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Accenture Data Engineering Analyst Interview Questions and Answers for Experienced

Updated 29 Dec 2024

14 Interview questions

A Data Engineering Analyst was asked
Q. Suppose you are adding a block that takes a significant amount of time. How would you start debugging it?
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.

  • Ch...

A Data Engineering Analyst was asked
Q. Explain Airflow and 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...

Data Engineering Analyst Interview Questions Asked at Other Companies for Experienced

asked in Accenture
Q1. Given an Employee table with columns Employee name, Salary, and D ... read more
asked in Accenture
Q2. You have 200 Petabytes of data to load. How will you decide the n ... read more
asked in Accenture
Q3. Suppose there is a file with 100 columns, and you only want to lo ... read more
asked in Accenture
Q4. Given a list of strings, how would you determine the frequency of ... read more
asked in Accenture
Q5. Suppose you are adding a block that takes a significant amount of ... read more
A Data Engineering Analyst was asked
Q. What is pre-partitioning?
Ans. 

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...

A Data Engineering Analyst was asked
Q. You have 200 Petabytes of data to load. How will you decide the number of executors required, considering the data is out of cache?
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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A Data Engineering Analyst was asked
Q. What are 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 commonl...

A Data Engineering Analyst was asked
Q. What is an 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 action...

A Data Engineering Analyst was asked
Q. Given an Employee table with columns Employee name, Salary, and Department, write a PySpark query to find the name of the employee with the second highest salary in each department.
Ans. 

Find the 2nd highest salary employee in each department using PySpark.

  • Read the CSV file into a DataFrame using spark.read.csv().

  • Group the DataFrame by 'Department' and use the 'dense_rank()' function to rank salaries.

  • Filter the DataFrame to get employees with a rank of 2.

  • Select the 'Employee name' and 'Department' columns for the final output.

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A Data Engineering Analyst was asked
Q. Define RDD Lineage and its process.
Ans. 

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 fi...

A Data Engineering Analyst was asked
Q. What do you mean by broadcast variables?
Ans. 

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 ...

A Data Engineering Analyst was asked
Q. Given a list of strings, how would you determine the frequency of each unique string value? For example, given the input ['a', 'a', 'a', 'b', 'b', 'c'], the expected output is a:3, b:2, c:1.
Ans. 

Calculate the frequency of each unique string in an array and display the results.

  • Use a dictionary to count occurrences: {'a': 3, 'b': 2, 'c': 1}.

  • Iterate through the list and update counts for each character.

  • Example: For input ['a', 'a', 'b'], output should be 'a,2' and 'b,1'.

  • Utilize collections.Counter for a more concise solution.

Accenture Data Engineering Analyst Interview Experiences for Experienced

5 interviews found

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Aug 2023.Ā There were 2 interview rounds.

Round 1 - Resume ShortlistĀ 
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 - TechnicalĀ 

(15 Questions)

  • Q1. Introduce your self and Explain Your Project and your Role?
  • 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...

  • Answered by AI
  • 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...

  • Answered by AI
  • Q4. Define RDD Lineage and its Process
  • Ans. 

    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...

  • Answered by AI
  • Q5. What do you mean by broadcast Variables?
  • Ans. 

    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...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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

  • Answered by AI
  • Q10. What is prepartition ?
  • Ans. 

    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...

  • Answered by AI
  • 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...
  • Ans. 

    Find the 2nd highest salary employee in each department using PySpark.

    • Read the CSV file into a DataFrame using spark.read.csv().

    • Group the DataFrame by 'Department' and use the 'dense_rank()' function to rank salaries.

    • Filter the DataFrame to get employees with a rank of 2.

    • Select the 'Employee name' and 'Department' columns for the final output.

  • Answered by AI
  • 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
  • Ans. 

    Calculate the frequency of each unique string in an array and display the results.

    • Use a dictionary to count occurrences: {'a': 3, 'b': 2, 'c': 1}.

    • Iterate through the list and update counts for each character.

    • Example: For input ['a', 'a', 'b'], output should be 'a,2' and 'b,1'.

    • Utilize collections.Counter for a more concise solution.

  • Answered by AI
  • 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...

  • Answered by AI
  • Q14. Suppose there is 100 column in a file i just want to only load 10 column from 100 column how you approach this?
  • Ans. 

    To load specific columns from a file, use data processing tools to filter the required columns efficiently.

    • Use libraries like Pandas in Python: `df = pd.read_csv('file.csv', usecols=['col1', 'col2', ...])`.

    • In SQL, you can specify columns in your SELECT statement: `SELECT col1, col2 FROM table_name;`.

    • For CSV files, tools like awk can be used: `awk -F, '{print $1,$2,...}' file.csv`.

    • In ETL processes, configure the extract...

