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Happiest Minds Technologies Data Engineer Interview Questions and Answers

Updated 28 Aug 2024

Happiest Minds Technologies Data Engineer Interview Experiences

2 interviews found

Data Engineer Interview Questions & Answers

user image Nagaraj Purohit

posted on 28 Aug 2024

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

(5 Questions)

  • Q1. Data warehousing related questions
  • Q2. SQL scenario based questions
  • Q3. Project experience
  • Ans. 

    I have experience working on projects involving data pipeline development, ETL processes, and data warehousing.

    • Developed ETL processes to extract, transform, and load data from various sources into a data warehouse

    • Built data pipelines to automate the flow of data between systems and ensure data quality and consistency

    • Optimized database performance and implemented data modeling best practices

    • Worked on real-time data pro...

  • Answered by AI
  • Q4. Python Based questions
  • Q5. AWS features and questions
Round 2 - Technical 

(2 Questions)

  • Q1. Similar to first round but in depth questions relatively
  • Q2. Asked about career goals and stuff
Round 3 - HR 

(2 Questions)

  • Q1. General work related conversation
  • Q2. Salary discussion

Data Engineer Interview Questions & Answers

user image Anonymous

posted on 15 Oct 2023

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

I applied via Recruitment Consulltant and was interviewed in Apr 2023. There were 4 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 - Technical 

(1 Question)

  • Q1. SQL questions mostly
Round 3 - Technical 

(1 Question)

  • Q1. Azure data factory scenario based questions
Round 4 - Technical 

(1 Question)

  • Q1. Tough sql and Azure scenario based questions

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Interview questions from similar companies

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

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

Round 1 - One-on-one 

(2 Questions)

  • Q1. Azure Scenario based questions
  • Q2. Pyspark Coding based questions
Round 2 - One-on-one 

(2 Questions)

  • Q1. ADF, Databricks related question
  • Q2. Spark Performance problem and scenarios
  • Ans. 

    Spark performance problems can arise due to inefficient code, data skew, resource constraints, and improper configuration.

    • Inefficient code can lead to slow performance, such as using collect() on large datasets.

    • Data skew can cause uneven distribution of data across partitions, impacting processing time.

    • Resource constraints like insufficient memory or CPU can result in slow Spark jobs.

    • Improper configuration settings, su...

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
6-8 weeks
Result
Selected Selected
Round 1 - Technical 

(1 Question)

  • Q1. More on Technical area
Round 2 - Technical 

(1 Question)

  • Q1. More on Technical area
Round 3 - One-on-one 

(1 Question)

  • Q1. Technical + Behaviour
Round 4 - One-on-one 

(1 Question)

  • Q1. Technical + Behaviour
Round 5 - HR 

(1 Question)

  • Q1. Expectation and Genaral
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.

Round 1 - One-on-one 

(1 Question)

  • Q1. Diff between Coalesce and repatriation
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. SCD questions. Iceberg questions
  • Q2. Basic python programing, pyspark architechture.
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Data Structures
  • Q2. Python Lambda Function
Round 2 - HR 

(2 Questions)

  • Q1. What are your Strengths
  • Ans. 

    My strengths include strong analytical skills, attention to detail, and problem-solving abilities.

    • Strong analytical skills - able to analyze complex data sets and derive meaningful insights

    • Attention to detail - meticulous in ensuring data accuracy and quality

    • Problem-solving abilities - adept at identifying and resolving data-related issues

    • Experience with data manipulation tools like SQL, Python, and Spark

  • Answered by AI
  • Q2. Why you are looking for job change
  • Ans. 

    Seeking new challenges and growth opportunities in a different environment.

    • Looking for new challenges to enhance my skills and knowledge

    • Seeking growth opportunities that align with my career goals

    • Interested in exploring different technologies and industries

    • Want to work in a more collaborative team environment

    • Seeking better work-life balance or location proximity

  • Answered by AI
Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
2-4 weeks
Result
Not Selected

I applied via Referral and was interviewed in Feb 2024. There was 1 interview round.

