EdgeQ Technologies Private Limited interview process & preparation
EdgeQ Technologies Private Limited currently has 13 live openings on OnJob.io, concentrated in Data Analyst, Frontend Developer and AI Engineer roles. There are no leaked or company-specific questions here — prepare with the general, role-based interview rounds and most-asked questions for those role types below.
OnJob.io does not publish leaked or company-confidential interview questions. The guides below are general, role-based preparation — the standard rounds and commonly asked questions for the role types EdgeQ Technologies Private Limited is currently hiring for. Your real EdgeQ Technologies Private Limited interview may differ.
What to expect when interviewing for Data Analyst, Frontend Developer and AI Engineer roles at companies like EdgeQ Technologies Private Limited
EdgeQ Technologies Private Limited is currently advertising these role types on OnJob.io. Each guide is the general interview structure and most-asked questions for that role.
General Data Analyst interview prep
2 live roles at EdgeQ Technologies Private Limited
SQL, Excel, statistics and storytelling with data — the analyst interview for product, BI and analytics teams across India.
Typical Data Analyst interview rounds
01
SQL round
Joins, aggregations, window functions and query writing.
02
Excel / spreadsheet round
Lookups, pivot tables and formulas.
03
Case / analytics round
Business case: define metrics, analyse and recommend.
04
Tool & viz round
Power BI / Tableau dashboards and stakeholder communication.
Commonly asked Data Analyst questions
What is the difference between INNER JOIN, LEFT JOIN and FULL OUTER JOIN?
Write a SQL query to find the second-highest salary in a table.
What is the difference between WHERE and HAVING?
Explain window functions like ROW_NUMBER(), RANK() and DENSE_RANK().
What is the difference between mean, median and mode, and when is each misleading?
How do you handle missing or null values in a dataset?
Explain the difference between a primary key and a foreign key.
What is normalisation, and why does it matter for analysis?
LLMs, prompt engineering, RAG and AI application development — the fast-growing generative-AI engineering interview at Indian product startups, GCCs and AI-first companies. Distinct from a classic ML engineer: the focus is building on top of foundation models, not training them.
Typical AI Engineer interview rounds
01
LLM & GenAI fundamentals
How transformers and LLMs work, tokens, embeddings, context windows and prompting.
02
RAG & application design
Retrieval-augmented generation, vector databases, chunking and grounding.
03
Coding round
Python coding plus building an API around an LLM with a framework like LangChain.
04
System design & evaluation
Designing a production AI feature, handling cost, latency, safety and evaluation.
Commonly asked AI Engineer questions
What is the difference between fine-tuning, prompt engineering and retrieval-augmented generation?
Explain how RAG works end to end, from a user query to a grounded answer.
What are embeddings and how are they used in semantic search?
What is a vector database and how does similarity search work?
What is a context window, and how do you handle inputs that exceed it?
What causes hallucinations in LLMs and how do you reduce them?
What is the difference between temperature and top-p sampling?
How would you chunk documents for a RAG pipeline, and why does chunk size matter?