ACCIONA Interview Questions and Answers

It was a combination of mcq and coding questions
mcq: aptitude + c++ + sql question, nothing really tough
Coding question: 3 leetcode question, medium level, of stack, binary search and graph traversal

Q1. Question regarding project
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Q2. SQL basic to moderate question
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Q3. C++ coding question on strings recursion callback in c, oops medium level question, finding error in code
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Q4. Puzzles Q1-> cake cutting ( 3 cuts, 8 slices) Q2 ->9 bottles, 8 same weight bottles, 1 heavy, 2 tries using a balance
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Q5. OS question difference bw multi-processing and multi-programming how would u reduce number of page faults. etc
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Q1. Once again project explanation
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Q2. Basic to high level oops question:( C++) i was asked to write some class and in that these questions were asked 1: different types of access modifiers, but the question was asked indirectly. like why did...read more
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Q3. HR questions, since it was a semi technical cum hr round,
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Q1. Describe projects?
Ans. 
I have worked on various projects ranging from data analysis to machine learning models.
• Developed a sentiment analysis model using natural language processing techniques

• Implemented a recommendation system based on collaborative filtering algorithm

• Worked on a predictive maintenance project for optimizing equipment performance
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Q2. Explain architecture of Efficient-nets
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EfficientNets are a family of convolutional neural networks that have been designed to achieve state-of-the-art accuracy with fewer parameters and FLOPS.
• EfficientNets use a compound scaling method to balance network depth, width, and resolution for optimal performance.

• They are based on a baseline network architecture called EfficientNet-B0, which is then scaled up to create larger models like EfficientNet-B1, Efficient...read more
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Q3. Explain transformers architecture
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Transformers architecture is a deep learning model that uses self-attention mechanism to process sequential data.
• Transformers consist of an encoder and a decoder, each composed of multiple layers of self-attention and feed-forward neural networks.

• Self-attention mechanism allows the model to weigh the importance of different input tokens when making predictions.

• Transformers have achieved state-of-the-art performance in ...read more
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Q4. Longest sub array question in python.
Ans. 
Find the longest subarray of strings in a given array.
• Iterate through the array and keep track of the current subarray length.

• Reset the subarray length when encountering a non-string element.

• Return the length of the longest subarray found.
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Q1. About family and past life
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Q2. Past internships and learnings
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Q3. College life and what they expect.
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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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SQL and Python coding test

Q1. Bagging vs Boosting
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Q2. Overfitting and Underfitting in GBDT
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Q1. Questions on Recent project
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Q2. Deep Learning vs classical ML
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