Mock Deloitte GenAI Engineer Interview (30 Mins)

🔹 Part 1: Introduction & Project (10 min)

Q1. Tell me about your recent project in Generative AI.
👉 They want:

A business context (who benefits, what problem).
Tech stack (LLM, LangChain, embeddings, vector DB, cloud, etc.).
Your role (not “team did this” → say I implemented chunking, embeddings…).
Outcome (accuracy, time saved, cost reduced).

Q2. What challenges did you face in implementing GenAI?
👉 They expect examples like:

Hallucinations → handled via RAG / prompt engineering.
Large docs → solved via chunking + embeddings.
Cost/latency → solved via quantization / caching.
Data privacy → anonymization, masking, on-prem deployment.

🔹 Part 2: Technical / GenAI Concepts (10 min)

Q3. What is RAG (Retrieval Augmented Generation)? Why is it useful?
👉 They expect:

“RAG = combining external knowledge (retrieved via embeddings/vector DB) with LLM response.”
Solves hallucination, up-to-date info, domain-specific Q&A.

Q4. How do you handle PDFs containing images?
👉 They expect:

Use OCR (Tesseract, Azure Form Recognizer, Google Vision API) to extract text.
Then chunk → embeddings → retrieval.

Q5. Explain prompt engineering techniques you have used.
👉 They expect examples:

Few-shot prompting.
Chain-of-thought prompting.
Role/task-based prompts (“You are an HR assistant…”).
Guardrails (“Always answer in JSON format…”).

Q6. What is an imbalanced dataset? How do you handle it?
👉 They expect:

Definition: one class dominates others.
Techniques: oversampling (SMOTE), undersampling, class weights, synthetic data generation.

Q7. What is the F1 Score? Why not just accuracy?
👉 They expect:

Harmonic mean of precision & recall.
Used when data is imbalanced.

🔹 Part 3: Quick Coding/SQL Check (5–7 min)

Q8. (Python) Write code to print even and odd numbers from a list.

nums = [1, 2, 3, 4, 5, 6, 7, 8, 9]

evens = [n for n in nums if n % 2 == 0]
odds = [n for n in nums if n % 2 != 0]

print("Evens:", evens)
print("Odds:", odds)

Q9. (SQL) How do you remove duplicates from a table?

DELETE FROM my_table t1
WHERE EXISTS (
  SELECT 1
  FROM my_table t2
  WHERE t1.id > t2.id AND t1.col1 = t2.col1
);

👉 They expect: you know ROW_NUMBER(), DISTINCT, or GROUP BY approaches.

🔹 Part 4: Behavioral / Fit (2–3 min)

Q10. Why Deloitte for GenAI?
👉 They expect:

“Deloitte has strong focus on AI consulting + large enterprise clients.”
“I want exposure to diverse industries & real business problems.”
“Opportunity to work end-to-end (strategy → build → deploy).”

Q11. How do you stay updated with GenAI?
👉 They expect:

“Arxiv, HuggingFace papers, OpenAI/Anthropic blogs, newsletters like TLDR AI, LinkedIn/X communities.”
Maybe mention side projects, hackathons, or content creation (if true).

✅ That’s a realistic 30-min flow. Deloitte wants to see:

You understand GenAI concepts & real use cases.
You can code basic problems.
You can explain projects in business terms.