~6–8 hours (elite interview preparation)

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🎯 Why This Lecture Exists

Top tech companies do not test tools.
They test thinking.

This lecture simulates:

• OpenAI
• Google DeepMind / Gemini
• Anthropic
• Meta FAIR
• Microsoft Research

Focus:

• Fundamentals
• Architecture
• Training
• Evaluation
• Safety
• Systems thinking

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🧠 Part I — Core LLM Architecture (Q1–Q10)

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Q1 (MCQ)

Why are most modern LLMs decoder-only?

A. Encoders are too slow
B. Decoders can model autoregressive generation
C. Encoders cannot scale
D. Decoders use less memory

Answer + Explanation

B. Decoder-only models naturally support autoregressive next-token prediction, which aligns perfectly with text generation.

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Q2 (Objective)

What does “autoregressive” mean in LLMs?

Answer + Explanation

Predicting the next token conditioned on all previous tokens; generation proceeds sequentially.

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Q3 (MCQ)

What mask is used in decoder self-attention?

A. Padding mask
B. Causal (look-ahead) mask
C. Bidirectional mask
D. Cross-attention mask

Answer + Explanation

B. Causal masks prevent the model from seeing future tokens during training.

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Q4 (Objective)

Why are encoders still useful in multimodal systems?

Answer + Explanation

Encoders excel at representation learning (images, audio, documents) which can be fused into LLMs.

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Q5 (MCQ)

Which model is encoder–decoder?

A. GPT-4
B. LLaMA
C. T5
D. PaLM

Answer + Explanation

C. T5 uses an encoder–decoder Transformer architecture.

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Q6 (Objective)

What is the role of positional encoding?

Answer + Explanation

It injects token order information into attention-based models which are otherwise permutation-invariant.

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Q7 (MCQ)

Why is self-attention preferred over RNNs?

A. Faster training
B. Parallelism
C. Long-range dependency modeling
D. All of the above

Answer + Explanation

D. Self-attention improves speed, scalability, and contextual understanding.

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Q8 (Objective)

What limits context length in Transformers?

Answer + Explanation

Quadratic attention cost in sequence length (O(n²)).

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Q9 (MCQ)

Which improves long-context handling?

A. FlashAttention
B. Sparse attention
C. RoPE
D. All of the above

Answer + Explanation

D. Each addresses efficiency or extrapolation in long contexts.

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Q10 (Objective)

Why is decoder-only dominant for chat models?

Answer + Explanation

It unifies understanding and generation into a single autoregressive process.

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🔥 Part II — Training & Fine-Tuning (Q11–Q20)

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Q11 (MCQ)

What is the pretraining objective of GPT-like models?

A. Masked language modeling
B. Next token prediction
C. Sentence classification
D. Contrastive loss

Answer + Explanation

B. GPT models are trained to predict the next token autoregressively.

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Q12 (Objective)

Why is pretraining so expensive?

Answer + Explanation

It requires massive datasets, compute, and long optimization cycles.

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Q13 (MCQ)

What does fine-tuning change?

A. Model architecture
B. Tokenizer
C. Weights
D. Loss function only

Answer + Explanation

C. Fine-tuning updates weights to adapt behavior.

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Q14 (Objective)

What is catastrophic forgetting?

Answer + Explanation

When fine-tuning overwrites previously learned knowledge.

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Q15 (MCQ)

Which method reduces forgetting?

A. Lower learning rate
B. Freezing layers
C. LoRA
D. All of the above

Answer + Explanation

D. Each constrains weight updates.

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Q16 (Objective)

What is LoRA?

Answer + Explanation

Low-Rank Adaptation: fine-tuning via small rank-decomposed matrices.

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Q17 (MCQ)

Why freeze base model weights?

A. Save memory
B. Prevent overfitting
C. Preserve general knowledge
D. All of the above

Answer + Explanation

D. Freezing improves stability and efficiency.

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Q18 (Objective)

Difference between instruction tuning and pretraining?

Answer + Explanation

Instruction tuning aligns model behavior to human instructions rather than raw text prediction.

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Q19 (MCQ)

What does RLHF optimize?

A. Accuracy
B. Likelihood
C. Human preference
D. Latency

Answer + Explanation

C. RLHF aligns outputs with human feedback.

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Q20 (Objective)

Why is RLHF unstable?

Answer + Explanation

Reward models are imperfect and can be exploited.

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🧠 Part III — Systems, Safety & Evaluation (Q21–Q35)

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Q21 (MCQ)

What causes hallucination most?

A. Small models
B. Lack of grounding
C. Bad tokenizer
D. Low temperature

Answer + Explanation

B. Hallucination arises from missing or unverified knowledge.

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Q22 (Objective)

How does RAG reduce hallucination?

Answer + Explanation

By grounding generation in retrieved external knowledge.

