Morningstar Quantitative Analyst Interview Questions and Answers

Q1. Define your dataset and what difficulties you have faced while preparing your model ?
Ans. 
The dataset consists of customer purchase history and demographic information. Difficulties faced include data cleaning and missing values.
• Dataset includes customer ID, purchase amount, purchase date, age, gender, and location.

• Difficulties faced include handling missing values in the age and location columns.

• Data cleaning involved removing duplicates and outliers to ensure accurate analysis.

• Normalization and standardiz
Answered by AI
Add your answer
Q2. What is Normalization and standardization
Ans. 
Normalization and standardization are techniques used to rescale data to have a mean of 0 and a standard deviation of 1.
• Normalization is the process of rescaling the data to have values between 0 and 1.

• Standardization is the process of rescaling the data to have a mean of 0 and a standard deviation of 1.

• Normalization is useful when the features have different ranges.

• Standardization is useful when the features have diff...read more
Answered by AI
Add your answer

Q1. Basic & intermediate SQL questions
Add your answer
Q2. Second highest salary of employee
Add your answer
Q3. DBMS key concepts and explation with examples of use case
Ans. 
DBMS key concepts and examples of use cases
• DBMS stands for Database Management System

• Key concepts include data modeling, query optimization, transaction management

• Use cases include online transaction processing (OLTP), data warehousing, customer relationship management (CRM)

• Examples: MySQL for OLTP, Oracle for data warehousing, Salesforce for CRM
Answered by AI
Add your answer

Q1. Techno business related questions
Add your answer

Develop an ML solution from the given problem statement and data.

Q1. Discussion on your assignment and previous work.
Add your answer
Q2. Why did you choose the model?
Add your answer
Q3. Explain how GBM works
Ans. 
GBM is a machine learning algorithm that builds multiple decision trees to predict outcomes and combines them to improve accuracy.
• GBM stands for Gradient Boosting Machine

• It builds multiple decision trees sequentially, each one correcting errors made by the previous tree

• The final prediction is a combination of predictions from all the trees

• GBM is an ensemble learning technique that is popular for its high accuracy and f
Answered by AI
Add your answer

Q1. About end to end Investment banking terminology. SQL Quires
Add your answer

Q1. Some SQL Questions and Behavioral questions Capital Market question.. Like what is CDS and how it works
Add your answer

Q1. Is it ok for Relocation,
Ans. 
Yes, I am open to relocation for the right opportunity.
• I am willing to relocate for a position that aligns with my career goals and offers growth opportunities.

• I am open to considering relocation packages and assistance.

• I am excited about the prospect of exploring new cities and cultures.

• Examples: I have previously relocated for a job and found it to be a positive experience.

• I am willing to discuss relocation during th
Answered by AI
Add your answer

Q1. General resume based question. Interviewer asked me to explain one of the past project and asked some questions based on that
Add your answer

It was the assignment including the data science problem and SQL questions for one of the datetime table. Assignment was for 3 hours and had to complete some mandatory questions from data science assignment.

Q1. It was a technical + data science case study round. Interviewer asked me some technical questions based on machine learning and gave me one case study.
Add your answer

Q1. Interviewer asked me some general behavioural questions and also gave me one case study to solve.
Add your answer

Q1. How to check model performance? Over fit vs underfit?
Ans. 
Model performance can be checked using various metrics such as accuracy, precision, recall, F1 score, and confusion matrix.
• Split data into training and testing sets

• Train the model on the training set

• Evaluate the model on the testing set using metrics such as accuracy, precision, recall, F1 score, and confusion matrix

• If the model performs well on the testing set, it is not overfit or underfit

• If the model performs well o...read more
Answered by AI
Add your answer

Python code for 45 mins. Pandas , group by , filtering questions

Q1. Mostly behavioral questions
Add your answer