Airbnb Systems Engineer (Mid-Level) Interview Preparation Guide

Direct answer
Put the ask, the essential context, and the next step in the first two sentences, so a busy reader can act on the email even if they only read the opening before deciding whether to reply now or later.

Structured elaboration

1. State the ask as the first sentence, not buried after several paragraphs of context: "I'd like to request temporary access to X" or "Could you approve Y by Thursday?"
2. Give only the essential context, one or two sentences of why this ask exists, not the full backstory. Include it because it makes the ask easier to say yes to quickly, not because it's interesting.
3. State the next step explicitly: what you need them to do, and by when, so they don't have to infer the deadline or the required action.
4. Use the subject line to state the ask, not just the topic: "Approval needed by Thursday: Q3 budget line" tells the reader more than "Budget question."
5. Keep the whole email short. If the request genuinely needs more context, put the essential ask up top and the detail below it, rather than making the reader wade through detail to find the ask.

Worked example
Subject: "Access request: prod DB read access, needed by Wednesday"

Body: "Could you grant me temporary read access to the orders table in prod? I'm investigating a customer-reported data discrepancy (ticket #4821) and need to check actual row values, which I can't do in staging since the issue only reproduces with real production data. Happy to have this access time-boxed to a few days and revoked afterward if that's easier to approve."

The ask (temporary read access) and the deadline context (needed by Wednesday) are in the subject line alone; the body confirms the specific ask, gives the minimum context needed to approve it, and proactively offers a constraint (time-boxed) that makes approval easier.

Trade-offs and pitfalls

• Leading with a long justification before the ask is the single most common failure; a reader has to hold the whole paragraph in their head waiting to find out what you actually want.
• Too little context can also fail: an ask with zero justification can force the reader to ask a clarifying question back, which is slower than including the one sentence of context that would have let them approve it immediately.
• For sensitive or high-stakes asks (a large budget approval, access to something risky), a slightly longer, more carefully justified email is worth the extra length; the "front-load the ask" principle still applies, it just means front-loading a well-justified ask rather than skipping justification entirely.

Direct answer

Cache-aside, read-through, write-through, and write-behind differ in who is responsible for populating the cache and when a write becomes durable: cache-aside puts that responsibility on the application, read-through/write-through push it into the caching layer itself, and write-behind trades immediate durability for write throughput.

Structured elaboration

• Cache-aside (lazy loading): on read, the application checks the cache; on a miss, it reads from the datastore and populates the cache itself. On write, the application writes to the datastore and either invalidates or updates the cache entry. The application owns all the logic; the cache is a dumb key-value store. This is the most common pattern because it fails gracefully (if the cache is down, reads just go straight to the datastore) and only caches what is actually requested.
• Read-through: functionally similar to cache-aside from the caller's perspective, but the cache library/layer itself knows how to fetch from the datastore on a miss, so the application only ever talks to the cache. This centralizes the fetch logic but requires a caching layer that supports it.
• Write-through: every write goes to the cache first (or simultaneously), and the cache synchronously writes through to the datastore before acknowledging. Reads are always fresh because the cache is never behind the datastore, at the cost of write latency (you pay for both writes on every request) and caching data that may never actually be read.
• Write-behind (write-back): writes go to the cache and are acknowledged immediately; the cache asynchronously flushes to the datastore in the background (often batched). This gives the best write throughput and latency, at the cost of a durability window: a crash between the acknowledged write and the flush can lose data unless the write queue itself is durable.
• Picking one, by use case: a product catalog with heavy reads and occasional updates fits cache-aside well (simple, only caches what's actually browsed). A durability-sensitive write path (a payments ledger) generally avoids write-behind's data-loss window and prefers write-through or a cache-aside pattern with synchronous invalidation.

Worked example

For a product catalog service: cache-aside is a strong default. On a product-detail read, check Redis; on miss, query the database and populate Redis with a time-to-live (TTL); on a price update, write to the database and then delete (or update) the cached entry so the next read repopulates it. This avoids caching the 90+ percent of the catalog nobody is currently browsing, unlike write-through, which would populate the cache for every single write regardless of read demand.

