Fractal Analytics Interview Questions and Answers

Q1. PySpark and SQL questions
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Q2. Azure datafactory related questions
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Q1. Spark internals
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Q2. Scenario based
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Q1. Soft skills
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Q2. Scenario based and general discussion
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Pretty simple aptitude test

Q1. Explain your projects in detail from data preprocessing to deployment
Ans. 
I have worked on various projects involving data preprocessing, model building, and deployment.
• I start by cleaning and preprocessing the raw data to remove missing values and outliers.

• I then perform feature engineering to create new features and select the most relevant ones for model building.

• Next, I train machine learning models using algorithms like Random Forest, XGBoost, and Neural Networks.

• I evaluate the models u...read more
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Q2. Given a SQL Query to solve
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Q3. What is logistic regression and its formula?
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Q4. What are the underlying assumptions of logistic regression?
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Q5. Difference between linearity and logistic assumptions are?
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15 MCQ, 2 coding round

Q1. What is PCA explain eigen values
Ans. 
PCA is a dimensionality reduction technique that uses eigenvalues to find the principal components of a dataset.
• PCA is used to reduce the dimensionality of a dataset by transforming the data into a new coordinate system.

• Eigenvalues represent the amount of variance captured by each principal component.

• Higher eigenvalues indicate that the corresponding principal component explains more variance in the data.

• Eigenvalues ar...read more
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Q2. How chatbot works really
Ans. 
Chatbots use natural language processing and machine learning to interact with users and provide automated responses.
• Chatbots use natural language processing (NLP) to understand and interpret user input.

• They use machine learning algorithms to learn from past interactions and improve responses.

• Chatbots can be rule-based, where responses are pre-programmed, or AI-based, where they learn and adapt over time.

• Examples inclu...read more
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Q1. Explain about your projects
Ans. 
I have worked on projects involving natural language processing, computer vision, and recommendation systems.
• Developed a sentiment analysis model using NLP techniques

• Implemented a facial recognition system using CNNs

• Built a movie recommendation engine based on collaborative filtering
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Q2. Basic Kubernetes quetions
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Q1. Python questions
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Q2. Situational Questions
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Q1. Expected salary?
Ans. 
My expected salary reflects my skills, experience, and market standards for an ML Engineer role.
• Based on my research, the average salary for an ML Engineer in this region is between $X and $Y.

• I have X years of experience, which positions me towards the higher end of that range.

• I am also considering the benefits and growth opportunities that come with the role.
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Q1. Describe your current project snd your role.
Ans. 
Leading a team in developing a mobile app for tracking fitness goals.
• Managing a team of developers and designers

• Creating project timelines and milestones

• Collaborating with stakeholders to gather requirements

• Testing and quality assurance of the app

• Implementing feedback and updates based on user testing

• Ensuring project stays within budget and timeline
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Q1. Describe your role
Ans. 
I oversee project planning, execution, and delivery to ensure successful completion.
• Lead project teams in defining project scope, goals, and deliverables

• Develop project plans and schedules, and track progress against milestones

• Manage project budget and resources effectively

• Communicate with stakeholders to ensure alignment and manage expectations

• Identify and mitigate project risks and issues

• Ensure quality standards are ...read more
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Q1. Describe your childhood
Ans. 
I had a happy and adventurous childhood filled with outdoor activities and family vacations.
• Grew up in a small town surrounded by nature

• Enjoyed playing sports like soccer and basketball

• Went on camping trips with my family every summer

• Participated in school plays and talent shows

• Had a close-knit group of friends from the neighborhood
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Q1. The HR representative will seek to understand your experience and its alignment with the role.
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Q2. Salary expectations, Offers in hand
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Q1. What are the key features and functionalities of Snowflake?
Ans. 
Snowflake is a cloud-based data warehousing platform known for its scalability, performance, and ease of use.
• Snowflake uses a unique architecture called multi-cluster, which separates storage and compute resources for better scalability and performance.

• It supports both structured and semi-structured data, allowing users to work with various data types.

• Snowflake offers features like automatic scaling, data sharing, and ...read more
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Q2. The interviewer asked several complex SQL questions that utilized analytical functions.
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Q3. I was asked to write some syntax-level code for Jinja statements in dbt.
Ans. 
Jinja in dbt allows for dynamic SQL generation using templating syntax.
• Use `{{ }}` for expressions, e.g., `{{ ref('my_model') }}` to reference another model.

• Use `{% %}` for control flow, e.g., `{% if condition %} ... {% endif %}` for conditional logic.

• Loop through lists with `{% for item in list %} ... {% endfor %}`.

• Define variables with `{% set var_name = value %}` and use them with `{{ var_name }}`.
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Q1. The interviewer, who was part of the senior management, inquired about dbt, its features, and my experience with dbt thus far.
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Q2. The interviewer reviewed my resume and inquired about the challenges I encountered in my previous roles and the methods I used to resolve them.
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Q1. Salary negotiations, Bonus discussions
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