Mock Interviews

Crack Your Next Data & AI Interview Before You Face the Real One

Explore Our Courses

Choose your target role – we’ll tailor questions, scenarios and feedback accordingly.

Who Is This Mock Interview For?

🚀 Data Engineering - Technical Interview

🎯 Focus: Judge how strong you are technically and how confidently you think under pressure.

Assessing SQL strength, analytical thinking, and coding skills
Problem-solving & DSA to evaluate logic-building capability
Technical depth check on core data engineering tech stack ( like Spark, NoSQL, Databricks, Kafka, Snowflake, Airflow, Azure/AWS/GCP )
Scenario-based debugging to test real-world mindset
Feedback on gaps, preparation roadmap & improvement areas

Coming Soon

Are You Interested?

✅ This plan includes one 60-minute mock interview session

🚀 Data Engineering - Design Interview

🎯 Focus: Judge if you think like a real-world Data Engineer who can design systems, not just code them.

Evaluating your ability to design scalable data pipelines
Testing warehouse modelling & data architecture thinking
Judging end-to-end thought process (latency, cost, SLAs, quality, governance)
Understanding how you pick right components and justify choices
Review of your thought clarity, trade-offs & design maturity

Coming Soon

Are You Interested?

✅ This plan includes one 60-minute mock interview session

🚀 AI / ML Engineer & Data Scientist – Junior Level (0–4 Years)

Focus: Evaluate your fundamentals plus your readiness to work with modern AI systems (LLMs & GenAI) at an implementation level.

Core ML fundamentals (regression, classification, clustering, evaluation metrics, bias–variance)
Statistics basics used in ML & data analysis
Data preprocessing, feature engineering & exploratory data analysis (EDA)
Hands-on skills in Python, Pandas, NumPy & SQL
Training, validating & interpreting machine learning models
Introductory deep learning concepts (neural networks, loss functions, embeddings)
Practical understanding of LLMs & GenAI basics (what LLMs are, prompts, tokenization, embeddings)
RAG fundamentals (why RAG is needed, high-level flow, basic vector search concepts)
Using tools and frameworks to build simple LLM-powered applications
Debugging ML & GenAI issues (hallucinations, poor retrieval, ineffective prompts)
Personalized feedback on concept gaps, projects & learning roadmap

Coming Soon

Are You Interested?

✅ This plan includes one 60-minute mock interview session

🚀 AI / ML Engineer & Data Scientist – Senior Level (5+ Years)

Focus: Judge whether you can design, scale, and own modern AI systems, not just train models.

End-to-end problem framing (business → data → ML/GenAI → impact)
Designing scalable ML & GenAI pipelines
Deep understanding of LLM-based system design
RAG system design & trade-offs (chunking strategy, embeddings, vector databases, retrieval vs generation balance)
Prompt engineering vs fine-tuning vs adapters — when and why to use each
Evaluation strategies for LLMs & GenAI systems
Handling latency, cost, observability & reliability in AI systems
Managing data drift, concept drift & continuous learning strategies
MLOps + LLMOps maturity (monitoring, versioning, rollback strategies)
Cloud & infrastructure choices for AI workloads
Stakeholder communication & justification of architecture decisions
Review of thought clarity, ownership mindset & system-level maturity

Coming Soon

Are You Interested?

✅ This plan includes one 60-minute mock interview session

Pick a track or combine multiple to build a custom learning journey for your teams.

How Does the Mock Interview Work?

Choose Role & Plan

Select your target role and choose a mock interview plan that fits your preparation stage.

Book a Slot

Pick a date & time from available mentor calendars using your registered email ID.

Face the Interview

Join the live interview, answer role-specific questions and experience a real interview environment.

Get Feedback & Plan

Receive a detailed scorecard + feedback + improvement roadmap to fix your weak areas.

Find Your Ideal Mentor

Book a mock interview with your mentor

Shashank Mishra

Staff Data Engineer @ Prophecy

Ex – Expedia, Amazon, PayTM, McKinsey & Company

Shashank Mishra is a Staff Data Engineer at Prophecy, bringing 8+ years of experience building large-scale data systems across Expedia, Amazon, PayTM, and McKinsey & Company. An MCA graduate from NIT Allahabad, he specializes in architecting modern data pipelines, cloud-native platforms, and real-time analytics at enterprise scale.

Beyond industry leadership, Shashank is a prominent educator — running the YouTube channel E-Learning Bridge (183K+ subscribers) and a LinkedIn community of 186K+ professionals. Over 20,000 students have been personally mentored by him, many securing offers from top global tech companies.

Known for his project-driven teaching style, he simplifies complex data engineering concepts into actionable, real-world learning. His mission is to empower the next wave of data talent to master tools, build production-grade solutions, and turn data into a catalyst for career-defining outcomes.

Shubhankit Sirvaiya

AI Engineer @ Fidelity International

Ex – World Wide Technology (WWT), Accenture

Shubhankit Sirvaiya is an AI Engineer at Fidelity International, with prior experience as an AI Engineer at World Wide Technology (WWT) and as a Data Scientist at Accenture Growth & Strategy. He has 5+ years of hands-on experience across Data Science, Machine Learning, MLOps, and AI systems, working on real-world, production-grade problems.

Alongside industry work, he has been teaching and mentoring for the last 4+ years, helping 1000+ students and professionals transition into Data Science and AI roles. He also runs an active LinkedIn community of 58k+ followers, where he shares practical insights around AI engineering, careers, and real industry expectations.

His teaching style focuses on story-based learning and connecting the dots — breaking down complex AI concepts into clear mental models so learners don’t feel lost in the vast and often overwhelming AI syllabus. His goal is not just to teach tools, but to help learners understand why things work and how they fit together in real systems.

Crack Your Next Data & AI Interview Before You Face the Real One

Who Is This Mock Interview For?

🚀 Data Engineering - Technical Interview

Coming Soon

🚀 Data Engineering - Design Interview

Coming Soon

🚀 AI / ML Engineer & Data Scientist – Junior Level (0–4 Years)

Coming Soon

🚀 AI / ML Engineer & Data Scientist – Senior Level (5+ Years)

Coming Soon

How Does the Mock Interview Work?

Choose Role & Plan

Book a Slot

Face the Interview

Get Feedback & Plan

Book a mock interview with your mentor

Shashank Mishra

Shubhankit Sirvaiya

Namaste 🙏