Prepare for Anthropic Software Engineering

Write self-documenting and type-safe code using Python's `typing` module and dataclasses.

Differentiate between standard dicts, `collections.OrderedDict`, and `collections.defaultdict` for various use cases.

Implement efficient queue-based and sliding-window algorithms using `collections.deque`.

Simplify frequency counting and data aggregation tasks using `collections.Counter`.

Write efficient and expressive iteration logic using key `itertools` utilities like `chain`, `islice`, and `groupby`.

Apply `functools` utilities, including `lru_cache` for memoization and `partial` for function specialization.

Implement robust error handling using custom exceptions and clear input validation.

Map concurrency patterns from Rust/Tokio (e.g., channels, mutexes) to their Python equivalents in `threading` and `asyncio`.

Implement a hash map from scratch using chaining for collision resolution.

Extend the hash map to support dynamic resizing based on a load factor, analyzing performance trade-offs.

Implement a doubly linked list supporting efficient insertion, deletion, and move-to-end operations.

Implement a thread-safe, production-quality LRU Cache from scratch using a hash map and a doubly linked list.

Justify the O(1) time complexity of the `get` and `put` operations in the from-scratch LRU Cache.

Compare the from-scratch LRU cache implementation with one using `collections.OrderedDict`, focusing on trade-offs.

Implement an LFU cache and articulate the scenarios where it is more suitable than an LRU cache.

Implement a thread-safe LFU cache and discuss the increased complexity compared to a thread-safe LRU cache.

Explain the impact of the GIL and determine when to use `threading`, `multiprocessing`, or `asyncio`.

Implement thread-safe counters and data structures using `threading.Lock` and `threading.RLock`.

Analyze and implement both coarse-grained and fine-grained locking strategies, discussing their trade-offs.

Implement the producer-consumer pattern using `threading` with thread-safe queues and condition variables.

Write asynchronous code using `asyncio` coroutines, tasks, and event loops.

Manage concurrency levels in `asyncio` applications using semaphores.

Re-implement the producer-consumer pattern using `asyncio` queues and tasks.

Systematically identify and resolve race conditions and deadlocks in concurrent Python code.

Implement a token bucket rate limiter and compare its Python implementation with Rust patterns used in systems like Rudi.

Implement a fixed-window counter rate limiter, explaining its memory efficiency and boundary condition issues.

Implement a sliding-window log rate limiter, detailing how it improves accuracy over the fixed-window approach.

Implement a sliding-window counter rate limiter as a memory-optimized alternative to the sliding-window log.

Compare the trade-offs (accuracy, memory, complexity) between token bucket, fixed-window, and sliding-window algorithms.

Design a distributed rate limiting system using an external store like Redis, focusing on atomicity.

Implement an adaptive rate limiting strategy that adjusts limits based on system health metrics.

Design and implement degraded response strategies (e.g., queueing, shedding) for managing system overload.

Model real-world problems such as task dependencies and resource allocation as graphs.

Implement Breadth-First Search (BFS) and Depth-First Search (DFS) and explain their distinct use cases.

Implement topological sort on a Directed Acyclic Graph (DAG) using Kahn's algorithm (BFS-based).

Implement topological sort on a DAG using a DFS-based approach.

Compare the trade-offs between Kahn's algorithm and the DFS approach, including for parallel execution.

Implement a robust cycle detection algorithm for a directed graph to handle invalid inputs.

Adapt graph traversal algorithms to correctly handle disconnected components and self-loops.

Solve a shortest path problem on a weighted graph using Dijkstra's algorithm.

Identify optimal substructure and overlapping subproblems to determine when to apply dynamic programming.

Solve a classic 1D dynamic programming problem using both top-down memoization and bottom-up tabulation.

Solve a classic 2D dynamic programming problem, analyzing the time and space complexity of the solution.

Solve problems by applying binary search on a monotonic answer space.

Solve 'next greater element' style problems efficiently using a monotonic stack.

Solve sliding window minimum/maximum problems in linear time using a monotonic queue (deque).

Solve top-K and 'merge k-sorted lists' problems using heaps (priority queues).

