Iron Mountain Inc Data Specialist Interview Questions and Answers

Q1. Self introduction
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Maths grammar & communication

Q1. Typeing speed skills check same use paragraph
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Q2. Typing speed 10 minutes
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Q1. Move on next round easy
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Q1. What are joins and its type
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Q2. Tell me about Dax query
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It was pair programming round where we need to attempt a couple of Spark Scenario along with the Interviewer. You will have a boiler plate code with some functionalities to be filled up. You will be assessed on writing clean and extensible code and test cases.

Q1. Questions will be based on the Project where they try to assess the depth of understading you have on your project, data quality checks, handling failure scenarios, reasoning behind choice of tech stack
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Q1. Write a few python queries and some live sql coding and pyspark coding
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Q2. Machine learning questions. Way too deep. which is not even required
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Q1. System performance management overview
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Basics of Statics and also verbal

Data structures and algorithms trees

Q1. Tell me about yourself
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Q2. Previous experience questions
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Q1. What is Pyspark?
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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. SQL questions and partitioning
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Q2. 3rd highest salary
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Q1. Technical interview
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Q2. SQL and data warehousing
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