Tredence Interview Questions and Answers for Freshers

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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55 ML questions and aptitude and 3 python questions and 2 sql questions

Q1. Normal distribution plot
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Q2. Standardisation vs normalisation
Ans. 
Standardisation and normalisation are techniques used to scale and transform data in order to improve model performance.
• Standardisation (Z-score normalisation) scales the data to have a mean of 0 and a standard deviation of 1.

• Normalisation (Min-Max scaling) scales the data to a specific range, typically between 0 and 1.

• Standardisation is less affected by outliers compared to normalisation.

• Standardisation is preferred w...read more
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Q3. How to handle outliars
Ans. 
Outliers can be handled by identifying, analyzing, and either removing or transforming them in the data.
• Identify outliers using statistical methods like Z-score or IQR.

• Analyze the outliers to understand if they are errors or valid data points.

• Remove outliers if they are errors or transform them using techniques like winsorization or log transformation.

• Consider using robust statistical methods that are less sensitive to...read more
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Q4. Draw Box-plot and explain its characteristics
Ans. 
Box-plot is a visual representation of the distribution of a dataset, showing the median, quartiles, and outliers.
• Box-plot displays the median (middle line), quartiles (box), and outliers (dots or lines).

• The length of the box represents the interquartile range (IQR).

• Whiskers extend to the smallest and largest non-outlier data points within 1.5 times the IQR from the quartiles.

• Outliers are plotted individually as dots o...read more
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Q1. How do you manage your time
Ans. 
I manage my time by prioritizing tasks, creating schedules, setting deadlines, and using time management tools.
• Prioritize tasks based on importance and deadlines

• Create daily or weekly schedules to allocate time for different tasks

• Set deadlines for each task to stay on track

• Use time management tools like calendars, to-do lists, and productivity apps

• Avoid multitasking and focus on one task at a time

• Take breaks to avoid b...read more
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Q2. Why you want to join Tredence
Ans. 
I want to join Tredence because of their reputation for cutting-edge data science projects and collaborative work environment.
• Tredence is known for their innovative data science projects

• I value the collaborative work environment at Tredence

• I believe Tredence will provide opportunities for professional growth and development
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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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Basic focus on aptitude questions

Q1. Basic guessestimates
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Q1. One of the questions was to test my analytical skill, trying to get all possible answers from me for a particular specified problem statement of (Looking on for possibility of expanding my Petrol Bunk Busi...read more
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Q1. He deep dived into my projects and asked me the varied kinda test cases I covered. It was quiet sun and interesting.
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It consisted of both aptitude questions, coding questions, Machine Learning Questions, Input based output questions in C/C++, Statistics. All carried one mark except the ML and stats questions which carried 2 marks. And the total questions were 60.

Q1. It was a technical round based on Resume. It also consisted of python coding questions, sql questions and Guesstimates. It all depends on the candidate on how to direct this round.
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Q1. It was a strict Round based on Resume. Every detail was scanned and multiple questions were asked from it. Along with, it HR asked the background check questions to assess if you are a good fit for company...read more
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