KPMG India Interview Questions, Process, and Tips

Q1. Top and Bottom in one sheet
Ans. 
Use LOD calculations to display top and bottom values in one sheet in Tableau.
• Use Level of Detail (LOD) calculations to create separate calculated fields for top and bottom values.

• Use these calculated fields in the same sheet to display both top and bottom values.

• For example, create a calculated field for top values using {FIXED [Dimension]: MAX([Measure])} and another for bottom values using {FIXED [Dimension]: MIN([M
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Q2. Usage of parameter and filters
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Parameters and filters are key features in Tableau for dynamic data analysis and visualization.
• Parameters allow users to input values that can be used in calculations, filters, and reference lines.

• Filters are used to limit the data displayed in a visualization based on specified criteria.

• Parameters can be used to create dynamic filters that allow users to interact with the data in real-time.

• Combining parameters and fil...read more
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Q3. Types of LOD and its use case
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Types of LOD include Fixed, Include, and Exclude LOD calculations for different use cases.
• Fixed LOD: Used to ignore the level of detail in the view and compute an aggregate value.

• Include LOD: Used to compute an aggregate value at a higher level of detail than the view.

• Exclude LOD: Used to compute an aggregate value at a lower level of detail than the view.
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Q4. Performance Optimization
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Q1. Project Overview and Size
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Q2. Performance Optimization
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Q3. Open for work in different technologies
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Q1. Introduction and Role
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Q2. Tableau Prep and its usage
Ans. 
Tableau Prep is a data preparation tool that allows users to clean, combine, and shape data for analysis in Tableau.
• Tableau Prep helps in cleaning and transforming data before visualizing it in Tableau Desktop.

• It allows users to easily combine multiple data sources and perform data cleaning tasks like removing duplicates, splitting columns, and aggregating data.

• Tableau Prep also provides a visual interface for creating...read more
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Q3. Snowflake vs Star Schema
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Snowflake schema is a normalized form of Star schema with multiple levels of dimension tables.
• Snowflake schema has normalized dimension tables, while Star schema has denormalized dimension tables.

• Snowflake schema is more complex and requires more joins compared to Star schema.

• Star schema is easier to understand and query, making it more suitable for smaller datasets.

• Snowflake schema is more suitable for larger datasets
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Q1. What is Linear regression
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Q2. What li list and tuple
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Q3. Previous company project experience
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Some multiple choice, 2 sql and 2 python questions were asked

Q1. Tell me about you project
Ans. 
Developed a real-time data processing system for analyzing customer behavior
• Used Apache Kafka for streaming data ingestion

• Implemented data pipelines using Apache Spark for processing and analysis

• Utilized Elasticsearch for storing and querying large volumes of data

• Developed custom machine learning models for predictive analytics
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Q2. Optimising technique that you have used
Ans. 
I have used partitioning and indexing to optimize query performance.
• Implemented partitioning on large tables to improve query performance by limiting the data scanned

• Created indexes on frequently queried columns to speed up data retrieval

• Utilized clustering keys to physically organize data on disk for faster access
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Q1. Window partition question was asked
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Q2. Project related question
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Q1. Write a program to print a pattern.
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Q2. Wite a program to check fibonaaci.
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Q3. Write a program to find a subarray
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Q1. What steps are involved in fetching data from an on-premises Unix server?
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Q2. Types of triggers in azure data factory
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