Analyze the time and space complexity trade-offs of heap-based solutions versus sorting-based alternatives.

Implement the core GET/SET/DELETE operations for an in-memory key-value store.

Extend the key-value store to support Time-To-Live (TTL) with a strategy for evicting expired keys.

Build a simple task scheduler that processes tasks in FIFO order using a queue.

Evolve the scheduler to support priority levels by replacing the FIFO queue with a min-heap.

Extend the priority scheduler to model task dependencies using a DAG.

Integrate a topological sort algorithm into the scheduler to process dependent tasks in a valid order.

Implement a cascading cancellation feature where cancelling a task also cancels all its dependents.

Add robust error handling to the scheduler, such as detecting circular dependencies upon task creation.

Parse a `robots.txt` file to determine if a given URL is allowed for a specific user-agent.

Implement URL normalization to handle relative links, fragments, and query parameters.

Build a single-threaded web crawler using BFS traversal with link extraction and visited URL tracking.

Convert the single-threaded crawler to a concurrent version using `asyncio` for fetching multiple URLs.

Integrate a semaphore-based rate limiter into the concurrent crawler to respect server policies.

Add robust timeout handling to the concurrent crawler for unresponsive servers.

Implement redirect loop detection to prevent the crawler from getting stuck in infinite chains.

Design a persistence layer for the crawler's state (frontier, visited set) to allow for resuming after a crash.

Explain how tokenization (e.g., BPE) works and why token count is the fundamental unit of LLM workload.

Explain the role and memory implications of the KV cache in accelerating autoregressive inference.

Explain why batching is critical for GPU throughput and how padding impacts efficiency.

Design a dynamic batching strategy that balances latency and throughput by grouping requests.

Design a memory management strategy for the KV cache to handle concurrent, variable-length requests.

Design a system for streaming token-by-token responses, comparing WebSocket, SSE, and chunked transfer.

Design an autoscaling policy for the inference fleet based on token-aware queue depth, not just GPU utilization.

Evaluate end-to-end trade-offs across the serving stack: time-to-first-token, throughput, and cost.

Describe typical input vs. output token pricing models and the rationale for their cost difference.

Calculate the cost for various request patterns, such as summarization vs. few-shot prompting.

Design the data model and API for reporting token usage from a single, non-streaming LLM response.

Extend the usage reporting API with an idempotency mechanism to prevent duplicate billing.

Adapt the API design to support incremental usage reporting for streaming responses.

Design a data model for storing aggregated token counts that supports efficient querying by user, model, and time.

Describe technical strategies for reducing token costs, such as prompt engineering and caching.

Design a cost-aware request routing system that balances model quality, latency, and token cost.

Explain how word embeddings represent semantic meaning as dense vectors.

Describe the self-attention mechanism step-by-step: creating queries, keys, and values, and computing scaled dot-product attention.

Explain how multi-head attention allows the model to attend to different information subspaces simultaneously.

Describe the role and implementation of positional encodings in capturing sequence order.

Diagram a full transformer decoder block, explaining the role of each component (attention, FFN, layer norm, residuals).

Describe the process of autoregressive language modeling (next-token prediction) and its role in GPT-style pretraining.

Compare common decoding strategies (greedy, beam search, top-k/top-p sampling) and the effect of temperature.

Explain quantization as a model compression technique and its trade-offs between speed, memory, and quality.

Define the AI alignment problem and give an example of why it becomes more difficult with more capable models.

Describe key AI risk categories (e.g., misuse, misalignment) and provide a concrete example for each.

Explain the Reinforcement Learning from Human Feedback (RLHF) pipeline for model alignment.

Describe Constitutional AI (CAI) and explain how it reduces reliance on human preference labels for safety tuning.

Explain the goals of mechanistic interpretability and how techniques like circuit analysis help us understand models.

Describe common LLM failure modes like hallucination, jailbreaking, and prompt injection.

Explain the core ideas behind Anthropic's Responsible Scaling Policy (RSP) and AI Safety Levels (ASL).

Articulate a thoughtful position on a trade-off between model capability, safety, and speed of deployment.