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LTIMindtree Junior Data Analyst Interview Questions and Answers

Updated 1 Jul 2024

LTIMindtree Junior Data Analyst Interview Experiences

1 interview found

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is duel axis
  • Ans. 

    Dual axis is a feature in data visualization where two different scales are used on the same chart to represent two different data sets.

    • Dual axis allows for comparing two different measures on the same chart

    • Each measure is assigned to its own axis, allowing for easy comparison

    • Commonly used in tools like Tableau for creating more complex visualizations

  • Answered by AI
  • Q2. What is scatter plot
  • Ans. 

    A scatter plot is a type of data visualization that displays the relationship between two numerical variables through dots on a graph.

    • Scatter plots are used to identify patterns and relationships between variables.

    • Each dot on the plot represents a single data point with the x-axis representing one variable and the y-axis representing the other variable.

    • The pattern of the dots can indicate the strength and direction of ...

  • Answered by AI
Round 2 - One-on-one 

(2 Questions)

  • Q1. What is blending
  • Ans. 

    Blending is the process of combining multiple data sources or datasets to create a unified view.

    • Blending involves merging data from different sources to gain insights or make decisions.

    • It helps in creating a comprehensive dataset by combining relevant information from various sources.

    • Blending can be done using tools like Tableau, Power BI, or Python libraries like Pandas.

    • For example, blending sales data from CRM with c...

  • Answered by AI
  • Q2. What is joining

Skills evaluated in this interview

Interview questions from similar companies

I applied via Naukri.com and was interviewed before Jul 2021. There were 2 interview rounds.

Round 1 - One-on-one 

(1 Question)

  • Q1. Details about current project and the issues faced
Round 2 - HR 

(1 Question)

  • Q1. About how to handle while production issues and some common HR questions

Interview Preparation Tips

Interview preparation tips for other job seekers - Apply through accenture website or tel anyone working in Accenture to refer

I applied via Company Website and was interviewed before Feb 2021. There were 5 interview rounds.

Round 1 - Coding Test 

It was technical MCQ with 60 questions, based Spark, Hive, Python, ML.

Round 2 - Coding Test 

Coding scenario like as - read xml, json using pyspark, flatten nested xml, json and basic data transformation related scenarios.

Round 3 - Technical 

(1 Question)

  • Q1. Window functions, optimization techniques etc.
Round 4 - Technical 

(1 Question)

  • Q1. Databricks, Pyspark level core technical concepts related architecture and internal working of functions with optimizations techniques.
Round 5 - HR 

(1 Question)

  • Q1. What are your salary expectations?

Interview Preparation Tips

Topics to prepare for Accenture Senior Data Engineer interview:
  • Python
  • Spark
  • Cloud Computing
  • Data Warehousing
Interview preparation tips for other job seekers - Need to have good understanding of core concepts of big data processing technologies and core working methodology of Data engineering with spark, python, databricks, data warehouse concepts.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

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

Round 1 - Technical 

(12 Questions)

  • Q1. Tell me about yourself and Project
  • Ans. 

    I am a Senior Data Engineer with experience in developing data pipelines and optimizing data storage for various projects.

    • Developed data pipelines using Apache Spark for real-time data processing

    • Optimized data storage using technologies like Hadoop and AWS S3

    • Worked on a project to analyze customer behavior and improve marketing strategies

  • Answered by AI
  • Q2. What was you day-to-day job in your project
  • Ans. 

    My day-to-day job in the project involved designing and implementing data pipelines, optimizing data workflows, and collaborating with cross-functional teams.

    • Designing and implementing data pipelines to extract, transform, and load data from various sources

    • Optimizing data workflows to improve efficiency and performance

    • Collaborating with cross-functional teams including data scientists, analysts, and business stakeholde...

  • Answered by AI
  • Q3. Spark Architecture
  • Q4. How DAG handle Fault tolerance?
  • Ans. 

    DAGs handle fault tolerance by rerunning failed tasks and maintaining task dependencies.

    • DAGs rerun failed tasks automatically to ensure completion.

    • DAGs maintain task dependencies to ensure proper sequencing.

    • DAGs can be configured to retry failed tasks a certain number of times before marking them as failed.

  • Answered by AI
  • Q5. What is shuffling? How to Handle Shuffling?
  • Ans. 

    Shuffling is the process of redistributing data across partitions in a distributed computing environment.

    • Shuffling is necessary when data needs to be grouped or aggregated across different partitions.

    • It can be handled efficiently by minimizing the amount of data being shuffled and optimizing the partitioning strategy.

