KANINI Software Solutions Associate Data Engineer Interview Questions and Answers

Medium level of coding questions.

Q1. About azure data factory
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Q2. How to copy multiple csv into single file
Ans. 
Use a command line tool like cat to concatenate multiple CSV files into a single file
• Use the cat command in the terminal to concatenate multiple CSV files into a single file

• Navigate to the directory where the CSV files are located

• Run the command 'cat file1.csv file2.csv > combined.csv' to merge file1.csv and file2.csv into a new file named combined.csv
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Q1. What is hdinsights in azure data factory
Ans. 
HDInsight is a cloud-based service in Azure that makes it easy to process big data using Apache Hadoop, Spark, and other tools.
• HDInsight is a fully managed cloud service that makes it easy to process big data using open-source frameworks like Apache Hadoop, Spark, and more.

• It allows you to create, scale, and monitor Hadoop clusters in Azure.

• HDInsight integrates with Azure Data Factory to provide data orchestration and ...read more
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Q2. How to perform data copy in azure
Ans. 
Data copy in Azure can be performed using Azure Data Factory or Azure Storage Explorer.
• Use Azure Data Factory to create data pipelines for copying data between various sources and destinations.

• Use Azure Storage Explorer to manually copy data between Azure storage accounts.

• Utilize Azure Blob Storage for storing the data to be copied.
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Q1. Expectations in the company
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Q2. Ctc and relevant stuff
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Q1. What is RAG ?
Ans. 
RAG stands for Red, Amber, Green and is a color coding system used to indicate the status of a project or task.
• RAG is commonly used in project management to quickly communicate the status of different tasks or projects.

• Red typically indicates that a task or project is behind schedule or facing significant issues.

• Amber signifies that there are some concerns or risks that need to be addressed.

• Green means that a task or p...read more
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Q2. How you trainined LLM model?
Ans. 
I trained the LLM model using a combination of labeled data, feature engineering, and iterative model tuning.
• Collected and preprocessed labeled data for training

• Performed feature engineering to extract relevant features

• Used iterative model tuning to optimize performance

• Evaluated model performance using metrics like accuracy, precision, and recall
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Q1. Sql regarding question was there
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Basic aptitude question

Q1. Basic analysis and ML questions , more focused on approach instead of coding
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Q1. As like other HR rounds
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Online aptitude test have been coundected

Q1. About the data procesig and projects on data science
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Q1. Fresher or experienced
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This was basic test it was easy

Q1. Here all question are based on my resume
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Q2. Explain your project What kind of work you did
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