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Cognizant Big Data Engineer Interview Questions, Process, and Tips

Updated 12 Oct 2023

Cognizant Big Data Engineer Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed in Apr 2023. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - One-on-one 

(5 Questions)

  • Q1. What is speculative execution in Hadoop?
  • Ans. 

    Speculative execution in Hadoop is a feature that allows the framework to launch duplicate tasks for a job, with the goal of completing the job faster.

    • Speculative execution is used when a task is taking longer to complete than expected.

    • Hadoop identifies slow-running tasks and launches duplicate tasks on other nodes.

    • The first task to complete is used, while the others are killed to avoid duplication of results.

    • This help...

  • Answered by AI
  • Q2. Spark Architecture
  • Q3. Difference in list , tuple,sets
  • Ans. 

    Lists are mutable ordered collections, tuples are immutable ordered collections, and sets are mutable unordered collections.

    • Lists are mutable and ordered, allowing for duplicate elements. Example: [1, 2, 3, 3]

    • Tuples are immutable and ordered, allowing for duplicate elements. Example: (1, 2, 3, 3)

    • Sets are mutable and unordered, not allowing for duplicate elements. Example: {1, 2, 3}

  • Answered by AI
  • Q4. Difference in rank, dense rank in sql
  • Ans. 

    Rank assigns a unique rank to each distinct row, while dense rank assigns consecutive ranks to rows with the same values.

    • Rank function assigns unique ranks to each distinct row in the result set

    • Dense rank function assigns consecutive ranks to rows with the same values

    • Rank function leaves gaps in the ranking sequence if there are ties, while dense rank does not

  • Answered by AI
  • Q5. Add data into a partitioned hive table
  • Ans. 

    To add data into a partitioned hive table, you can use the INSERT INTO statement with the PARTITION clause.

    • Use INSERT INTO statement to add data into the table.

    • Specify the partition column values using the PARTITION clause.

    • Example: INSERT INTO table_name PARTITION (partition_column=value) VALUES (data);

  • Answered by AI
Round 3 - Coding Test 

WAP to add index wise elements of a list . A=[1,2,3] , B=[4,5,7] C should be [5,7,10]

Interview Preparation Tips

Topics to prepare for Cognizant Big Data Engineer interview:
  • Spark Architecture
  • Sql window functions
  • Python data structures
  • Spark and hive optimisation
  • Pyspark dataframe operations
Interview preparation tips for other job seekers - Understanding of spark Architecture is must . Even if you get stuck or don't understand the question you can confirm with them if your understanding is correct

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed in Aug 2024. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. All type of joins with example
  • Ans. 

    Different types of joins in SQL with examples

    • Inner Join: Returns rows when there is a match in both tables

    • Left Join: Returns all rows from the left table and the matched rows from the right table

    • Right Join: Returns all rows from the right table and the matched rows from the left table

    • Full Outer Join: Returns all rows when there is a match in either table

  • Answered by AI
  • Q2. How to handle large spark datasets
  • Ans. 

    Large Spark datasets can be handled by partitioning, caching, optimizing transformations, and tuning resources.

    • Partitioning data to distribute workload evenly across nodes

    • Caching frequently accessed data to avoid recomputation

    • Optimizing transformations to reduce unnecessary processing

    • Tuning resources like memory allocation and parallelism for optimal performance

  • Answered by AI
Round 2 - Technical 

(2 Questions)

  • Q1. How do you tune sparks configuration setting's to optimize query performance
  • Ans. 

    Spark configuration settings can be tuned to optimize query performance by adjusting parameters like memory allocation, parallelism, and caching.

    • Increase executor memory and cores to allow for more parallel processing

    • Adjust shuffle partitions to optimize data shuffling during joins and aggregations

    • Enable dynamic allocation to scale resources based on workload demands

    • Utilize caching to store intermediate results and avo...

  • Answered by AI
  • Q2. What strategies do you use to handle data skew and partition imbalance in spark
  • Ans. 

    To handle data skew and partition imbalance in Spark, strategies include using salting, bucketing, repartitioning, and optimizing join operations.

    • Use salting to evenly distribute skewed keys across partitions

    • Implement bucketing to pre-partition data based on a specific column

    • Repartition data based on a specific key to balance partitions

    • Optimize join operations by broadcasting small tables or using partitioning strategi

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
-

I applied via Company Website and was interviewed in Sep 2024. There was 1 interview round.

Round 1 - Technical 

(3 Questions)

  • Q1. Big Data fundamentals concepts on rdd, dataframe and datasets.
  • Q2. Spark optimization techniques
  • Ans. 

    Spark optimization techniques involve partitioning, caching, and tuning resource allocation.

    • Partitioning data to distribute workload evenly

    • Caching frequently accessed data to avoid recomputation

    • Tuning resource allocation for optimal performance

  • Answered by AI
  • Q3. SQL queries on windows function and joins.

Interview Preparation Tips

Interview preparation tips for other job seekers - Data Engineer fundamentals should be well prepared

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
-

I applied via Company Website and was interviewed in Apr 2024. There were 3 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. Mostly about spark topics like shuffeling joins, data skew, and pyspark scenarios
  • Q2. Mostly about python and sql scenarios
Round 2 - Behavioral 

(2 Questions)

  • Q1. About behavioural questions
  • Q2. Day to day work and workflow model based questions
Round 3 - HR 

(2 Questions)

  • Q1. Why you need to join infosys
  • Ans. 

