Fidelity Investments Lead Data Analyst Interview Questions and Answers

Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Q1. Detailed project questions
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Q2. Explain completed gen ai project
Ans. 
Developed a generative AI model to create realistic images of fictional characters.
• Used GANs (Generative Adversarial Networks) to generate new images based on existing data.

• Trained the model on a dataset of character images from various sources.

• Implemented techniques like style transfer to enhance the diversity and creativity of generated images.

• Evaluated the model's performance based on image quality metrics and user
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Q1. Given a variable, how to do Linear Regression?
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Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Collect data on the variables of interest

• Plot the data to visualize the relationship between the variables

• Choose a suitable linear regression model (simple or multiple)

• Fit the model to the data using a regression algorithm (e.g. least squares)

• Evaluate the model's performance using ...read more
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Q2. How Linear Regression handles noise?
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Linear Regression minimizes noise by fitting a line that best represents the relationship between variables.
• Linear Regression minimizes the sum of squared errors between the actual data points and the predicted values on the line.

• It assumes that the noise in the data is normally distributed with a mean of zero.

• Outliers in the data can significantly impact the regression line and its accuracy.

• Regularization techniques l...read more
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Q3. Solve two equations to find coefficients?
Ans. 
Use linear algebra to solve for coefficients in two equations.
• Set up the two equations with unknown coefficients

• Solve the equations simultaneously using methods like substitution or elimination

• Example: 2x + 3y = 10 and 4x - y = 5, solve for x and y
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Q4. Probability question on picking a red ball from Red, blue, black ball bag with replacement.
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Q1. How do you define model Gini?
Ans. 
Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
Ans. 
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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Question on Probability and basic aptitude questions

Q1. Question were related to Past projects
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Q2. SQL and Excel medium level questions
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Q1. What is Bert and transformer
Ans. 
Bert and transformer are models used in natural language processing for tasks like text classification and language generation.
• Bert (Bidirectional Encoder Representations from Transformers) is a transformer-based model developed by Google for NLP tasks.

• Transformer is a deep learning model architecture that uses self-attention mechanisms to process sequential data like text.

• Both Bert and transformer have been widely use...read more
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Q2. NLP pre processing techniques
Ans. 
NLP pre processing techniques involve cleaning and preparing text data for analysis.
• Tokenization: breaking text into words or sentences

• Stopword removal: removing common words that do not add meaning

• Lemmatization: reducing words to their base form

• Normalization: converting text to lowercase

• Removing special characters and punctuation
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Q1. Basic questions
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Q2. Strength weakness
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Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Q1. Introduction by my self
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Q2. Pyspark related getting the column names
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