Ingram Micro Manager Interview Questions and Answers

Q1. What are joins and its type
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Q2. Tell me about Dax query
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Q1. Questions on Pyspark
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Q2. Questions on SQL
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Q3. Transformations
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Q4. Questions on Sql optimizations
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Q5. Questions About my current Project
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Q1. What is Pyspark?
Ans. 
Pyspark is a Python API for Apache Spark, a powerful open-source distributed computing system.
• Pyspark is used for processing large datasets in parallel across a cluster of computers.

• It provides high-level APIs in Python for Spark programming.

• Pyspark allows seamless integration with other Python libraries like Pandas and NumPy.

• Example: Using Pyspark to perform data analysis and machine learning tasks on big data sets.
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Q2. What is Pyspark SQL?
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Pyspark SQL is a module in Apache Spark that provides a SQL interface for working with structured data.
• Pyspark SQL allows users to run SQL queries on Spark dataframes.

• It provides a more concise and user-friendly way to interact with data compared to traditional Spark RDDs.

• Users can leverage the power of SQL for data manipulation and analysis within the Spark ecosystem.
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Q3. How to merge 2 dataframes of different schema?
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To merge 2 dataframes of different schema, use join operations or data transformation techniques.
• Use join operations like inner join, outer join, left join, or right join based on the requirement.

• Perform data transformation to align the schemas before merging.

• Use tools like Apache Spark, Pandas, or SQL to merge dataframes with different schemas.
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Q4. What is Pyspark streaming?
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Pyspark streaming is a scalable and fault-tolerant stream processing engine built on top of Apache Spark.
• Pyspark streaming allows for real-time processing of streaming data.

• It provides high-level APIs in Python for creating streaming applications.

• Pyspark streaming supports various data sources like Kafka, Flume, Kinesis, etc.

• It enables windowed computations and stateful processing for handling streaming data.

• Example: C...read more
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Q1. What joins you used
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I have used various types of joins including inner join, left join, right join, and full outer join.
• Used inner join to retrieve records that have matching values in both tables

• Utilized left join to retrieve all records from the left table and matching records from the right table

• Employed right join to retrieve all records from the right table and matching records from the left table

• Utilized full outer join to retrieve ...read more
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Q2. Write query for joins
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Query for joins in SQL to combine data from multiple tables
• Use JOIN keyword to combine data from two or more tables based on a related column

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• Example: SELECT * FROM table1 INNER JOIN table2 ON table1.id = table2.id
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Just focus on the basics of pyspark.

There are one hour exam

Q1. Advance level of sql and python
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Advanced level of SQL and Python skills are essential for a Data Engineer role.
• Strong understanding of SQL queries, joins, subqueries, and optimization techniques.

• Proficiency in writing complex Python scripts for data manipulation, analysis, and automation.

• Experience with data modeling, ETL processes, and working with large datasets.

• Knowledge of data warehousing concepts and tools like SQL Server, PostgreSQL, or Snowfl...read more
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Q1. Basic of SQL,some join condition
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Q2. Basic of python
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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...read more
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Easy questions related to DSA and Python and SQL

Q1. Query to join two table using different joins
Ans. 
Query to join two tables using different joins
• Use INNER JOIN to return rows that have matching values in both tables

• Use LEFT JOIN to return all rows from the left table and the matched rows from the right table

• Use RIGHT JOIN to return all rows from the right table and the matched rows from the left table

• Use FULL JOIN to return rows when there is a match in one of the tables
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Q2. Delete and truncate difference
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Delete removes rows from a table while truncate removes all rows from a table.
• Delete is a DML command while truncate is a DDL command.

• Delete operation can be rolled back while truncate operation cannot be rolled back.

• Delete operation is slower than truncate operation.

• Example: DELETE FROM table_name WHERE condition; TRUNCATE TABLE table_name;
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Q1. Easy HR questions related to your previous experience
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