Symrise Interview Questions and Answers

Q1. How many company all over india
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Q2. How many workers does your company have
Ans. 
Our company has 100 employees working in various departments.
• We have a total of 100 workers in our company.

• The employees are distributed across different departments such as sales, marketing, operations, and finance.

• Each department has a varying number of employees based on their functions and workload.
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Q3. When did your company open
Ans. 
Our company opened in 2005, specializing in luxury travel experiences.
• Opened in 2005

• Specializes in luxury travel experiences
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Q1. Chest in your company
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Q2. When did your company open
Ans. 
Our company opened in 2005 and has been providing travel services for over 15 years.
• Our company was established in 2005

• We have been in operation for over 15 years

• We have a proven track record of providing travel services since our opening
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Q1. Company profile
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Q2. What is travel and tourism
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Q1. What is a dmc?
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Q2. Disadvantages faced by a dmc
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Q1. Criteria based questions
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Q2. Situation handling questions
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Q1. Introduction yourself in details covering each points
Ans. 
Experienced cluster manager with a proven track record in team leadership and strategic planning.
• Over 5 years of experience in managing multiple teams and projects simultaneously

• Strong leadership skills with the ability to motivate and inspire team members

• Expertise in strategic planning and execution to achieve organizational goals

• Excellent communication and interpersonal skills for effective team collaboration

• Proven t...read more
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Q2. Location can be for new hotels for max revenue
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A random topic was given and we had to speak on it

Q1. Why do u want to join us
Ans. 
I am passionate about data science and believe your company offers a great opportunity for growth and learning.
• Passionate about data science

• Believe company offers growth and learning opportunities
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Q2. A guestimate question, how many tea cups are consumed daily in delhi
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Q1. What is Bias In ML?
Ans. 
Bias in ML refers to the systematic error in a model's predictions, leading to inaccurate results.
• Bias is the algorithm's tendency to consistently learn the wrong thing by not taking all factors into account.

• It can result from the data used to train the model being unrepresentative or skewed.

• Bias can lead to unfair or discriminatory outcomes, especially in sensitive areas like hiring or lending decisions.

• Examples inclu...read more
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Q2. Regression concept in ML
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Regression in ML is a supervised learning technique used to predict continuous values based on input features.
• Regression models establish a relationship between dependent and independent variables.

• Common regression algorithms include linear regression, polynomial regression, and logistic regression.

• Evaluation metrics for regression models include Mean Squared Error (MSE) and R-squared.

• Example: Predicting house prices b...read more
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50 question in 15 mins

Q1. On site evaluation, where various tasks were given
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Q2. Why do u want to join us
Ans. 
I am passionate about data science and believe your company offers a great opportunity for growth and learning.
• I am passionate about data science and enjoy working with data to derive insights and make informed decisions.

• I believe your company is at the forefront of innovation in the field of data science, and I am excited about the opportunity to learn from experts in the industry.

• I am impressed by the projects your c...read more
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Q1. What is the implementation of Merge Sort in Java?
Ans. 
Merge Sort is a divide and conquer algorithm that divides the input array into two halves, recursively sorts them, and then merges the sorted halves.
• Divide the input array into two halves

• Recursively sort the two halves

• Merge the sorted halves back together
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Q2. What is the implementation of the Quick Sort algorithm in Java?
Ans. 
Quick Sort is a popular sorting algorithm that uses a divide-and-conquer approach to sort elements in an array.
• Choose a pivot element from the array.

• Partition the array into two sub-arrays: elements less than the pivot and elements greater than the pivot.

• Recursively apply the same process to the sub-arrays.

• Combine the sorted sub-arrays to get the final sorted array.
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