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Broadridge Financial Solutions Data Analyst Intern Interview Questions and Answers
Broadridge Financial Solutions Data Analyst Intern Interview Experiences
2 interviews found
Anonymous
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There were 2 interview rounds.
(1 Question)
- Q1. Short cut keys of tools
- Ans.
Short cut keys are keyboard shortcuts that allow users to quickly perform actions in various tools.
Ctrl + C: Copy
Ctrl + V: Paste
Ctrl + X: Cut
Ctrl + Z: Undo
Ctrl + S: Save
Ctrl + P: Print
(1 Question)
- Q1. Project explanation in brief manner
- Ans.
Developed a predictive model to forecast sales based on historical data
Collected and cleaned historical sales data
Performed exploratory data analysis to identify trends and patterns
Built and trained machine learning model using regression techniques
Evaluated model performance using metrics like RMSE and MAE
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There was 1 interview round.
(2 Questions)
- Q1. About the project
- Q2. How much mb the latest project consumed and what are ur weakenesses.
- Ans.
The latest project consumed approximately 500 MB of data.
The latest project consumed 500 MB of data.
It is important to track data consumption for future optimization.
Weaknesses can include lack of experience with certain tools or techniques.
Weaknesses can also include difficulty in time management or communication.
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I applied via Company Website and was interviewed before Feb 2023. There were 2 interview rounds.
(2 Questions)
- Q1. Tell me about yourself
- Q2. Projects questions
Case details on economics and data. how would you use data to do XYZ?
I applied via campus placement at Kalinga Institute of Industrial Technology, Khurda and was interviewed before May 2022. There were 4 interview rounds.
B2B invoice management application - Using ReactJS in front-end and Java in back-end and then integrate with machine learning algorithms to update dates and find out the customers according to the present data (important customers and then manage their invoices).
(1 Question)
- Q1. Interview - About technical skills.
(1 Question)
- Q1. Programming skills
Interview Preparation Tips
Selected
I applied via Company Website and was interviewed in Dec 2024. There were 4 interview rounds.
Given Python coding challenge related to string processing and second question was matrix processing using python
(4 Questions)
- Q1. Bagging boosting and its difference and uses.
- Q2. Methods to minimize overfitting and underfitting
- Q3. Transformer architecture
- Q4. LLM usecase and explain how to work on it
(2 Questions)
- Q1. Given data, find out what AI project can be discovered and how to progress on implementing it.
- Q2. Given data and usecase, which model to use to implement solution and how detailed steps.
(2 Questions)
- Q1. Discussed each project in my resume in detail and related questions
- Q2. Discussed team and personal behavioural questions
Interview Preparation Tips
I applied via LinkedIn and was interviewed in Dec 2023. There was 1 interview round.
(2 Questions)
- Q1. Discussion on linear regression and regularisation techniques
- Q2. Discussion on tree based models and measures to score the models
Interview Preparation Tips
- Regression
- Machine Learning
(2 Questions)
- Q1. DBSCAN Clustering
- Q2. How does current work helps solving use cases for PayU
- Ans.
My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.
Analyzing transaction data to identify fraudulent activities and improve security measures for PayU
Developing predictive models to forecast transaction volumes and optimize payment processing times
Utilizing machine learning algorithms to personalize user experiences and inc...
(2 Questions)
- Q1. Details about past projects
- Q2. Details about latest project
Interview Preparation Tips
(1 Question)
- Q1. SQL coding tests
(1 Question)
- Q1. Discussed about hybrid mode of work
(1 Question)
- Q1. What is a Transformer
- Ans.
A Transformer is a type of deep learning model that uses attention mechanisms to improve the efficiency of sequence-to-sequence tasks.
Utilizes self-attention mechanism to weigh the importance of different input elements
Commonly used in natural language processing tasks such as machine translation and text generation
Examples include BERT, GPT, and Transformer-XL
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