Visa Senior Consultant Interview Questions and Answers

A typical case interview.

Q1. Case interview for a banking company
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Q2. Standard guestimate questions
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Q1. Guesstimate simple
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Q2. Behavior questions
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Banking industry related case

Q1. Why are you looking for job?
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Q1. Roles in the current org
Ans. 
I currently hold the role of Senior Consultant in my organization.
• Lead client engagements and provide strategic guidance

• Manage project timelines and deliverables

• Mentor junior team members and provide training

• Collaborate with cross-functional teams to drive results
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Q2. Responsibilities
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Guesstimate on number of cars sold

Growth case on growing a cobranding card

Q1. What are you passionate about?
Ans. 
I am passionate about helping others achieve their full potential and making a positive impact in their lives.
• I enjoy mentoring and coaching individuals to help them reach their goals

• I find fulfillment in volunteering and giving back to the community

• I am dedicated to continuous learning and personal development

• I strive to create a supportive and inclusive environment for those around me
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Q2. Why do you want to join consulting?
Ans. 
I am passionate about problem-solving, enjoy working with diverse clients, and thrive in fast-paced environments.
• Enjoy problem-solving and finding innovative solutions

• Excited about working with diverse clients and industries

• Thrives in fast-paced and challenging environments
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Q1. Exaplain about PGDBA programme
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Q2. Explain DBSCAN.
Ans. 
DBSCAN is a density-based clustering algorithm that groups together points that are closely packed.
• DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise.

• It groups together points that are closely packed based on two parameters - epsilon (eps) and minimum points (minPts).

• Points are classified as core points, border points, or noise points.

• Core points have at least minPts points within eps distanc...read more
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Q3. Explain KNn models and its difference to K-means
Ans. 
KNN models are used for classification and regression tasks based on similarity to nearest neighbors, while K-means is a clustering algorithm based on distance to centroids.
• KNN models assign a class label to a new data point based on majority class of its k-nearest neighbors

• K-means clusters data points into k clusters based on distance to centroids

• KNN is a supervised learning algorithm, while K-means is an unsupervised
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Q4. What features will you take into consideration while designing a time series model.
Ans. 
Features to consider in designing a time series model
• Identifying seasonality and trends in the data

• Selecting appropriate lag values for autoregressive components

• Choosing the right forecasting method (e.g. ARIMA, Exponential Smoothing)

• Evaluating model performance using metrics like RMSE and MAE
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Q5. Explain about the contest you have won in the past, which ML models have you applied
Ans. 
I won a Kaggle competition by applying a combination of XGBoost and neural network models.
• Won Kaggle competition by predicting housing prices using XGBoost and neural network models

• Achieved top 5% ranking in the competition

• Used feature engineering techniques to improve model performance
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2 case studies asked, from topics picked up from my CV

Q1. Another case study round
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