    • Techniques like partitioning, combiners, and reducers can help reduce the amount of shuffling in MapRed

  • Answered by AI
  • Q6. What is the difference between repartition and Coelsce?
  • Ans. 

    Repartition increases or decreases the number of partitions in a DataFrame, while Coalesce only decreases the number of partitions.

    • Repartition can increase or decrease the number of partitions in a DataFrame, leading to a shuffle of data across the cluster.

    • Coalesce only decreases the number of partitions in a DataFrame without performing a full shuffle, making it more efficient than repartition.

    • Repartition is typically...

  • Answered by AI
  • Q7. How do you handle Incremental data?
  • Ans. 

    Incremental data is handled by identifying new data since the last update and merging it with existing data.

    • Identify new data since last update

    • Merge new data with existing data

    • Update data warehouse or database with incremental changes

  • Answered by AI
  • Q8. What is SCD ??
  • Ans. 

    SCD stands for Slowly Changing Dimension, a concept in data warehousing to track changes in data over time.

    • SCD is used to maintain historical data in a data warehouse.

    • There are three types of SCD - Type 1, Type 2, and Type 3.

    • Type 1 SCD overwrites old data with new data.

    • Type 2 SCD creates a new record for each change, preserving history.

    • Type 3 SCD maintains both old and new values in the same record.

    • SCD is important for...

  • Answered by AI
  • Q9. Scenerio based questions related to Spark ?
  • Q10. Two SQL Codes and Two Python codes like reverse a string ?
  • Ans. 

    Reverse a string using SQL and Python codes.

    • In SQL, use the REVERSE function to reverse a string.

    • In Python, use slicing with a step of -1 to reverse a string.

  • Answered by AI
  • Q11. Find top 5 countries with highest population in Spark and SQL
  • Ans. 

    Use Spark and SQL to find the top 5 countries with the highest population.

    • Use Spark to load the data and perform data processing.

    • Use SQL queries to group by country and sum the population.

    • Order the results in descending order and limit to top 5.

    • Example: SELECT country, SUM(population) AS total_population FROM table_name GROUP BY country ORDER BY total_population DESC LIMIT 5

  • Answered by AI
  • Q12. Using two tables find the different records for different joins
  • Ans. 

    To find different records for different joins using two tables

    • Use the SQL query to perform different joins like INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

    • Identify the key columns in both tables to join on

    • Select the columns from both tables and use WHERE clause to filter out the different records

  • Answered by AI
Round 2 - One-on-one 

(7 Questions)

  • Q1. What is a catalyst optimiser? How it works?
  • Ans. 

    A catalyst optimizer is a query optimization tool used in Apache Spark to improve performance by generating an optimal query plan.

    • Catalyst optimizer is a rule-based query optimization framework in Apache Spark.

    • It leverages rules to transform the logical query plan into a more optimized physical plan.

    • The optimizer applies various optimization techniques like predicate pushdown, constant folding, and join reordering.

    • By o...

  • Answered by AI
  • Q2. Tell me about the optimization you used in your project.
  • Ans. 

    Used query optimization techniques to improve performance in database queries.

    • Utilized indexing to speed up search queries.

    • Implemented query caching to reduce redundant database calls.

    • Optimized SQL queries by restructuring joins and subqueries.

    • Utilized database partitioning to improve query performance.

    • Used query profiling tools to identify and optimize slow queries.

  • Answered by AI
  • Q3. Pyspark question related to merging two schemas?
  • Q4. What is the best approach to finding whether the data frame is empty or not?
  • Ans. 

    Use the len() function to check the length of the data frame.

    • Use len() function to get the number of rows in the data frame.

    • If the length is 0, then the data frame is empty.

    • Example: if len(df) == 0: print('Data frame is empty')

  • Answered by AI
  • Q5. Spark Architecture
  • Q6. How do you decide on cores and worker nodes?
  • Ans. 

    Cores and worker nodes are decided based on the workload requirements and scalability needs of the data processing system.

    • Consider the size and complexity of the data being processed

    • Evaluate the processing speed and memory requirements of the tasks

    • Take into account the parallelism and concurrency needed for efficient data processing

    • Monitor the system performance and adjust cores and worker nodes as needed

  • Answered by AI
  • Q7. What happens when we enforce schema ?
  • Ans. 

    Enforcing schema ensures that data conforms to a predefined structure and rules.