Trade-offs and pitfalls

Cache-aside has a well-known race: a read that misses, starts fetching from the datastore, and finishes AFTER a concurrent write has already invalidated the cache, can re-populate the cache with the now-stale value it fetched before the write. Write-through eliminates staleness but adds write latency and can cache "dead weight" (data nobody reads). Write-behind's throughput win is real but its durability trade-off must be an explicit decision, not a default; never use write-behind for data where losing the last few seconds of writes is unacceptable.

Direct answer

Keep alignment across time zones with three levers: shrink what actually needs real-time overlap by defaulting to async updates on a fixed template, protect a small deliberately scheduled overlap window for anything that truly needs live discussion, and make handoffs explicit in writing so context transfers cleanly across the boundary instead of depending on someone's memory.

Framework

Reduce dependence on overlap. Default to async status updates on a fixed cadence, and use written decision docs rather than requiring a live meeting for every decision. Most updates don't need a room, only genuinely ambiguous or high-stakes calls do.

Protect a deliberate overlap window. Negotiate a recurring block, even a short one, and rotate who takes the inconvenient time so the burden doesn't always fall on the same region.

Make handoffs explicit. When work crosses a time-zone boundary, produce a short written artifact rather than relying on a quick chat message. This matters most in ops-heavy, always-on contexts.

Worked example

Consider an on-call rotation providing 24/7 production coverage across three time zones (for example [Region A], [Region B], and [Region C]), where the two outer regions have little or no live overlap with each other.

• Shadow and overlap periods: the incoming region's on-call shadows the outgoing region's on-call for a short deliberate window at the shift boundary, even 15 to 30 minutes, to ask questions live before the outgoing engineer signs off.
• Written handoff template: a standard document filled at every handoff covering open incidents, any systems in a degraded state, changes deployed in the last shift, and explicit 'known risk' or 'do not touch' notes.
• Escalation expectations: a written policy defining what counts as page-worthy versus a handoff note, who the secondary on-call is in each region, and how long the incoming engineer has to acknowledge before it auto-escalates.
  Result: even with zero live overlap between two of the three regions, the written handoff plus the short shadow window from the middle region means each incoming on-call starts already briefed, instead of reconstructing state from raw logs.
  For non-ops roles the same mechanism applies with a different artifact, for example a design or product handoff might be a written decision log plus a recorded walkthrough rather than an incident handoff, but the principle (explicit written handoff over a live conversation) is the same.

Trade-offs and pitfalls

• Repeatedly scheduling occasional syncs at painful hours burns out whichever time zone draws the short straw. Rotate it deliberately.
• Async-only breaks down for genuinely ambiguous or high-stakes decisions. Some live channel for true emergencies still has to exist.
• A handoff template that's too heavy gets skipped under time pressure. Keep it short enough to fill in within a few minutes.
• Assuming a chat message counts as a handoff is the actual failure mode this whole approach is designed to prevent. The structured artifact is the point, not the tool it's written in.

What I'd want to know first

Before I put a number on anything, I'd pull four things: the traffic profile (is it steady, bursty, or spiky through the day), the read/write mix (reads and writes scale very differently), the resource cost per request (how much CPU, memory, and database load one typical request consumes), and the existing SLA (service level agreement) or error budget (how much latency and error headroom I'm actually allowed to spend). Skipping this step and jumping straight to "just double everything" is the most common mistake, because growth is rarely uniform across every resource.

Headroom calculation

Say the service peaks at 3,000 requests per second (RPS) today, running on 20 instances. Load testing shows each instance holds a safe ceiling of about 175 RPS before p99 latency (the 99th percentile response time, the slowest 1 in 100 requests) starts climbing. That puts the fleet's tested ceiling at 20 x 175 = 3,500 RPS, so today's headroom is about 500 RPS, roughly 14% of the tested ceiling (500 of 3,500 RPS still unused).

