Celebal Technologies Interview Questions, Process, and Tips

The session lasted three hours and covered a wide range of topics, including DBMS, operating systems, SQL, computer networks, command-line interface commands, and data science topics such as Python, Pandas, and NumPy. Advanced topics included error handling and various algorithms like classification and clustering, along with deep learning concepts. Additionally, two data structures and algorithms questions were addressed, one being moderate and the other challenging, focusing on dynamic programming with bitmasking.

Q1. What are the basics of data science?
Ans. 
Data science involves extracting insights from data using various techniques and tools.
• Data collection and cleaning

• Exploratory data analysis

• Statistical modeling and machine learning

• Data visualization

• Communication of results
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Q2. What are the best practices for handling large data sets ?
Ans. 
Best practices for handling large data sets include data preprocessing, using distributed computing frameworks, and optimizing storage and retrieval methods.
• Perform data preprocessing to clean and transform data before analysis.

• Utilize distributed computing frameworks like Hadoop or Spark for parallel processing.

• Optimize storage and retrieval methods by using efficient data structures and indexing.

• Consider using cloud ...read more
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Q3. What is the purpose of a confusion matrix in data science?
Ans. 
A confusion matrix is a table that is used to describe the performance of a classification model.
• It shows the number of true positives, true negatives, false positives, and false negatives.

• It helps in evaluating the performance of a machine learning model by providing insights into the model's accuracy, precision, recall, and F1 score.

• It is particularly useful in scenarios where class imbalance exists or when different...read more
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Q4. Discussion around Kubernetes ?
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Q1. Can you provide an overview of yourself and your past experiences?
Ans. 
I am a data scientist with a background in statistics and machine learning.
• I have a Master's degree in Data Science from XYZ University.

• I have worked as a data analyst at ABC Company, where I developed predictive models for customer behavior.

• I am proficient in programming languages such as Python and R, and have experience working with big data tools like Hadoop and Spark.
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Q2. How effective do you find it when interviewers check your confidence by asking apparent questions that may lead to hesitation in your responses?
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Q1. Java Basic question , oops, Puzzle
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Q1. Deep oops question , SQL, DBMS
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Q1. In my case 3 technical round occurred
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Q2. Java , basic CN, Oops , HR oriented
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EASY AND SIMPLE NOT TO WORRY

Q1. WHAT IS INDEX IN SQL
Ans. 
Index in SQL is a data structure that improves the speed of data retrieval operations on a database table.
• Indexes are created on columns in a database table to quickly retrieve data based on the values in those columns.

• They can be unique or non-unique, clustered or non-clustered.

• Indexes can significantly improve the performance of SELECT queries but may slow down INSERT, UPDATE, and DELETE operations.

• Examples: CREATE I...read more
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Q1. Indepth knowledge on garbage collection
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Q2. Indepth knowledge of memory management in java
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Q3. 2 DSA questions
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Very hard test with dynamic programming questions almost 6 questions to do in 90 minutes along with MCQs

Q1. It was a long interview based on oops DSA machine learning
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Q1. There was another technical interview I don't know why but there was another interview the as the same level questions
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Q1. Then they finally selected and then ask about the location and other miscellaneous information
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Q1. What are the different types of loans?
Ans. 
Different types of loans include personal loans, home loans, auto loans, student loans, and business loans.
• Personal loans

• Home loans

• Auto loans

• Student loans

• Business loans
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Q2. Difference between Secured and Unsecured loans?
Ans. 
Secured loans are backed by collateral, while unsecured loans are not.
• Secured loans require collateral to secure the loan, such as a house or car

• Unsecured loans do not require collateral, but typically have higher interest rates

• Secured loans are less risky for lenders, as they have a way to recover their money if the borrower defaults

• Examples of secured loans include mortgages and auto loans, while credit cards and per
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Q3. Difference in Credit underwriting procedures for Retail and Agri Loans?
Ans. 
Credit underwriting procedures differ in Retail and Agri Loans due to varying risk factors and collateral requirements.
• Retail loans typically involve individual borrowers with stable income and credit history, while Agri loans involve farmers with fluctuating income and collateral in the form of agricultural assets.

• Retail loans may require credit score checks and income verification, while Agri loans may focus more on ...read more
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Q4. Do you have knowledge of Cloud service, SQL etc?
Ans. 
Yes, I have knowledge of Cloud services such as AWS, Azure, and Google Cloud, as well as SQL databases.
• Familiar with Cloud services like AWS, Azure, and Google Cloud

• Proficient in SQL databases

• Experience in setting up and managing databases in the Cloud
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