Ernst & Young Analyst Interview Questions, Process, and Tips

Spark has a master-slave architecture with a cluster manager and worker nodes.

• Spark has a driver program that communicates with a cluster manager to allocate resources and schedule tasks.

• Worker nodes execute tasks and return results to the driver program.

• Spark supports multiple cluster managers like YARN, Mesos, and standalone.

• Spark also has a DAG (Directed Acyclic Graph) scheduler that optimizes task execution.

• Spark's...read more

The PySpark query to find the sum and average using Spark DataFrames.

• Use the `groupBy` method to group the data by a specific column

• Use the `agg` method to apply aggregate functions like `sum` and `avg`

• Specify the column(s) to perform the aggregation on

Joining three tables using SQL

• Use the JOIN keyword to combine the tables based on a common column

• Specify the columns to select from each table

• Use the ON keyword to define the join condition

GroupBy is used to group data based on a column while Having is used to filter grouped data based on a condition.

• GroupBy is used with aggregate functions like SUM, COUNT, AVG, etc.

• Having is used to filter data after grouping based on a condition.

• GroupBy is used before Having in a SQL query.

• Example: SELECT department, COUNT(*) FROM employees GROUP BY department HAVING COUNT(*) > 5;

• Example: SELECT department, AVG(salary)...read more

Filtering data in PySpark based on a particular condition.

• Use the filter() function to filter data based on a condition.

• Conditions can be specified using logical operators such as ==, >, <, etc.

• Multiple conditions can be combined using logical operators such as and, or, not.

• Example: df.filter(df['age'] > 25).filter(df['gender'] == 'Male')

• This will filter the data where age is greater than 25 and gender is Male.

Columns can be added or removed from Spark dataframes using select() and drop() functions.

• To add a column, use withColumn() function and provide a name and expression for the new column.

• To remove a column, use drop() function and provide the name of the column to be removed.

• To select specific columns, use select() function and provide the names of the columns to be selected.

• Example: df.withColumn('new_col', df.col1 + d

I want to join EY because of its reputation for excellence, global presence, and opportunities for professional growth.

• EY is known for its high standards of excellence in the industry.

• EY has a strong global presence, which provides opportunities to work with diverse clients and gain international exposure.

• EY offers a wide range of services and sectors, allowing for professional growth and development.

• EY's collaborative...read more

I expect EY to provide a challenging and rewarding work environment with opportunities for growth and development.

• Opportunities for professional growth

• Challenging work assignments

• Supportive and collaborative team

• Clear communication and feedback

• Recognition for achievements

• Work-life balance

• Competitive compensation and benefits

Porter's Five Forces is a framework for analyzing the competitive forces in an industry.

• Porter's Five Forces include: threat of new entrants, bargaining power of buyers, bargaining power of suppliers, threat of substitute products, and intensity of competitive rivalry.

• It helps businesses understand the competitive environment they operate in and develop strategies to stay competitive.

• For example, in the airline industr...read more

Secondary research involves gathering information from existing sources such as books, articles, and reports.

• Involves analyzing and synthesizing existing data

• Does not involve collecting new data

• Sources can include books, articles, reports, and online databases

• Helps in gaining a deeper understanding of a topic

• Can be used to validate primary research findings