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Microsoft LLM & GenAI Fundamentals interview questions

LLM & GenAI Fundamentals is a core part of the Microsoft Forward Deployed Engineer loop. Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. Below are the llm & genai fundamentals questions to prepare, the ones tagged to Microsoft first, then the highest-signal questions from our LLM & GenAI Fundamentals track, each with an answer written to a senior-engineer bar.

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LLM & GenAI Fundamentals questions tagged to Microsoft

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More LLM & GenAI Fundamentals questions for Microsoft's loop

The highest-signal llm & genai fundamentals questions candidates rate most useful, modeled on what Microsoft's Forward Deployed Engineer loop tests.

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Concepts behind Microsoft's LLM & GenAI Fundamentals round

The vocabulary and mental models these questions assume. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

Foundational
Tokenization & TokensA language model does not read characters or words. It reads tokens: sub-word chunks produced by a tokenizer, each mapped to an integer the model embeds. Tokens are the unit of the context window and of billing, and the way text splits into them explains a surprising number of model quirks, which is why almost every loop opens here.
Foundational
The Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.
Foundational
Embeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.
Foundational
The Transformer, IntuitivelyThe transformer is the architecture behind every modern large language model, built on self-attention that lets each token look at every other token in parallel. FDE loops do not want the math; they want you to explain why attention beat RNNs, what decoder-only means, and why context length is expensive, in plain language an exec or a teammate can follow.
Core
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Attention and Self-AttentionAttention computes a weighted sum of value vectors, where the weights come from how well each token's query matches every other token's key. Self-attention applies this within one sequence so each token can pull from all the others, and the all-pairs comparison is why cost grows with the square of sequence length. FDE loops probe it because it explains context limits, latency, and the KV cache in one mental model.
Core
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RoPE and Positional EncodingsSelf-attention has no built-in sense of word order, so transformers inject position information into the token vectors. Rotary position embeddings (RoPE) rotate query and key vectors by a position-dependent angle so relative position falls out of the dot product, which is why RoPE underpins almost every long-context model and why extending a context window means rescaling RoPE.
Foundational
Temperature, Top-p and SamplingAt each step a model outputs a probability over every possible next token, and sampling settings like temperature, top-p, and top-k decide how that distribution is turned into an actual choice. FDE loops test this because it controls the determinism-versus-creativity dial: knowing when to set it low for extraction and high for brainstorming, and why the same prompt giving different answers is expected, not a bug.
Advanced
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LoRA and Parameter-Efficient Fine-tuningFull fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.
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MICROSOFT LLM & GENAI FUNDAMENTALS FAQ
What LLM & GenAI Fundamentals questions does Microsoft ask in interviews?▲

Microsoft's Forward Deployed Engineer loop draws llm & genai fundamentals questions such as "Explain temperature and top-p. When would you run temperature 0 in an enterprise workflow?", "You're building a classifier that flags risky contract clauses. Do you optimize for precision or recall?", "A customer says your LLM app is too slow. Give me five levers to reduce latency, and their tradeoffs.". Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. The full set, ordered easy to hard with expert answers, is below.

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