Altimetrik Data Analyst Interview Questions and Answers

General knowledge

English knowledge

Q1. Self and technical
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Technical questions from hive , spark Scala and azure

Q1. Technical questions on spark Scala hive
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Q2. Project architecture related questions
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Q1. It wnt well basics of hadoop,spark and my project
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Q2. Basics of sql and joins
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Basics of sql and joins

Code in Python for checking palindromes and SQL for sales data based on months.

Generic aptitude test

Q1. Basic questions
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Q2. Basic questions in sql
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Q1. General discussion
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Q2. Managerial round
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Q1. General hobbies
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Q2. Salary and joining date
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Q1. Types of prompting
Ans. 
Prompting is a technique used to encourage a person to continue speaking or to provide more information.
• Verbal prompts: asking open-ended questions, using encouraging words

• Non-verbal prompts: nodding, maintaining eye contact

• Visual prompts: showing images or videos to stimulate conversation
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Q2. Memory handling in conversational
Ans. 
Memory handling in conversational AI involves managing data storage and retrieval efficiently.
• Use efficient data structures to store and retrieve information

• Implement caching mechanisms to reduce memory usage

• Optimize algorithms for processing large amounts of data

• Consider using cloud-based storage solutions for scalability

• Monitor memory usage and performance regularly
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Q1. Questions on SQL complex queries
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Q2. Handling ADF pipelines
Ans. 
Handling ADF pipelines involves designing, building, and monitoring data pipelines in Azure Data Factory.
• Designing data pipelines using ADF UI or code

• Building pipelines with activities like copy data, data flow, and custom activities

• Monitoring pipeline runs and debugging issues

• Optimizing pipeline performance and scheduling triggers
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Q3. Schedules and triggers
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Q4. About project work and complexity faced and handling issues
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Q1. Salary discussion
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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?
Ans. 
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?
Ans. 
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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