Capgemini Data Engineering Analyst Interview Questions and Answers

Sql, pyhton, azure databricks, azure data factory

Q1. Introduce your self and Explain Your Project and your Role?
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Q2. Explain Airflow with its Internal Architecture?
Ans. 
Airflow is a platform to programmatically author, schedule, and monitor workflows.
• Airflow is written in Python and uses Directed Acyclic Graphs (DAGs) to define workflows.

• It has a web-based UI for visualization and monitoring of workflows.

• Airflow consists of a scheduler, a metadata database, a web server, and an executor.

• Tasks in Airflow are defined as operators, which determine what actually gets executed.

• Example: A D...read more
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Q3. What is RDD in Spark?
Ans. 
RDD stands for Resilient Distributed Dataset in Spark, which is an immutable distributed collection of objects.
• RDD is the fundamental data structure in Spark, representing a collection of elements that can be operated on in parallel.

• RDDs are fault-tolerant, meaning they can automatically recover from failures.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (tr
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Q4. Define RDD Lineage and its Process
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RDD Lineage is the record of transformations applied to an RDD and the dependencies between RDDs.
• RDD Lineage tracks the sequence of transformations applied to an RDD from its source data.

• It helps in fault tolerance by allowing RDDs to be reconstructed in case of data loss.

• RDD Lineage is used in Spark to optimize the execution plan by eliminating unnecessary computations.

• Example: If an RDD is created from a text file an...read more
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Q5. What do you mean by broadcast Variables?
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Broadcast Variables are read-only shared variables that are cached on each machine in a Spark cluster rather than being sent with tasks.
• Broadcast Variables are used to efficiently distribute large read-only datasets to all worker nodes in a Spark cluster.

• They are useful for tasks that require the same data to be shared across multiple stages of a job.

• Broadcast Variables are created using the broadcast() method in Spark...read more
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Q6. What is Broadcasting are you using Broadcasting and what is the limitation of broadcasting?
Ans. 
Broadcasting is a technique used in Apache Spark to optimize data transfer by sending smaller data to all nodes in a cluster.
• Broadcasting is used to efficiently distribute read-only data to all nodes in a cluster to avoid unnecessary data shuffling.

• It is commonly used when joining large datasets with smaller lookup tables.

• Broadcast variables are cached in memory and reused across multiple stages of a Spark job.

• The limi...read more
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Q7. Are you using acumulator and Explain cathelyst optimizer
Ans. 
Accumulators are used for aggregating values across tasks, while Catalyst optimizer is a query optimizer for Apache Spark.
• Accumulators are variables that are only added to through an associative and commutative operation and can be used to implement counters or sums.

• Catalyst optimizer is a rule-based query optimizer that leverages advanced programming language features to build an extensible query optimizer.

• Catalyst op...read more
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Q8. Suppose you adding a block and that takes much time you have to debug it how you start the debug ?
Ans. 
To debug a slow block, start by identifying potential bottlenecks, analyzing logs, checking for errors, and profiling the code.
• Identify potential bottlenecks in the code or system that could be causing the slow performance.

• Analyze logs and error messages to pinpoint any issues or exceptions that may be occurring.

• Use profiling tools to analyze the performance of the code and identify areas that need optimization.

• Check f...read more
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Q9. You have to 200 Petabyte of data to load how you will decide the number of executor required ?out of cache you have
Ans. 
The number of executors required to load 200 Petabytes of data depends on the size of each executor and the available cache.
• Calculate the size of each executor based on available resources and data size

• Consider the amount of cache available for data processing

• Determine the optimal number of executors based on the above factors
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Q10. What is prepartition ?
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Prepartition is the process of dividing data into smaller partitions before performing any operations on it.
• Prepartitioning helps in improving query performance by reducing the amount of data that needs to be processed.

• It can also help in distributing data evenly across multiple nodes in a distributed system.

• Examples include partitioning a large dataset based on a specific column like date or region before running anal
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Q11. Sql Query Table Name Employee column Employee name Salary Department first read this csv file and then write the query in pyspark to find out the name of the employee whose salary is 2nd highest in eac...read more
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Q12. Suppose you have string values now you have to find out the frequency of values ? For Example like input ['a' ,'a' ,'a', 'b', 'b', 'c' ] output a,3 b,2 c,1
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Q13. What is case classes in python ?
Ans. 
Case classes in Python are classes that are used to create immutable objects for pattern matching and data modeling.
• Case classes are typically used in functional programming to represent data structures.

• They are immutable, meaning their values cannot be changed once they are created.

• Case classes automatically define equality, hash code, and toString methods based on the class constructor arguments.

• They are commonly use...read more
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Q14. Suppose there is 100 column in a file i just want to only load 10 column from 100 column how you approach this?
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Q15. What is lambda Architecture and lambda function?
Ans. 
Lambda Architecture is a data processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods. Lambda function is a small anonymous function that can take any number of arguments, but can only have one expression.
• Lambda Architecture combines batch processing and stream processing to handle large amounts of data efficiently.

• Batch layer stores and proc...read more
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Q1. There was only one technical round and questions where from SQL number joins questions and tool related questions
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Q1. CTC discussion with HR and offer Letter was released after submitting all documents
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Reasoning,logical, grammatical

Q1. Self introduction
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Q2. Academic Project explanation
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Developed a data analysis tool to predict customer churn using machine learning algorithms.
• Used Python for data preprocessing and model building

• Implemented logistic regression and random forest algorithms

• Evaluated model performance using metrics like accuracy, precision, and recall
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2 questions on basics of DS and algo. easy and medium level included.

Q1. Basic concept of OOP, data types in python , C
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Q2. Explain your personal project briefly
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Aptitude questions, verbal test and pseudocode.

Q1. About Myself and my life
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Q2. About college project and academics
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Q3. About leadership skills in college
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Q4. About my skills sets
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Q5. About my aspirations on career
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That was great and easy

Gave 2 codes
Difficult level is medium

Q1. Ask about project
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