Anicca Data Science Solutions Data Engineer Interview Questions and Answers for Experienced

Q1. What is the SQL query to calculate the average sales over a period of 7 days?
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Q2. Explain Data bricks architecture?
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Q1. Basic Python, SQL & Spark coding questions.
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Q2. Nth highest salary, word count in pyspark
Ans. 
To find the nth highest salary in pyspark, use the window function with row_number and filter on the desired rank.
• Use window function with row_number to assign a rank to each salary

• Filter the result to get the row with the desired rank

• Example: df.withColumn('rank', F.row_number().over(Window.orderBy(F.col('salary').desc()))).filter(F.col('rank') == n).select('salary')
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Q1. Questions on SQL & PySpark.
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Q1. Questions about previous projects & optimization techniques etc. Also behavioural
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Q1. Salary negotiation part.
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Q1. Explain the architecture of delta lake
Ans. 
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
• Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

• It stores data in Parquet format and uses Apache Spark for processing.

• Delta Lake ensures data reliability and data quality by providing schema enforcement and data versioning.

• It supports time tra...read more
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Q2. Tuning operations in databricks
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Tuning operations in Databricks involves optimizing performance and efficiency of data processing tasks.
• Use cluster configuration settings to allocate resources efficiently

• Optimize code by minimizing data shuffling and reducing unnecessary operations

• Leverage Databricks Auto Optimize to automatically tune performance

• Monitor job performance using Databricks Runtime Metrics and Spark UI
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Q1. Normal questions on spark, hadoop, sql
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Q2. One python coding question was there
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Q1. Python coding felt difficult, its a moderate difficult question
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Q2. Questions on project, sql moderate questions
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Q1. Spark architecture
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Q1. Explain Spark RDD,DF
Ans. 
Spark RDD and DF are two data structures in Apache Spark for processing and analyzing data.
• RDD (Resilient Distributed Dataset) is a distributed collection of elements that can be operated on in parallel. It is immutable and fault-tolerant.

• DF (DataFrame) is a distributed collection of data organized into named columns. It provides a more structured and efficient way to work with data compared to RDDs.

• RDD is low-level an...read more
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Q2. Spark Architecture
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End to end data engineering architecture

Q1. Salary expectation
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Q2. Cultural fitment
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Q1. Mostly on Spark
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Q1. What is executor memory
Ans. 
Executor memory is the amount of memory allocated to each executor in a Spark application.
• Executor memory is specified using the 'spark.executor.memory' configuration property.

• It determines how much memory each executor can use to process tasks.

• It is important to properly configure executor memory to avoid out-of-memory errors or inefficient resource utilization.
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Q2. Difference between rdd and data frame
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RDD is a low-level abstraction in Spark representing distributed data, while DataFrames are higher-level structured APIs for working with data.
• RDD is an immutable distributed collection of objects, while DataFrames are distributed collection of data organized into named columns.

• RDDs are more suitable for unstructured data and low-level transformations, while DataFrames provide a more user-friendly API for structured da...read more
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