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TVS Motor Interview Questions and Answers
Q1. How do you find error from a service?
To find errors from a service, analyze logs, monitor system metrics, and use debugging tools.
Analyze logs generated by the service to identify any error messages or exceptions.
Monitor system metrics such as CPU usage, memory usage, and network traffic for any anomalies that may indicate errors.
Use debugging tools like breakpoints, stack traces, and error logs to pinpoint the source of errors.
Perform thorough testing and quality assurance to catch and fix errors before they re...read more
Q2. Different data definition language query of SQL
Different data definition language queries in SQL
DDL queries are used to create, modify, and delete database objects
Some common DDL queries include CREATE, ALTER, and DROP
CREATE TABLE is used to create a new table
ALTER TABLE is used to modify an existing table
DROP TABLE is used to delete a table
Q3. Guesstimate: Guess the number of vadapavs consumed in mumbai
Approximately 18 million vadapavs are consumed in Mumbai every day.
Consider the population of Mumbai and the popularity of vadapav as a snack.
Estimate the number of street vendors selling vadapav in Mumbai.
Assume an average number of vadapavs sold per vendor per day.
Factor in the demand for vadapav during peak hours and festivals.
Consider the price of vadapav and its affordability for the common people.
Compare the consumption of vadapav with other popular snacks in Mumbai.
Q4. Why SQL and not NoSQL?
SQL is preferred for structured data, transactions, complex queries, and ACID compliance.
SQL is better for structured data and relationships between data.
SQL is preferred for transactions and ensuring data integrity.
SQL is more suitable for complex queries involving multiple tables.
SQL databases provide ACID compliance for data consistency and reliability.
Q5. how to remove the second-highest salary in SQL
Use a subquery to remove the second-highest salary in SQL
Use a subquery to select the highest salary
Use the NOT IN operator to exclude the second-highest salary from the results
Q6. What is over fitting
Overfitting occurs when a model learns the training data too well, leading to poor performance on new, unseen data.
Overfitting occurs when a model is too complex and captures noise in the training data.
It can be identified when a model performs well on training data but poorly on test data.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
Example: A decision tree with too many branches that perfectly fits the training data but fail...read more
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