  • Answered by AI
  • 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...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare more around Pyspark and SQL

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding TestĀ 

Coding in python use many tools scikit learn dashboarding such as tableau additionally I am skilled in ML

Interview Preparation Tips

Interview preparation tips for other job seekers - Practice your skills and do better for the better tomorrow
Interview experience
3
Average
Difficulty level
-
Process Duration
More than 8 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed before Feb 2023.Ā There were 2 interview rounds.

Round 1 - TechnicalĀ 

(1 Question)

  • Q1. There was only one technical round and questions where from SQL number joins questions and tool related questions
Round 2 - HRĀ 

(1 Question)

  • Q1. CTC discussion with HR and offer Letter was released after submitting all documents
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Resume ShortlistĀ 
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 - Coding TestĀ 

2 questions on basics of DS and algo. easy and medium level included.

Round 3 - TechnicalĀ 

(2 Questions)

  • Q1. Basic concept of OOP, data types in python , C
  • Q2. Explain your personal project briefly

Interview Preparation Tips

Interview preparation tips for other job seekers - DA ,algo basics, along with the personal project is enough
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Mar 2023.Ā There were 3 interview rounds.

Round 1 - Aptitude TestĀ 

That was great and easy

Round 2 - Coding TestĀ 

Gave 2 codes
Difficult level is medium

Round 3 - TechnicalĀ 

(1 Question)

  • Q1. Ask about project

Interview questions from similar companies

Interview QuestionnaireĀ 

2 Questions

  • Q1. About SAP ABAP and some technical staff
  • Q2. WRICEF , ABAP on HANA , CDS view , AMDP , O-data basics etc.

Interview Preparation Tips

Interview preparation tips for other job seekers - Be Confident , and have a self believe what you are answering . And don't give any answer that you are not sure , and you just say - I don't know . and later once the interview is google those question whose answer was not aware to you.

Senior Analyst Interview Questions & Answers

Capgemini user image NITIN KSHIRSAGAR

posted on 10 Sep 2020

I applied via Naukri.com and was interviewed before Sep 2019.Ā There were 3 interview rounds.

Interview QuestionnaireĀ 

4 Questions

  • Q1. Technology Specific Questions like Java, Salesforce
  • Q2. OOPS Concepts
  • Q3. Spring Framework
  • Q4. Previous Project Related

Interview Preparation Tips

Interview preparation tips for other job seekers - Try to be at the top in a field for which you are preparing for an interview. Clear all your basics. Knowledge is what will bring confidence while facing any kind of Interview. Believe in yourself and your efforts and try to be honest all the time

I applied via Referral and was interviewed before Oct 2021.Ā There were 2 interview rounds.

Round 1 - Resume ShortlistĀ 
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 - HRĀ 

(3 Questions)

  • Q1. General Question on process
  • Q2. Common self introduction
  • Q3. Salary expectation and are you ready for rotational shifts

Interview Preparation Tips

Interview preparation tips for other job seekers - Normal Questions... If fresher they will ask how many years u will be with us..

For experienced salary expectation and previous job roles and responsibilities.

Interview QuestionnaireĀ 

1 Question

  • Q1. About IAM process, tecnical bg about IAM

I applied via Naukri.com and was interviewed in Feb 2021.Ā There was 1 interview round.

Interview QuestionnaireĀ 

1 Question

  • Q1. Tell you about 5 point organization level qualities?

Interview Preparation Tips

Interview preparation tips for other job seekers - Right time to switch over the organization.
Prepare well attend multiple interview.

Accenture Interview FAQs

How many rounds are there in Accenture Data Engineering Analyst interview for experienced candidates?
Accenture interview process for experienced candidates usually has 2-3 rounds. The most common rounds in the Accenture interview process for experienced candidates are Technical, Coding Test and Resume Shortlist.
What are the top questions asked in Accenture Data Engineering Analyst interview for experienced candidates?

Some of the top questions asked at the Accenture Data Engineering Analyst interview for experienced candidates -

  1. Sql Query Table Name Employee column Employee name Salary Department first r...read more
  2. You have to 200 Petabyte of data to load how you will decide the number of exe...read more
  3. Suppose there is 100 column in a file i just want to only load 10 column from 1...read more

Tell us how to improve this page.

Overall Interview Experience Rating

3.2/5

based on 5 interview experiences

Difficulty level

Moderate 100%

Duration

Less than 2 weeks 67%
More than 8 weeks 33%
View more

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Accenture Data Engineering Analyst Salary
based on 2.9k salaries
₹5 L/yr - ₹11 L/yr
9% less than the average Data Engineering Analyst Salary in India
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Accenture Data Engineering Analyst Reviews and Ratings

based on 227 reviews

3.8/5

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3.9

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3.6

Work-life balance

3.1

Salary

3.8

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3.8

Company culture

2.7

Promotions

3.5

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