EY Global Delivery Services ( EY GDS) Senior Analyst Interview Questions, Process, and Tips

Q1. Explain Spark architecture
Ans. 
Spark architecture is a distributed computing framework that allows processing of large datasets in parallel across a cluster of computers.
• Spark has a master-slave architecture with a driver program that communicates with the cluster manager to allocate resources and schedule tasks.

• The cluster manager can be YARN, Mesos or Spark's own standalone cluster manager.

• Spark applications are written in high-level APIs like Spa...read more
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Q2. Different joins in spark
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Different types of joins in Spark include inner join, left outer join, right outer join, full outer join, and cross join.
• Inner join returns only the matching records from both tables

• Left outer join returns all records from the left table and matching records from the right table

• Right outer join returns all records from the right table and matching records from the left table

• Full outer join returns all records from both...read more
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Q3. Coalesce and repartition
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Q4. Cache and types of cache
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Cache is a hardware or software component that stores data to reduce access time and improve performance.
• Cache is used to store frequently accessed data closer to the processor for faster access.

• There are three types of cache: L1, L2, and L3.

• L1 cache is the smallest and fastest, while L3 cache is the largest and slowest.

• Cache can be found in CPUs, hard drives, and web browsers.

• Cache can be cleared to free up space or r
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Q5. Difference between Hadoop and spark
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Hadoop is a distributed storage and processing system while Spark is a fast and general-purpose cluster computing system.
• Hadoop is based on MapReduce programming model while Spark uses Resilient Distributed Datasets (RDDs)

• Spark is faster than Hadoop due to in-memory processing

• Hadoop is better suited for batch processing while Spark is better for iterative processing and real-time data processing

• Hadoop has a complex eco...read more
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Q1. Explain Spark architecture
Ans. 
Spark architecture is a distributed computing framework that allows processing of large datasets in parallel.
• Spark has a master-slave architecture with a driver program that controls the execution of tasks on worker nodes.

• It uses Resilient Distributed Datasets (RDDs) for data processing and storage.

• Spark supports various programming languages such as Java, Scala, Python, and R.

• It has a built-in module for SQL and data ...read more
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Q2. Broadcast join and shuffle join
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Q3. Window function with example
Ans. 
Window function is used to perform calculations on a specific subset of data in a table.
• Window function operates on a set of rows and returns a single value for each row.

• It is used to calculate running totals, moving averages, and rank functions.

• Examples include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().
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Q4. Spark streaming concepts and live data processing and analytics
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Q5. AWS lambda function and its limitations
Ans. 
AWS Lambda is a serverless computing service that has some limitations.
• AWS Lambda has a maximum execution time of 15 minutes.

• It has a maximum memory allocation of 3GB.

• It has a maximum package size of 50MB.

• It has a cold start problem, which can cause latency issues.

• It has limited control over the underlying infrastructure.

• It has limited support for languages other than Node.js, Python, Java, C#, and Go.
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Q6. What is elastic load balancer
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Elastic Load Balancer is a service provided by AWS to distribute incoming traffic across multiple targets.
• ELB automatically scales to meet traffic demands

• It improves the availability and fault tolerance of applications

• ELB supports multiple protocols and SSL offloading

• It can be used with EC2 instances, containers, and IP addresses

• Examples of ELB include Application Load Balancer and Network Load Balancer
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Q7. How can performance of a spark job improved
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Performance of a Spark job can be improved by optimizing resources, tuning configurations, and optimizing code.
• Optimize resources by increasing memory and CPU cores

• Tune configurations such as executor memory, shuffle partitions, and parallelism

• Optimize code by reducing data shuffling, using broadcast variables, and caching data

• Use appropriate data storage formats such as Parquet or ORC

• Use appropriate cluster management...read more
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Q1. Questions on current job profile and other exposure on live deals.
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Q1. Financial Modelling, Valuation
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Psychometric test through some 3rd party app

Q1. Salary negotiation and joining details
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All numeric ability. logical thinking etc

Topic-social media boon or bane

Q1. Tell me abt yourself
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Q1. Tell me about yourself
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Q1. KPIs, ebitda and related questions, working capital related questions.
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Q2. M&A related adjustments questions
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