Global Market Insights Senior Data Engineer Interview Questions and Answers for Experienced

Q1. Mostly on your experience. Azure, .net core, microservices.
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Q2. Coding round with basic programming questions. Easy level.
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Q1. Basic concept of c#,.net framework
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Q2. What is CFA and DFA in entity framework core
Ans. 
CFA and DFA are Change-Tracking and Database-First Approaches in Entity Framework Core.
• CFA (Change-Tracking Approach) is used to track changes made to entities in memory and then save them to the database.

• DFA (Database-First Approach) is used to generate entity classes and DbContext from an existing database schema.

• CFA is useful when working with disconnected entities, while DFA is useful when working with an existing ...read more
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Q1. Basic questions
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Two coding questions, one is easy and the other is difficult. Who solved 2nd question was shortlisted for an interview.

Q1. Introduce yourself—questions from OS, DBMS, DSA, Project, and one coding problem.
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Excel assignment look up assignments

Q1. Questions regarding my previous work experience.
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1 coding question, and basic MCQ

The assignment was an excel assignment to check the advance excel efficiency of the interviewee

Q1. What is scd in dw?
Ans. 
SCD stands for Slowly Changing Dimension in Data Warehousing.
• SCD is a technique used in data warehousing to track changes to dimension data over time.

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

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

• Example: In a customer dimension table, if a customer chan...read more
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Q2. Inferschema in pyspark when reading file
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inferschema in pyspark is used to automatically infer the schema of a file when reading it.
• inferschema is a parameter in pyspark that can be set to true when reading a file to automatically infer the schema based on the data

• It is useful when the schema of the file is not known beforehand

• Example: df = spark.read.csv('file.csv', header=True, inferSchema=True)
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Q3. Triplet program
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Q4. Rank vs dense rank
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Rank assigns unique ranks to each distinct value, while dense rank assigns ranks without gaps.
• Rank function assigns unique ranks to each distinct value in a result set.

• Dense rank function assigns ranks to rows in a result set without any gaps between the ranks.

• Rank function may skip ranks if there are ties in values, while dense rank will not skip ranks.
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Q5. Some numbers gave how many rows for inner join,left,right,full join
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Q6. Read parquet file and remove duplicates
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Q7. Optimizing techniques in spark
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Optimizing techniques in Spark involve partitioning, caching, and tuning resources for efficient data processing.
• Use partitioning to distribute data evenly across nodes for parallel processing

• Cache frequently accessed data in memory to avoid recomputation

• Tune resources such as memory allocation and parallelism settings for optimal performance
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Q8. Repartition vs coalesce
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Repartition is used to increase the number of partitions in a DataFrame, while coalesce is used to decrease the number of partitions.
• Repartition involves shuffling data across the network, which can be expensive in terms of performance and resources.

• Coalesce is a more efficient operation as it minimizes data movement by only merging existing partitions.

• Repartition is typically used when there is a need for more paralle...read more
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Q9. Normalization in db and types
Ans. 
Normalization in databases is the process of organizing data in a database to reduce redundancy and improve data integrity.
• Normalization is used to eliminate redundant data and ensure data integrity.

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

• There are different normal forms such as 1NF, 2NF, 3NF, and BCNF.

• Normalization helps in reducing data redundancy and improving qu...read more
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Q10. Transformation vs action
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Transformation involves changing the data structure, while action involves performing a computation on the data.
• Transformation changes the data structure without executing any computation

• Action performs a computation on the data and triggers the execution

• Examples of transformation include map, filter, and reduce in Spark or Pandas

• Examples of action include count, collect, and saveAsTextFile in Spark
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