LatentView Analytics Junior Data Analyst 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. Can you tell me about yourself?
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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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Q1. Join, window function excel vlookup, sumif,count etc.
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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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In this test they asked about basic internet knowledge like what is domain and co domain. And how to get the details of a company and cross check them. And basic ms office knowledge to work on given files.

Q1. Why do you wanna work here.
Ans. To achieve my goals while helping the company to grow.
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Q2. What you see yourself after 2 years.
Ans. To be at higher position in this company.
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Q3. Why do you want to work in this field.
Ans. Because it's an great opportunity to earn while learning and growing.
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