Filter interviews by
Spark optimization techniques improve performance and efficiency of Spark applications.
Partitioning data to reduce shuffling
Caching frequently used data
Using broadcast variables for small data
Tuning memory allocation and garbage collection
Using efficient data formats like Parquet
Avoiding unnecessary data shuffling
Using appropriate hardware configurations
Optimizing SQL queries with appropriate indexing and partitio...
Hive optimization techniques improve query performance and reduce execution time.
Partitioning tables to reduce data scanned
Using bucketing to group data for faster querying
Using vectorization to process data in batches
Using indexing to speed up lookups
Using compression to reduce storage and I/O costs
Slowly changing data handling in Spark involves updating data over time.
Slowly changing dimensions (SCD) are used to track changes in data over time.
SCD Type 1 updates the data in place, overwriting the old values.
SCD Type 2 creates a new record for each change, with a start and end date.
SCD Type 3 adds a new column to the existing record to track changes.
Spark provides functions like `from_unixtime` and `unix_tim...
Answering about joins in SQL and modeling/visualization in PowerBI
Joins in SQL are used to combine data from two or more tables based on a related column
There are different types of joins such as inner join, left join, right join, and full outer join
PowerBI is a data visualization tool that allows users to create interactive reports and dashboards
Data modeling in PowerBI involves creating relationships between tab...
What people are saying about Fractal Analytics
I created a KPI to measure customer retention rate.
Developed formula to calculate percentage of customers retained over a specific period
Monitored customer churn rate to track effectiveness of retention strategies
Analyzed customer feedback and behavior to identify factors influencing retention
Implemented initiatives to improve customer retention based on data insights
Fractals are used in data science for analyzing complex and self-similar patterns.
Fractals are useful for analyzing data with repeating patterns at different scales.
They are used in image compression, signal processing, and financial market analysis.
Fractal analysis can help in understanding the underlying structure of data and making predictions.
I enjoy reading a variety of genres, including mystery, science fiction, and historical fiction.
Mystery
Science fiction
Historical fiction
Truth is subjective and can be influenced by personal experiences and cultural beliefs.
Truth is not always objective or universal
It can be shaped by personal experiences and cultural beliefs
What is considered true in one culture may not be true in another
Truth can also change over time as new information is discovered
For example, the belief that the earth was flat was once considered true, but is now known to be f...
In one box there are 12 red and 12 green balls and in another box there are 24 res and 24 green balls.
You have two balls choose from each of the box with replacement such that they have t...
The box with 12 red and 12 green balls has a better probability.
The probability of selecting two balls of the same color from the first box is higher due to the equal number of red and green balls.
In the second box, with 24 red and 24 green balls, the probability of selecting two balls of the same color is lower.
Therefore, the box with 12 red and 12 green balls has a better probability.
Given two strings, S
and T
with respective lengths M
and N
, your task is to determine the length of their longest common subsequence.
A subsequence is a sequen...
The task is to find the length of the longest common subsequence between two given strings.
Use dynamic programming to solve this problem efficiently.
Create a 2D array to store the lengths of longest common subsequences of substrings.
Iterate through the strings to fill the array and find the length of the longest common subsequence.
Example: For strings 'abcde' and 'ace', the longest common subsequence is 'ace' with...
I applied via Approached by Company and was interviewed in Nov 2024. There were 4 interview rounds.
Main pillars of Project management include scope, time, cost, quality, communication, risk, and procurement.
Scope management involves defining and controlling what is included in the project.
Time management focuses on creating and maintaining a project schedule.
Cost management involves budgeting and controlling project costs.
Quality management ensures that the project meets the required standards.
Communication manageme...
I have 5 years of project management experience, including leading a team to successfully complete a software development project.
Managed cross-functional teams to ensure project milestones were met
Identified and resolved conflicts within the team to maintain productivity
Implemented Agile methodologies to improve project efficiency
Communicated project updates to stakeholders regularly
Managed project budget and resource...
