Datapure Technologies Customer Success Manager Interview Questions and Answers

Q1. How to evaluate ML models ? Difference between Bagging and Boosting ? What is F1 score and how change weights of Precision or recall while calculating F1 score ? What does it mean to have F1 score value 1 ...read more
Ans. 
ML models can be evaluated using metrics like accuracy, precision, recall, F1 score. Bagging combines multiple models, while boosting focuses on correcting errors. F1 score balances precision and recall.
• ML models can be evaluated using metrics like accuracy, precision, recall, and F1 score.

• Bagging is an ensemble technique where multiple models are trained independently and then combined by averaging or voting.

• Boosting ...read more
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Q1. What is proba method ? How is class probability calculated in decision trees ? How's a machine learning model evaluated ?
Ans. 
Proba method is used to calculate class probability in decision trees. Machine learning models are evaluated using metrics like accuracy, precision, recall, and F1 score.
• Proba method calculates the probability of a class label in decision trees by counting the occurrences of each class in a leaf node and dividing by the total number of samples in that node.

• Class probability in decision trees is calculated based on the ...read more
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Q1. How you delegate work to your peers
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Q2. Anything about the project you did
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Quant,Logical Reasoning and Data interpretation

Q1. Tell me something about your academic background?
Ans. 
I have a Bachelor's degree in Business Administration with a focus on marketing.
• Bachelor's degree in Business Administration

• Focus on marketing

• Completed coursework in market research and consumer behavior
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Q2. There were two to three Guestimates
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It was an ai based question. Very easy but you have to document everything properly.

Q1. Complete technical round covering nlp, computer vison and related engineering aspects
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Q1. This was technical managerial round. Completely cv based and some use cases.
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Q1. Sql, python and Analytics basics
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Q1. Guestimates, problem solving
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Q1. Discussion with leadership. Checking cultural fit
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Q1. SQL Python Data analytics Statistics
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Functional, case study, guesstimates, problem solving

Q1. Director/Senior Director would be taking the round. Check fitment
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Q1. Basic intro about profile
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Q2. Background of scm
Ans. 
SCM stands for Supply Chain Management, which involves the planning, coordination, and execution of all activities involved in sourcing, procurement, production, and logistics.
• SCM involves the management of the flow of goods and services from the point of origin to the point of consumption.

• It includes activities such as inventory management, demand forecasting, supplier relationship management, and distribution.

• Effecti...read more
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In this round, we have to answer a case study within two days and present are findings to the panel

Q1. In depth grilling on scm
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Q1. Salary discussion
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Q1. What is precision and recall
Ans. 
Precision and recall are metrics used in classification tasks to evaluate the performance of a model.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision is about how many of the actual positive cases were correctly predicted as positive.

• Recall is...read more
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Q2. How you will remove hallucination in LLM
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Q1. What is PEFT?and what is QLORA
Ans. 
PEFT stands for Physical Education, Fine Arts, Technology. QLORA is a software platform for managing and analyzing data.
• PEFT is an acronym for Physical Education, Fine Arts, Technology, representing different areas of study or activities in education.

• QLORA is a software platform used for managing and analyzing data, often used in research or business settings.

• PEFT can be implemented in schools to ensure a well-rounded ...read more
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Q2. Explain random forest algorithm
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges their predictions to improve accuracy.
• Random forest creates multiple decision trees during training.

• Each tree is built using a random subset of features and data points.

• The final prediction is made by averaging the predictions of all trees (regression) or taking a majority vote (classification).
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Q1. What are the challanges you faced
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Q2. How do you manage team?
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