focused on technical part for the first half, than some bit on optimisation of Big data application as well as infra overall. Than some basic managerial questions.
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Q1. This round was about basic HR questions, such as why Target, salary discussion etc.
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