Round 1 - Coding Test 

Just focus on the basics of pyspark.

I applied via Approached by Company and was interviewed in Nov 2021. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Normalisation of database, views,stored procedures
  • Ans. 

    Normalization is a process of organizing data in a database to reduce redundancy and improve data integrity.

    • Normalization involves breaking down a table into smaller tables and defining relationships between them.

    • It helps in reducing data redundancy and inconsistencies.

    • Views are virtual tables that are created based on the result of a query. They can be used to simplify complex queries.

    • Stored procedures are precompiled...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Nice interviews.bhgdgjnfdtubxrrikfdeijjgdryihffuijggiok
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Sep 2024. There was 1 interview round.

Round 1 - Technical 

(14 Questions)

  • Q1. How to create pipeline in adf?
  • Ans. 

    To create a pipeline in ADF, you can use the Azure Data Factory UI or code-based approach.

    • Use Azure Data Factory UI to visually create and manage pipelines

    • Use code-based approach with JSON to define pipelines and activities

    • Add activities such as data movement, data transformation, and data processing to the pipeline

    • Set up triggers and schedules for the pipeline to run automatically

  • Answered by AI
  • Q2. Diffrent types of activities in pipelines
  • Ans. 

    Activities in pipelines include data extraction, transformation, loading, and monitoring.

    • Data extraction: Retrieving data from various sources such as databases, APIs, and files.

    • Data transformation: Cleaning, filtering, and structuring data for analysis.

    • Data loading: Loading processed data into a data warehouse or database.

    • Monitoring: Tracking the performance and health of the pipeline to ensure data quality and reliab

  • Answered by AI
  • Q3. What is use of getmetadata
  • Ans. 

    getmetadata is used to retrieve metadata information about a dataset or data source.

    • getmetadata can provide information about the structure, format, and properties of the data.

    • It can be used to understand the data schema, column names, data types, and any constraints or relationships.

    • This information is helpful for data engineers to properly process, transform, and analyze the data.

    • For example, getmetadata can be used ...

  • Answered by AI
  • Q4. Diffrent types of triggers
  • Ans. 

    Triggers in databases are special stored procedures that are automatically executed when certain events occur.

    • Types of triggers include: DML triggers (for INSERT, UPDATE, DELETE operations), DDL triggers (for CREATE, ALTER, DROP operations), and logon triggers.

    • Triggers can be classified as row-level triggers (executed once for each row affected by the triggering event) or statement-level triggers (executed once for eac...

  • Answered by AI
  • Q5. Diffrence between normal cluster and job cluster in databricks
  • Ans. 

    Normal cluster is used for interactive workloads while job cluster is used for batch processing in Databricks.

    • Normal cluster is used for ad-hoc queries and exploratory data analysis.

    • Job cluster is used for running scheduled jobs and batch processing tasks.

    • Normal cluster is terminated after a period of inactivity, while job cluster is terminated after the job completes.

    • Normal cluster is more cost-effective for short-liv...

  • Answered by AI
  • Q6. What is slowly changing dimensions
  • Ans. 

    Slowly changing dimensions refer to data warehouse dimensions that change slowly over time.

    • SCDs are used to track historical changes in data over time.

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

    • Type 1 SCDs overwrite old data with new data, Type 2 creates new records for changes, and Type 3 maintains both old and new data in separate columns.

    • Example: A customer's address changing would be a Type 2 SCD.

    • Ex...

  • Answered by AI
  • Q7. Incremental load
  • Q8. With use in python
  • Ans. 

    Use Python's 'with' statement to ensure proper resource management and exception handling.

    • Use 'with' statement to automatically close files after use

    • Helps in managing resources like database connections

    • Ensures proper cleanup even in case of exceptions

  • Answered by AI
  • Q9. List vs tuple in python
  • Ans. 

    List is mutable, tuple is immutable in Python.

