The bigram part was fine, just a frequency map.
Start by clarifying the problem scope and constraints, then outline a simple counting-based approach using a hash map of hash maps. Discuss how to handle sampling proportionally, edge cases, and potential improvements like smoothing or scaling.
Pro tip: Mention that you would use a prefix tree or nested dictionaries for efficient storage and that sampling can be done via cumulative distribution or reservoir sampling for large vocabularies. Also, note that this is a Markov model and discuss how to extend it to higher orders.
Ask about the size of the dataset, vocabulary size, memory limits, and whether sequences are independent. Confirm that the model is a bigram model and that sampling should be proportional to observed frequencies.
Use a nested hash map (dictionary) where the outer key is the current word and the inner key is the next word, with the count as the value. Alternatively, use a single hash map with a composite key (current_word, next_word).
Iterate through each token sequence, and for each adjacent pair of words, increment the count in the data structure. Handle start and end tokens if needed.
For a given current word, retrieve its next-word counts, compute the total count, and sample the next word with probability proportional to its count. This can be done by generating a random number between 0 and total, then iterating through the counts cumulatively.
Address unseen words (e.g., use a fallback or smoothing), memory efficiency for large vocabularies (e.g., use sparse representations), and potential speed improvements (e.g., precomputing cumulative distributions).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and requirements, then propose a hybrid approach that combines algorithmic optimizations (e.g., pruning, caching) with system-level techniques (e.g., sharding, compression). Emphasize trade-offs between memory, latency, and accuracy, and justify your choices based on the specific constraints.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how Google's systems (e.g., Google Search's autocomplete) handle large vocabularies with techniques like distributed caching and approximate algorithms. Also, quantify the impact of your proposed solutions (e.g., 'This reduces memory by X% at the cost of Y% latency increase').
Ask questions to understand the scale (vocabulary size, successor count), memory limits, latency SLA, and acceptable trade-offs (e.g., accuracy vs. speed).
Analyze where memory and latency issues arise: storing the full vocabulary, successor lists, or lookup operations. Consider read/write patterns and access frequency.
Suggest techniques like pruning low-probability successors, using approximate data structures (e.g., Bloom filters, Count-Min Sketch), or leveraging compression (e.g., trie compression, quantization).
Propose distributed storage (sharding), caching (LRU, Redis), and precomputation to reduce latency. Discuss consistency and fault tolerance.
Compare solutions based on memory, latency, accuracy, and complexity. Recommend a hybrid approach and outline how to measure and monitor performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.