    I want to join Infosys because of its reputation for innovation and growth opportunities.

    • Infosys is known for its cutting-edge technology solutions and innovative projects.

    • I am impressed by Infosys' commitment to employee development and career growth.

    • I believe that joining Infosys will provide me with the opportunity to work on challenging projects and enhance my skills.

  • Answered by AI
  • Q2. Self introduction

Interview Preparation Tips

Interview preparation tips for other job seekers - be prepare well in pyspark and sql, python and practise well in scenario based questions
Interview experience
2
Poor
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Company Website and was interviewed in Apr 2024. There were 3 interview rounds.

Round 1 - Coding Test 

Questions on software and system designs

Round 2 - Group Discussion 

Helps employer identify particular personality traits like leadership, confidence, interpersonal and teamwork skills of potential employees

Round 3 - One-on-one 

(2 Questions)

  • Q1. Why do you want this job? Again, companies want to hire people who are passionate about the job, so you should have a great answer about why you want the ...
  • Q2. This opportunity can help me in many ways to boost my confidence as well as drive my career forward

Interview Preparation Tips

Topics to prepare for Tech Mahindra Big Data Engineer interview:
  • The role of technology in educat
Interview preparation tips for other job seekers - Your search is likely to be much more successful if you approach it with initiative, creativity and a positive attitude. Nobody "owes" you a job – the future ...
Job Search Tips & Resources
Interview experience
2
Poor
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Job Portal and was interviewed in Sep 2023. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Aptitude Test 

Easy only so prepare well that's it

Round 3 - Technical 

(5 Questions)

  • Q1. What method you use
  • Ans. 

    I use a combination of programming languages, tools, and frameworks to analyze and process large datasets.

    • Utilize programming languages like Python, Java, or Scala for data processing

    • Leverage tools like Hadoop, Spark, or Kafka for distributed computing

    • Implement frameworks like MapReduce or Apache Flink for data analysis

    • Use SQL or NoSQL databases for data storage and retrieval

  • Answered by AI
  • Q2. Why you lleave current company
  • Q3. What skill you have
  • Q4. Tell about ur project
  • Q5. What you implemented
  • Ans. 

    Implemented a real-time data processing system using Apache Kafka and Spark for analyzing customer behavior.

    • Developed data pipelines to ingest, process, and analyze large volumes of data

    • Utilized Apache Kafka for real-time data streaming

    • Implemented machine learning algorithms for predictive analytics

    • Optimized data storage and retrieval for faster query performance

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Do well easy

Skills evaluated in this interview

Big Data Engineer Interview Questions & Answers

Capgemini user image Manshi Raghuvanshi

posted on 26 Jun 2024

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Coalesce vs repartition
  • Ans. 

    Coalesce is used to reduce the number of partitions in a DataFrame, while repartition is used to increase the number of partitions.

    • Coalesce is a narrow transformation that can only decrease the number of partitions without shuffling data.

    • Repartition is a wide transformation that can both increase or decrease the number of partitions and involves shuffling data across the cluster.

    • Coalesce is more efficient for reducing ...

  • Answered by AI
Round 2 - Coding Test 

Rank vs dense rank quetions ctes

Round 3 - Coding Test 

Python data structure

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed in Jun 2022. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
Round 2 - Technical 

(1 Question)

  • Q1. Scenario based questions
Round 3 - One-on-one 

(1 Question)

  • Q1. Project explanation , internal vs external table
  • Ans. 

    Internal tables are managed by Hive, while external tables are managed by the user.

    • Internal tables are stored in a Hive-managed warehouse directory, while external tables can be stored anywhere.

    • Internal tables are deleted when the table is dropped, while external tables are not.

    • External tables can be used to access data stored in non-Hive formats, such as CSV or JSON.

    • Internal tables are typically used for temporary or ...

  • Answered by AI
Round 4 - HR 

(1 Question)

  • Q1. What is your current salary and your expectations

Interview Preparation Tips

Topics to prepare for IBM Big Data Engineer interview:
  • Spark,Hive ,Scala
Interview preparation tips for other job seekers - Prepare basics well , Try to solve some real time questions on SQL,spark
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed before Oct 2022. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. Scala , Hadoop Spark, SQL, sqoop
  • Q2. Scala Hadoop Spark SQL sqoop
Round 2 - Technical 

(1 Question)

  • Q1. Scala Hadoop Spark SQL sqoop

Interview Preparation Tips

Interview preparation tips for other job seekers - Client

I applied via Recruitment Consultant and was interviewed in Jul 2021. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Basic concepts of big data technologies such as schemas on read and write

Interview Preparation Tips

Interview preparation tips for other job seekers - Go trough all the basic content

Cognizant Interview FAQs

How many rounds are there in Cognizant Big Data Engineer interview?
Cognizant interview process usually has 3 rounds. The most common rounds in the Cognizant interview process are Resume Shortlist, One-on-one Round and Coding Test.
How to prepare for Cognizant Big Data Engineer interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at Cognizant. The most common topics and skills that interviewers at Cognizant expect are Big Data, Spark, Hadoop, Hdfs and Hive.
What are the top questions asked in Cognizant Big Data Engineer interview?

Some of the top questions asked at the Cognizant Big Data Engineer interview -

  1. What is speculative execution in Hado...read more
  2. Difference in rank, dense rank in ...read more
  3. Add data into a partitioned hive ta...read more

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