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This part felt like a pop quiz with no clear scope.
Structure your answer by first clarifying the scope (e.g., which CS and ML topics are expected), then systematically cover core concepts from both areas, linking them to practical software engineering scenarios. Use concrete examples and trade-offs to demonstrate depth and relevance to the role.
Pro tip: Tie each fundamental concept to a real-world application or trade-off at Salesforce (e.g., scalability, data handling) to show you understand how theory translates to production systems.
Ask the interviewer which specific CS and ML areas they want to focus on (e.g., algorithms, data structures, supervised learning, optimization) to tailor your answer.
Discuss key CS concepts such as time/space complexity, data structures (arrays, trees, graphs), algorithm paradigms (divide-and-conquer, dynamic programming), and system design basics.
Explain core ML concepts like bias-variance trade-off, regularization, evaluation metrics, model selection, and common algorithms (linear regression, decision trees, neural networks).
Relate CS and ML concepts to software engineering practices, such as writing efficient code, designing scalable systems, and integrating ML models into production.
Briefly recap key points and ask if the interviewer wants to dive deeper into any specific area, showing engagement and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They were pretty specific about the data structure upfront, which actually helped.
Start by clarifying the requirements: O(1) average time for both get and put, and the need to evict the least frequently used key, breaking ties by least recently used. Then outline the data structures: a key-to-node hashmap for O(1) access, a frequency-to-bucket hashmap where each bucket is a doubly linked list of nodes with the same frequency, and a min-frequency pointer. Finally, walk through the get and put operations, explaining how nodes move between frequency buckets and how eviction works.
Pro tip: Mention that using a doubly linked list for each frequency bucket allows O(1) removal and insertion, and that maintaining a min-frequency pointer avoids scanning for the least frequency. Also, discuss edge cases like updating an existing key and handling capacity 0.
Confirm that both get and put must be O(1) average time, and that eviction is based on least frequency, with LRU as tiebreaker. Ask about capacity constraints and whether keys/values are integers.
Propose two hashmaps: one mapping keys to nodes (for O(1) access), and another mapping frequencies to doubly linked lists (buckets) of nodes with that frequency. Maintain a min_freq variable to track the lowest frequency in the cache.
If key exists, retrieve its node, update its frequency (move it to the next frequency bucket), adjust min_freq if necessary, and return the value. If not, return -1.
If key exists, update its value and increase its frequency (similar to get). If key is new, check capacity: if full, evict the least frequent node (from min_freq bucket, LRU order). Then insert the new node with frequency 1, and update min_freq to 1.
Explain that all operations are O(1) average due to hashmap lookups and constant-time linked list manipulations. Discuss edge cases: capacity 0, updating existing key, and tie-breaking with LRU.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.