Brillio Data Specialist Interview Questions and Answers

Q1. About your self
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Q2. Communication skills
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Q1. Programming language
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Q2. What tools do you utilize for data analysis?
Ans. 
I utilize tools such as Excel, Python, SQL, and Tableau for data analysis.
• Excel for basic data manipulation and visualization

• Python for advanced data analysis and machine learning

• SQL for querying databases

• Tableau for creating interactive visualizations
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Q3. Pandas numpy seaborn matplot
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Data analysis of code in the context of data analysis.

Coding logical question paper.

Q1. Data warehousing related questions
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Q2. SQL scenario based questions
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Q3. Project experience
Ans. 
I have experience working on projects involving data pipeline development, ETL processes, and data warehousing.
• Developed ETL processes to extract, transform, and load data from various sources into a data warehouse

• Built data pipelines to automate the flow of data between systems and ensure data quality and consistency

• Optimized database performance and implemented data modeling best practices

• Worked on real-time data pro...read more
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Q4. Python Based questions
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Q5. AWS features and questions
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Q1. Similar to first round but in depth questions relatively
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Q2. Asked about career goals and stuff
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Q1. General work related conversation
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Q2. Salary discussion
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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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You're like this job opportunity

Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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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. Incremental load in pyspark
Ans. 
Incremental load in pyspark refers to loading only new or updated data into a dataset without reloading the entire dataset.
• Use the 'delta' function in pyspark to perform incremental loads by specifying the 'mergeSchema' option.

• Utilize the 'partitionBy' function to optimize incremental loads by partitioning the data based on specific columns.

• Implement a logic to identify new or updated records based on timestamps or uni...read more
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Q2. Drop duplicates
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Q1. Tell about your current project
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Q2. What is distribution in spark
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Distribution in Spark refers to how data is divided across different nodes in a cluster for parallel processing.
• Data is partitioned across multiple nodes in a cluster to enable parallel processing

• Distribution can be controlled using partitioning techniques like hash partitioning or range partitioning

• Ensures efficient utilization of resources and faster processing times
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Q3. How much data can be processed in AWS Glue
Ans. 
AWS Glue can process petabytes of data per hour
• AWS Glue can process petabytes of data per hour, depending on the configuration and resources allocated

• It is designed to scale horizontally to handle large volumes of data efficiently

• AWS Glue can be used for ETL (Extract, Transform, Load) processes on massive datasets
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Q1. What is distribution in spark ?
Ans. 
Distribution in Spark refers to how data is divided across different nodes in a cluster for parallel processing.
• Distribution in Spark determines how data is partitioned across different nodes in a cluster

• It helps in achieving parallel processing by distributing the workload

• Examples of distribution methods in Spark include hash partitioning and range partitioning
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Q2. How much data can be processed in AWS glue
Ans. 
AWS Glue can process petabytes of data per hour.
• AWS Glue can process petabytes of data per hour, making it suitable for large-scale data processing tasks.

• It can handle various types of data sources, including structured and semi-structured data.

• AWS Glue offers serverless ETL (Extract, Transform, Load) capabilities, allowing for scalable and cost-effective data processing.

• It integrates seamlessly with other AWS services...read more
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Q3. What is spark and pyspark
Ans. 
Spark is a fast and general-purpose cluster computing system, while PySpark is the Python API for Spark.
• Spark is a distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

• PySpark is the Python API for Spark that allows developers to write Spark applications using Python.

• Spark and PySpark are commonly used for big data processing, machine...read more
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Q1. Sql regarding question was there
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