    • Ensures data integrity by validating incoming data against predefined schema

    • Helps in maintaining consistency and accuracy of data

    • Prevents data corruption and errors in data processing

    • Can lead to rejection of data that does not adhere to the schema

  • Answered by AI

Interview Preparation Tips

Topics to prepare for Persistent Systems Senior Data Engineer interview:
  • SQL
  • Pyspark
  • Python
  • Spark
  • Database
Interview preparation tips for other job seekers - Be prepared with Spark core concepts and SQL Coding

Skills evaluated in this interview

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

I applied via Naukri.com and was interviewed before Jun 2023. There were 3 interview rounds.

Round 1 - One-on-one 

(2 Questions)

  • Q1. It’s general type of question
  • Q2. Experience n all
Round 2 - Group Discussion 

It’s just reasoning type questions.

Round 3 - Technical 

(2 Questions)

  • Q1. What is ssis? How we use
  • Ans. 

    SSIS stands for SQL Server Integration Services, a tool provided by Microsoft for data integration and workflow applications.

    • SSIS is a platform for building high-performance data integration and workflow solutions.

    • It allows you to create packages that move data from various sources to destinations.

    • SSIS includes a visual design interface for creating, monitoring, and managing data integration processes.

    • You can use SSIS ...

  • Answered by AI
  • Q2. When we use ssis packages? Difference between union merge
  • Ans. 

    SSIS packages are used for ETL processes in SQL Server. Union combines datasets vertically, while merge combines them horizontally.

    • SSIS packages are used for Extract, Transform, Load (ETL) processes in SQL Server.

    • Union in SSIS combines datasets vertically, stacking rows on top of each other.

    • Merge in SSIS combines datasets horizontally, matching rows based on specified columns.

    • Union All in SSIS combines datasets vertica...

  • Answered by AI

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Sql, spark, python released questions

Interview Preparation Tips

Interview preparation tips for other job seekers - I interviewed at HCL in april, 2024. There are two technical rounds. Mostly questions were on spark, python and sql. I cleared both the round. HR asked for documents for offer release. I shared as it was requested.

Now there is NO offer nor HR is responding even after dropping 1000 emails. This is frustrating as I am not sure what happened with my application.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

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

Round 1 - Technical 

(1 Question)

  • Q1. All medium level theory and SQL, python/pyspark code.
Round 2 - Technical 

(1 Question)

  • Q1. More in detail about tools and technologies, project handling, agile, CICD, scenario based.
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(3 Questions)

  • Q1. ReduceByKey vs groupByKey
  • Ans. 

    reduceByKey is more efficient than groupByKey for aggregating data in Spark due to reduced shuffling.

    • reduceByKey combines values for each key in each partition before shuffling data

    • groupByKey shuffles all data to a single partition before combining values for each key

    • reduceByKey is preferred for large datasets to minimize data movement and improve performance

  • Answered by AI
  • Q2. Word count in scala
  • Ans. 

    Scala provides a simple way to count words in a string using built-in functions.

    • Use the split function to split the string into an array of words

    • Use the length function to get the count of words in the array

  • Answered by AI
  • Q3. Second highest salary SQL
  • Ans. 

    Use SQL query with ORDER BY and LIMIT to find the second highest salary.

    • Use ORDER BY clause to sort salaries in descending order

    • Use LIMIT 1,1 to skip the first highest salary and get the second highest salary

  • Answered by AI

Skills evaluated in this interview

I applied via Naukri.com and was interviewed in Jul 2021. There were 4 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. As it was developer role so they asked performance turning practise in all Hadoop tool like hive, sqoop,spark etc.

Interview Preparation Tips

Interview preparation tips for other job seekers - There was 2 technical round. 1st was 45 min and 2nd was 30 min. As I am hadoop data engineer so they asked question from different Hadoop tool like spark hive scala. Suggestion is like U need to prepare whichever tool/technology u have mentioned in resume.
Interview experience
3
Average
Difficulty level
Easy
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed before Jul 2023. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. SQL basic join and window function
  • Q2. PySpark basic operations and spark basic

LTIMindtree Interview FAQs

How many rounds are there in LTIMindtree Junior Data Analyst interview?
LTIMindtree interview process usually has 2 rounds. The most common rounds in the LTIMindtree interview process are Technical and One-on-one Round.
What are the top questions asked in LTIMindtree Junior Data Analyst interview?

Some of the top questions asked at the LTIMindtree Junior Data Analyst interview -

  1. What is duel a...read more
  2. what is scatter p...read more
  3. what is blend...read more

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LTIMindtree Junior Data Analyst Interview Process

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