phData Associate Data Engineer Interview Questions and Answers

Q1. NLP based question
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Q1. SQL problems to solve nth highest salary
Ans. 
Use SQL query with subquery to find nth highest salary
• Use ORDER BY and LIMIT to get the nth highest salary

• Use a subquery to exclude the top n-1 salaries before selecting the nth highest salary
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Q1. GCP big query Architecture
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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
Ans. 
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
Ans. 
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
Ans. 
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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Basics of SQL, Python

Experience based questions, SQL and Python

Q1. Tell me about the projects you worked on
Ans. 
I have worked on projects involving building data pipelines, optimizing data storage, and developing machine learning models.
• Built data pipelines using Apache Spark and Airflow

• Optimized data storage by implementing partitioning and indexing strategies

• Developed machine learning models for predictive analytics
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Q2. Based on experience some questions related to projects.
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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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Q1. How to build data pipeline
Ans. 
Building a data pipeline involves extracting, transforming, and loading data from various sources to a destination for analysis.
• Identify data sources and determine the data to be collected

• Extract data from sources using tools like Apache NiFi or Apache Kafka

• Transform data using tools like Apache Spark or Python scripts

• Load data into a destination such as a data warehouse or database

• Schedule and automate the pipeline fo...read more
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Q2. IICS
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Q1. Tell me about yourself, second highest salary , question related to coleasce function
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