    • List can be modified after creation, tuple cannot be modified.

    • List uses square brackets [], tuple uses parentheses ().

    • Lists are used for collections of items that may need to be changed, tuples are used for fixed collections of items.

    • Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)

  • Answered by AI
  • Q10. Datalake 1 vs datalake2
  • Ans. 

    Datalake 1 and Datalake 2 are both storage systems for big data, but they may differ in terms of architecture, scalability, and use cases.

    • Datalake 1 may use a Hadoop-based architecture while Datalake 2 may use a cloud-based architecture like AWS S3 or Azure Data Lake Storage.

    • Datalake 1 may be more suitable for on-premise data storage and processing, while Datalake 2 may offer better scalability and flexibility for clou...

  • Answered by AI
  • Q11. How to read a file in databricks
  • Ans. 

    To read a file in Databricks, you can use the Databricks File System (DBFS) or Spark APIs.

    • Use dbutils.fs.ls('dbfs:/path/to/file') to list files in DBFS

    • Use spark.read.format('csv').load('dbfs:/path/to/file') to read a CSV file

    • Use spark.read.format('parquet').load('dbfs:/path/to/file') to read a Parquet file

  • Answered by AI
  • Q12. Star vs snowflake schema
  • Ans. 

    Star schema is denormalized with one central fact table surrounded by dimension tables, while snowflake schema is normalized with multiple related dimension tables.

    • Star schema is easier to understand and query due to denormalization.

    • Snowflake schema saves storage space by normalizing data.

    • Star schema is better for data warehousing and OLAP applications.

    • Snowflake schema is better for OLTP systems with complex relationsh

  • Answered by AI
  • Q13. Repartition vs coalesece
  • Ans. 

    repartition increases partitions while coalesce decreases partitions in Spark

    • repartition shuffles data and can be used for increasing partitions for parallelism

    • coalesce reduces partitions without shuffling data, useful for reducing overhead

    • repartition is more expensive than coalesce as it involves data movement

    • example: df.repartition(10) vs df.coalesce(5)

  • Answered by AI
  • Q14. Parquet file uses
  • Ans. 

    Parquet file format is a columnar storage format used for efficient data storage and processing.

    • Parquet files store data in a columnar format, which allows for efficient querying and processing of specific columns without reading the entire file.

    • It supports complex nested data structures like arrays and maps.

    • Parquet files are highly compressed, reducing storage space and improving query performance.

    • It is commonly used ...

  • Answered by AI

Skills evaluated in this interview

Happiest Minds Technologies Interview FAQs

How many rounds are there in Happiest Minds Technologies Data Engineer interview?
Happiest Minds Technologies interview process usually has 3-4 rounds. The most common rounds in the Happiest Minds Technologies interview process are Technical, Resume Shortlist and HR.
How to prepare for Happiest Minds Technologies 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 Happiest Minds Technologies. The most common topics and skills that interviewers at Happiest Minds Technologies expect are Python, Data Modeling, Hospitality, AWS and Analytical.
What are the top questions asked in Happiest Minds Technologies Data Engineer interview?

Some of the top questions asked at the Happiest Minds Technologies Data Engineer interview -

  1. Similar to first round but in depth questions relativ...read more
  2. Azure data factory scenario based questi...read more
  3. Tough sql and Azure scenario based questi...read more

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Happiest Minds Technologies Data Engineer Interview Process

based on 2 interviews

Interview experience

4.5
  
Good
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Happiest Minds Technologies Data Engineer Salary
based on 96 salaries
₹4.5 L/yr - ₹17.9 L/yr
At par with the average Data Engineer Salary in India
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Happiest Minds Technologies Data Engineer Reviews and Ratings

based on 11 reviews

3.5/5

Rating in categories

3.2

Skill development

3.6

Work-life balance

3.0

Salary

3.6

Job security

3.9

Company culture

3.0

Promotions

3.6

Work satisfaction

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