Tredence Analyst Interview Questions, Process, and Tips for Freshers

Around 60 questions were asked including aptitude and basic coding in 2:30 hrs.

Q1. Basic questions on python, sql and project related discussion.
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Q2. Rate yourself in sql and python
Ans. 
I rate myself highly in SQL and Python.
• I have extensive experience in writing complex SQL queries and optimizing database performance.

• I am proficient in Python and have used it for data analysis, automation, and web scraping.

• I have worked on various projects where I utilized both SQL and Python together to extract, transform, and load data.

• I am familiar with popular SQL databases like MySQL, PostgreSQL, and Oracle, as ...read more
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Q3. Tell me about your projects
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Basic aptitude, verbal, and coding tests with one program to run and execute. The remaining questions are multiple-choice questions and one guestimate question.

Aptitude test consist of 4 segment
1. Quantitative aptitude
2.verbal ability
3. Data interpretation
4. Short written ( who is better gifted or hard worker..??) this question ask to me AND second question is on guesstimating (how much car sold in India in 2018 ) solved based on assumption and calculations.
5. It was consign round which has 3 questions 1 easy 1 moderate 1 tough of 20,20,50 marks respectively

Q1. This round has 3 section 1. Read sentence and speak loudly and record it You can record only once 2. Second listen audio clip and speak loudly and record it you can listen audio clip once only 3 listen a...read more
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Q1. It is totally technical interview totally based on resume it means whatever you have written in your resume you have to know everything in deep my interview was last for 35 min totally on machine learning....read more
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Q2. How you reduce error in model Which kind of matrix used in your model How you map data
Ans. 
To reduce error in a model, I use techniques like cross-validation, regularization, and feature selection. I use matrices like confusion matrix and correlation matrix. Data is mapped using techniques like normalization and encoding.
• Reduce error in model by using techniques like cross-validation, regularization, and feature selection

• Use matrices like confusion matrix to evaluate classification models and correlation mat...read more
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Q1. What are different optimisation techniques you have used so far in databricks
Ans. 
I have used techniques like hyperparameter tuning, feature engineering, and model selection in Databricks for optimization.
• Hyperparameter tuning using GridSearchCV or RandomizedSearchCV

• Feature engineering to create new features or transform existing ones

• Model selection using techniques like cross-validation or ensemble methods
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Q1. What is your salary expectations
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Q2. What drives you to switch from previous company
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