Банк вопросов из реальных собеседований

One fact about the mine appears in the annual report, presentation and call transcript. How to combine these sources into one forecasting state?

How do you move from revenue, seller success, and buyer value to offline pricing model metrics or recommendations?

Design the end-to-end pipeline for a RAG system: data preparation, vector index ingestion and serving-time retrieval.

A deployed ML service has 300 ms latency, but the product now needs 30 ms. What do you investigate and what optimizations can you try?

You replace brute-force nearest-neighbor search with a Qdrant HNSW index in an offline recommendation pipeline. What design and operational choices matter?

You replace brute-force nearest-neighbor search with a Qdrant HNSW index in an offline recommendation pipeline. What design and operational choices matter?

What line should the stream processor write to the aggregate store for the panel?

What are the differences between the W8A8 and W4A16 diagrams for the LLM application, and which indicators should be compared before choosing?

How to select and adapt a text encoder for queries like “walker crosses the road at night”?

A speech-AI pipeline needs fast analytical queries over training-data processing events. What requirements would you give DevOps before asking for ClickHouse?

How can you increase LLM serving throughput or batch size on the same GPU without buying a larger GPU?

What techniques help to obtain a stable, verifiable and safe result in the work system?

What products can not be recommended in the cart and at what stage to apply the restrictions: when searching for candidates, filtering or re-ranking?

The model can evaluate the price, discount, utility of the carousel or promo tags. How exactly to determine the output of the model and the subsequent product action?

A bank wants to launch a lifestyle social feed without ads. What goals, metrics and guardrails would you define before designing the recommender?

What components transform a language model into an agent and for what tasks is the cycle of external actions justified?

What do you do when an Airflow DAG brakes, freezes, or fails to fit into a scheduled window?

What approaches are there for training a large neural network on several GPU and how do they differ?

What is a reliable pipeline for you and how to verify that it is reliable?

What is a reliable pipeline for you and how to verify that it is reliable?

The LLM-agent product already has an offline benchmark: for each change you can see whether the metric has become better or worse. How to turn the evaluation results into a cycle of improving the system, without slipping into blind automatic optimization for a noisy benchmark?

Design a system that, based on the photo and metadata of the ad, determines that different cars have appeared in the card or car history.

How do FSDP, tensor parallelism and pipeline parallelism differ when training large models?

How do FSDP, tensor parallelism and pipeline parallelism differ when training large models?