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LLMOps Engineer
LLMOps Engineer
874 questions
Serves, scales, and monitors LLM systems, capacity, latency, observability, rollouts, on-call.
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Questions
You upsert a document and query for it 200 milliseconds later, but it never appears. Pick the most likely cause.
Multiple Choice
Easy
Fill in the missing term in this description of how HNSW handles deletes.
Fill in Blank
Easy
Select every symptom that points to tombstone bloat in a long running HNSW index.
Multi-select
Medium
Match each production search symptom to its most likely vector database root cause.
Match Pairs
Medium
Premium
A news product ingests…
Multiple Choice
Medium
Premium
Pick between int8 scalar…
Multiple Choice
Medium
Pick the right billing model for a search feature that bursts to 2,000 QPS one hour per day.
Multiple Choice
Easy
One billion vectors must live mostly on cheap storage with a 200 ms budget. Decide between SPANN style and DiskANN style designs.
Multiple Choice
Hard
Predict how often a 16 shard fan out query gets hit by at least one slow shard.
Predict Output
Medium
During a background reindex, p99 latency triples while p50 barely moves. Pick the explanation.
Multiple Choice
Medium
Production recall dropped overnight with no deploy. Order the investigation from cheapest check to most invasive.
Order Steps
Hard
Your nightly index rebuild takes 14 hours on CPU and the maintenance window is 2 hours. Pick the most direct fix.
Multiple Choice
Easy
Estimate the raw vector storage for 10 million 768-dim float32 embeddings before any index overhead.
Predict Output
Easy
Order the migration steps for moving 8M vectors from Pinecone to pgvector with zero query downtime.
Order Steps
Medium
Pick the workload where an object storage backed serverless vector database beats a RAM resident cluster on cost.
Multiple Choice
Medium
Predict the storage blowup when moving from single vector embeddings to ColBERT style multi-vector retrieval.
Predict Output
Hard
Order the zero downtime cutover sequence for swapping embedding models under live traffic with rollback safety.
Order Steps
Hard
Design the retrieval architecture when 95% of queries carry five or more metadata predicates of wildly varying selectivity.
Short Answer
Hard
Estimate the compression from truncating 3072-dim Matryoshka embeddings to 1024 dims and then binary quantizing.
Predict Output
Medium
A filter matches 0.1% of 100M vectors. Predict whether pre-filter brute force or post-filter ANN serves it faster.
Predict Output
Medium
Spot the flaws in this plan to upgrade embedding models by lazily overwriting old vectors in place.
Spot the Error
Medium
Premium
Diagnose why a pgvector…
Short Answer
Medium
Spot the error in this hybrid search scoring code that combines BM25 and cosine scores.
Spot the Error
Medium
Order the stages of a hybrid sparse plus dense query with reranking inside the database layer.
Order Steps
Easy
EU users see yesterday's documents while US users see today's. Debug this geo replicated vector deployment.
Multiple Choice
Medium
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