I appeared for an interview in Jan 2025.
Basic Pyspark/python question with some mcqs
I am passionate about data engineering and believe this company offers unique opportunities for growth and learning.
Exciting projects and challenges at this company
Opportunities for growth and learning in data engineering
Alignment with company values and culture
Potential for career advancement and development
Interest in the industry or specific technologies used by the company
I have experience working with data modelling in the GenAI project to optimize algorithms and improve performance.
Utilized various data modelling techniques to analyze and interpret data
Developed predictive models to enhance decision-making processes
Collaborated with data scientists to refine and validate models
Implemented machine learning algorithms to improve accuracy and efficiency
The challenge in end-to-end product delivery and implementation of solution roadmap involved coordinating multiple teams, managing dependencies, and ensuring alignment with business goals.
Coordinating cross-functional teams to ensure timely delivery of each component of the product
Managing dependencies between different teams and components to avoid delays
Ensuring alignment of the solution roadmap with the overall busi...
Informatica Cloud offers more comprehensive data integration capabilities compared to Azure Cloud.
Informatica Cloud provides a wide range of data integration tools and services for various data sources and formats.
Informatica Cloud offers advanced data quality and data governance features that are not available in Azure Cloud.
Informatica Cloud has a strong focus on data security and compliance, with built-in encryption...
I prefer Azure cloud solution for Data Engineering pipelines due to its scalability, reliability, and integration with other Microsoft services.
Azure provides a wide range of tools and services specifically designed for data engineering tasks, such as Azure Data Factory, Azure Databricks, and Azure HDInsight.
Azure offers seamless integration with other Microsoft services like Power BI, SQL Server, and Azure Machine Lea...
I manage client obligations by setting clear expectations, communicating regularly, and prioritizing tasks based on deadlines and importance.
Set clear expectations with clients regarding deliverables and timelines
Communicate regularly to provide updates on progress and address any concerns
Prioritize tasks based on deadlines and importance to ensure all client obligations are met
Proactively identify potential issues and...
I manage conflicts in the project by promoting open communication, addressing issues promptly, seeking compromise, and involving stakeholders in resolution.
Promote open communication among team members to address conflicts early on
Address issues promptly and directly to prevent escalation
Seek compromise and find win-win solutions to resolve conflicts
Involve stakeholders in conflict resolution to ensure all perspectives...
I applied via LinkedIn and was interviewed in Nov 2024. There were 2 interview rounds.
There are 10 multiple-choice questions (MCQs) on Python, 20 MCQs on machine learning (ML), and 10 questions on deep learning (DL).
I appeared for an interview in Jan 2025.
I applied via Campus Placement
Pretty simple aptitude test
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...
I applied via Referral and was interviewed in Dec 2024. There were 2 interview rounds.
15 MCQ, 2 coding round
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...
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...
I applied via Approached by Company and was interviewed in Oct 2024. There were 3 interview rounds.
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
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.
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
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 ...
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
I applied via Approached by Company and was interviewed in Jun 2024. There were 4 interview rounds.
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 ...
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 }}`.
Some of the top questions asked at the Fractal Analytics interview -
The duration of Fractal Analytics interview process can vary, but typically it takes about less than 2 weeks to complete.
based on 151 interview experiences
Difficulty level
Duration
based on 895 reviews
Rating in categories
Consultant
1.2k
salaries
| ₹11.3 L/yr - ₹20 L/yr |
Data Engineer
940
salaries
| ₹9.1 L/yr - ₹23.4 L/yr |
Senior Consultant
740
salaries
| ₹18.8 L/yr - ₹34.5 L/yr |
Data Scientist
598
salaries
| ₹14 L/yr - ₹25 L/yr |
Senior Data Scientist
342
salaries
| ₹22 L/yr - ₹38 L/yr |
Kiya.ai
MathCo
Innovatiview India Ltd
Zeta