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Rave Innovations Interview Questions and Answers
Q1. What is bais-variance tradeoff? Explain P values to non technical and technical audience.
Bais-variance tradeoff is the balance between overfitting and underfitting. P values measure the significance of statistical results.
Bais-variance tradeoff is the tradeoff between the model's ability to fit the training data and its ability to generalize to new data.
Overfitting occurs when the model is too complex and fits the training data too closely, resulting in poor performance on new data.
Underfitting occurs when the model is too simple and fails to capture the underlyi...read more
Q2. Situations where you provided good customer services?
I have provided good customer service in various situations by actively listening, empathizing, and finding solutions to meet their needs.
Resolving a customer's issue with a product by offering a replacement or refund
Assisting a customer with navigating a website or online account
Handling a difficult customer with patience and professionalism
Following up with a customer to ensure their satisfaction with the service provided
Q3. Scenario where you received good customer service?
Received excellent customer service at a local restaurant
Staff greeted me warmly upon arrival
Waiter was attentive and knowledgeable about the menu
Manager personally checked in to ensure everything was satisfactory
Q4. Situtations where you recieved good service
I received good service when a restaurant staff went out of their way to accommodate my dietary restrictions.
Staff member listened attentively to my dietary needs
Offered alternative menu options
Checked in periodically to ensure satisfaction
Q5. Difference between Baysian and Frequentist statistics
Bayesian statistics involves prior knowledge and updating beliefs, while frequentist statistics relies on probability and sampling.
Bayesian statistics uses prior knowledge to update beliefs about a parameter, while frequentist statistics relies on probability and sampling.
Bayesian statistics involves the use of Bayes' theorem, while frequentist statistics involves hypothesis testing and confidence intervals.
Bayesian statistics can handle small sample sizes and complex models,...read more
Q6. What is Non morkov processes
Non-Markov processes are stochastic processes where the future state depends on more than just the current state.
Non-Markov processes violate the Markov property, which states that the future state depends only on the current state.
Examples of Non-Markov processes include autoregressive processes, hidden Markov models, and time series with long-term dependencies.
Non-Markov processes are more complex than Markov processes and require more sophisticated modeling techniques.
Non-...read more
Q7. How would you prioritise when there are competing priorities?
I would prioritize based on impact, urgency, and alignment with company goals.
Assess the potential impact of each priority on key metrics or goals
Consider the urgency of each priority and any deadlines that need to be met
Evaluate how each priority aligns with the overall company goals and strategy
Communicate with stakeholders to understand their perspectives and priorities
Use a prioritization framework such as MoSCoW (Must have, Should have, Could have, Won't have) or ICE (Im...read more
Q8. Pair programming for solve pyspark problem
Pair programming in pyspark involves two programmers working together on the same code to solve problems efficiently.
Pair programming can help in brainstorming ideas and finding solutions faster.
It allows for immediate feedback and collaboration between team members.
One person can write the code while the other reviews and suggests improvements.
Pair programming can lead to better code quality and reduce errors.
Examples: Collaborating on optimizing pyspark jobs, debugging comp...read more
Q9. How to deal with angry customer
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