Your organization runs a production application pinned to claude-3-haiku-20240307. Anthropic announces a 6-month deprecation window for this model version. Your application processes 2 million classification requests per day with strict latency SLAs. What is the most operationally sound deprecation migration strategy?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — a is correct because shadow traffic testing with statistical comparison of output distributions identifies behavioral regressions without risking production SLAs. Staged rollout with automated rollback triggers is the industry-standard approach for high-volume, latency-sensitive migration.
Full Explanation
A is correct because shadow traffic testing with statistical comparison of output distributions identifies behavioral regressions without risking production SLAs. Staged rollout with automated rollback triggers is the industry-standard approach for high-volume, latency-sensitive migration. B is wrong because running both versions for only 30 days is arbitrary and does not validate output consistency statistically — it adds cost without structured evaluation. C is wrong because automatic forwarding is not guaranteed, and assuming classification backward compatibility at 2M req/day is a dangerous operational gamble that violates the principle of validating before relying on. D is wrong because claude-haiku-latest is not a supported API alias; Anthropic's API requires explicit version strings, so this approach would fail at the API call level.