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Q23 (MCQ)

Which metric is worst for reasoning?

A. BLEU
B. ROUGE
C. Exact Match
D. Accuracy

Answer + Explanation

A. BLEU focuses on surface n-gram overlap.

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Q24 (Objective)

Why is human evaluation critical?

Answer + Explanation

Humans judge meaning, usefulness, and harm beyond metrics.

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Q25 (MCQ)

What is alignment?

A. Model speed
B. Model size
C. Matching human values
D. Token efficiency

Answer + Explanation

C. Alignment ensures AI behaves consistently with human intent.

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Q26 (Objective)

Why is safety not solved by data alone?

Answer + Explanation

Values are contextual, evolving, and require judgment.

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Q27 (MCQ)

Which is an agent failure?

A. Wrong answer
B. Tool misuse
C. Infinite loop
D. All of the above

Answer + Explanation

D. Agents introduce new failure modes.

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Q28 (Objective)

Why must agents be logged?

Answer + Explanation

For debugging, auditing, and accountability.

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Q29 (MCQ)

What is temperature?

A. Training speed
B. Randomness control
C. Model size
D. Loss scaling

Answer + Explanation

B. Temperature controls output diversity.

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Q30 (Objective)

Why is low temperature risky?

Answer + Explanation

It can amplify confident but wrong answers.

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Q31 (MCQ)

Which improves long-context reasoning?

A. Bigger model
B. Better data
C. Memory mechanisms
D. UI design

Answer + Explanation

C. Memory and retrieval matter more than size.

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Q32 (Objective)

Why is evaluation harder than training?

Answer + Explanation

Correctness is ambiguous, contextual, and human-dependent.

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Q33 (MCQ)

What is distribution shift?

A. Token drift
B. Deployment data differs from training
C. Model collapse
D. Optimizer bug

Answer + Explanation

B. Real-world data rarely matches training data.

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Q34 (Objective)

How do you detect silent failures?

Answer + Explanation

Stress tests, adversarial inputs, and monitoring.

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Q35 (Objective)

Why is abstention important?

Answer + Explanation

Saying “I don’t know” prevents harm and hallucination.

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🌍 Part IV — Research Mindset (Q36–Q50)

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Q36 (MCQ)

What makes a strong LLM researcher?

A. Model size obsession
B. Tool mastery
C. Question formulation
D. Coding speed

Answer + Explanation

C. Research starts with the right questions.

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Q37 (Objective)

Why is ablation important?

Answer + Explanation

It isolates which components actually matter.

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Q38 (MCQ)

What does “scaling law” describe?

A. Inference speed
B. Relationship between compute, data, performance
C. Model compression
D. Tokenization

Answer + Explanation

B. Scaling laws guide resource allocation.

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Q39 (Objective)

Why are smaller models still relevant?

Answer + Explanation

They are cheaper, faster, safer, and deployable.

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Q40 (MCQ)

What is the biggest unsolved problem?

A. Accuracy
B. Speed
C. Alignment
D. UI

Answer + Explanation

C. Alignment is fundamentally human and societal.

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Q41 (Objective)

Why is interpretability important?

Answer + Explanation

To trust, debug, and regulate AI systems.

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Q42 (MCQ)

What does “emergent behavior” mean?

A. Bugs
B. Overfitting
C. Capabilities appearing at scale
D. Prompt tricks

Answer + Explanation

C. New abilities emerge non-linearly with scale.

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Q43 (Objective)

Why are benchmarks insufficient?

Answer + Explanation

They fail to represent real-world complexity.

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Q44 (MCQ)

What defines a good LLM system?

A. Model size
B. Latency
C. User trust
D. Parameter count

Answer + Explanation

C. Trust defines real adoption.

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Q45 (Objective)

Why must humans stay in the loop?

Answer + Explanation

AI lacks values, responsibility, and moral judgment.

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Q46 (MCQ)

What will differentiate future LLMs?

A. Bigger GPUs
B. Better prompts
C. Better systems & alignment
D. More tokens

Answer + Explanation

C. Systems and alignment matter more than scale.

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Q47 (Objective)

What mindset do interviewers seek?

Answer + Explanation

Clarity, humility, rigor, and responsibility.

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Q48 (MCQ)

What is a red flag in interviews?

A. Admitting uncertainty
B. Asking questions
C. Overconfidence
D. Thoughtful pauses

Answer + Explanation

C. Overconfidence signals lack of depth.

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Q49 (Objective)

Why is “I don’t know” powerful?

Answer + Explanation

It shows intellectual honesty and growth mindset.

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Q50 (Final Reflection)

What makes a great LLM engineer?

Answer + Explanation

Someone who combines technical mastery, ethical responsibility, and human-centered thinking.

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🌱 Final Words

You are not training models.
You are shaping intelligence.

Build wisely.
Question deeply.
Stay human.

❤️

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