Google Cloud and TPU Coding for Infra interview questions

Two phases of N-1 steps each, a chunk index that rotates with the step, and a byte count that should come out to exactly the textbook formula. The implementation, the verification against a direct sum, and the measured traffic matching 2(N-1)/N times the message at every rank count tested.

The tiling is ten lines and the theory is a working-set calculation. The interesting part is that the obvious benchmark does not measure what it appears to, because the library underneath is already tiled, and knowing which effect your numbers contain is the skill being tested.

Three decisions decide whether this is correct: the numeric type, how partial hours are handled, and what happens to a record that does not make sense. The implementation with its tests, the floating-point trap that costs real money, and the questions to ask before writing any of it.

Two parameters, one lazy refill and one lock. What capacity and rate each control and why conflating them is the usual bug, the clock choice that avoids a whole class of failure, and the retry-after value that turns a rejection into something a client can act on.

The union is a sort and a scan. The intersection is the same sweep with a counter, and it is the one that goes wrong, because a single GPU reporting overlapping intervals will be counted twice and produce an answer that looks plausible. Both implementations, the trap, and the tests that catch it.

Exponential backoff spaces one client's retries and does nothing for a thousand clients that failed together, which is the case that matters. The measured difference jitter makes to the peak arrival rate, the two budgets that bound the damage, and the classification of what is safe to retry at all.

Per key, an ascending list of versions and a parallel list of values, with reads doing a binary search. The three cases that decide whether the design is right: a key that did not exist yet, a key that was deleted, and a version at which some other key was written.

Two triggers, one shared queue, and a timer task that will deadlock the whole thing if it cancels itself. The implementation with the bug I hit and its fix, the latency the window costs, and the error path that decides whether one bad batch fails one caller or all of them.

A heap orders the queue and the interesting logic is elsewhere: which running jobs may be evicted, how many, and what happens to them. The victim-selection rule that avoids evicting more than necessary, the equal-priority case that must not preempt, and the starvation the design creates if nothing ages.

The classification is a small function; the parser around it is where the problem is. Generators rather than lists, aggregation bounded by the number of distinct instructions rather than by the file, a top-k that never sorts the whole thing, and the malformed lines that stop the job at hour three.

Entries differ in size by a factor of fifty, so a count-based cache is meaningless, and evicting the model currently serving requests is a correctness bug rather than a performance one. Size accounting, pinning, and the two failures that must raise rather than silently evict.

The queue is four lines and the shutdown is where it goes wrong. One sentinel per consumer rather than one for all of them, an exception path that cannot silently kill a worker, and a bound that must be small enough to actually apply backpressure.

Three one-line policy differences produce different fragmentation, and the metric that separates them is not utilization. The measured outcome on the same job sequence, why the largest free block is what matters on a training cluster, and the case where the intuitive policy is exactly wrong.

A four-byte emoji split across three tokens will raise on every naive decode, and the fix is a buffer that holds the incomplete tail. The decoder in a dozen lines, the exception field that tells you exactly where to cut, and what to do when the stream ends mid-character.

A free list, a per-sequence block table, and a reference count are the whole structure. What forking costs, why the first write after a fork must copy, and the measured waste that this design accepts in exchange for eliminating the far larger waste of pre-allocation.

Admission, a step that grows every sequence, and preemption under memory pressure. The loop is short and it has a failure that only appears when the cache is tight: in a measured run it preempted seventeen times and completed nothing, because every preempted sequence restarted from zero.

Unbounded goroutines are the default failure: ten thousand shards means ten thousand concurrent connections and an immediate rate limit. The semaphore pattern, the context that makes cancellation propagate, and the two mistakes that produce a leak nothing reports.