SPRINKLR Interview Questions, Process, and Tips

Q1. Tell me about yourself
Ans. 
Experienced HR professional with a background in employee relations, talent acquisition, and performance management.
• Over 5 years of experience in HR roles

• Skilled in conflict resolution and mediation

• Strong understanding of labor laws and regulations

• Implemented successful recruitment strategies at previous company
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Q2. Why do you want to join Sprinklr
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I want to join Sprinklr because of its innovative technology, strong company culture, and opportunities for growth.
• Innovative technology: I am excited about the cutting-edge technology and solutions that Sprinklr offers to help businesses succeed in the digital age.

• Strong company culture: I have heard great things about Sprinklr's inclusive and collaborative work environment, which aligns with my values.

• Opportunities f...read more
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3 hours - Coding questions and Aptitude test

Aptitude questions - normal RS aggarwal type

CSE core concepts and basic aptitude, few code snippet analysis

Q1. About selenium codes
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Q2. Rest assured framework
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Kind of to explain selenium framework in detail.

Q1. Problem solving coding questions
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Q2. Selenium questions and logical thinking questions
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Q1. General discussion about role
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Q2. Culture fit round
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Q1. General hr questions
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Q2. General hr questions again
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Q1. The questions are based on CV/ past work
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Mostly data related question and two coding questions which we had to do in python only

Q1. They asked the mathematics behind Adam Optimizer
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Q2. What is attention in term of data science?
Ans. 
Attention in data science refers to the mechanism that allows models to focus on specific parts of the input data.
• Attention mechanisms help models to weigh the importance of different input features.

• They are commonly used in natural language processing tasks such as machine translation and text summarization.

• Attention can improve the performance of models by allowing them to selectively focus on relevant information.

• Ex...read more
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Q3. Questions on whether you would use precision and recall in real life example for measuring accuracy.
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Q1. How would you judge the efficiency of LLMs
Ans. 
Efficiency of LLMs can be judged based on various factors such as accuracy, speed, resource consumption, and interpretability.
• Evaluate accuracy by comparing LLM predictions with ground truth labels

• Assess speed by measuring the time taken for LLM to process data

• Analyze resource consumption in terms of memory and computational power usage

• Consider interpretability by examining how easily LLM decisions can be understood

• Use...read more
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Q2. Questions on CV mostly
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