If traffic doubles in 6 months, peak becomes 6,000 RPS. I'd size for that peak plus a buffer for forecast error, say 20%, giving a target ceiling of 7,200 RPS. At 175 RPS per instance that's 7,200 / 175 = about 42 instances, roughly doubling the fleet plus a bit more because the buffer also grows.

Scaling strategy

For a stateless web tier fronting a database, horizontal scaling (adding more instances of the same size) is usually the right default: no single-instance ceiling to hit, and it's cheaper to add commodity capacity than to keep buying bigger boxes. But I'd check the database write path separately, since it often becomes the actual constraint before the web tier does. If the database is the binding resource, the plan has to include read replicas, connection pooling limits, or a write-path change, not just more web servers.

What I'd benchmark

• A steady-state load test at the projected 6,000 RPS peak with the real production read/write mix, not a synthetic uniform load.
• A soak test (sustained load over hours) to catch memory leaks and connection pool exhaustion that only show up over time.
• A burst test that ramps quickly to the new ceiling, since sustained-load tests can hide how the system behaves under a sudden spike.

Cost versus performance

If each instance costs roughly $140/month, the fleet grows from $2,800/month to about $5,880/month, a cost increase of around 110% to support a 100% traffic increase, which is close to linear and reasonable. If the math came out sharply super-linear (say 300% cost for 100% traffic), that would push me to look at right-sizing the instance type or fixing an inefficiency instead of just buying more of the same box.

Direct answer
Blue-green, canary, and rolling all reduce the risk of a bad release, but through different mechanisms: blue-green switches ALL traffic at once between two full environments, canary exposes a small SLICE of traffic to the new version before widening it, and rolling replaces instances gradually IN PLACE. A plain recreate deployment, by contrast, tears down the old version entirely before starting the new one, accepting downtime in exchange for simplicity.

Structured elaboration

Rollback complexity nuance: blue-green's rollback is fastest because the old environment never stopped running; canary and rolling both have to actively redeploy or re-route, which takes real time even if it's automated; recreate's "rollback" is simple mechanically but means accepting a second period of downtime.

Stateful services: all three of blue-green/canary/rolling get significantly harder with state (a database, in-memory session data, local disk), because you can't just duplicate or partially expose the data layer the way you can stateless compute; the deployment strategy for the STATELESS layer often decouples from a separate, more careful strategy for the DATA layer.

Worked example
A stateless API fronting a shared database: canary is a strong default, since it limits blast radius on the code change while the shared database (which doesn't get canaried the same way) stays constant underneath. Blue-green would be a better fit if the team's top priority is minimizing time-to-rollback over minimizing blast radius, since flipping back to the old environment is close to instant.

Trade-offs and pitfalls
There's no universally "best" strategy: the choice trades off blast radius, rollback speed, infrastructure cost, and operational complexity, and the right answer depends on which of those the specific service and change profile cares about most. A common mistake is picking a strategy based on what's trendy (everyone reaches for canary) rather than what the actual risk profile of the change calls for; a low-risk config change might not need any of this ceremony at all.

Direct answer

Give the startup a small set of measurable triggers, not a vibe: sustained traffic growth that vertical scaling can no longer absorb, a build or deploy pipeline slow enough to block multiple teams, incidents where one team's unrelated change repeatedly takes down another team's feature, and enough independent teams that they're routinely waiting on each other to ship. If none of those are true yet, stay on a well-structured monolith and invest in automation instead; splitting before any trigger fires adds real operational cost for a benefit the team can't cash in yet.

Structured elaboration

Triggers, with what each one actually signals

Default for an MVP-stage team
For a brand-new MVP with one or two engineers and no confirmed product-market fit yet, none of these triggers are even reachable: default to a single, well-organized modular monolith (one deployable codebase with clear internal module boundaries), because splitting now means guessing at service boundaries before there's usage data to draw them correctly, and redrawing a wrong boundary between two live services is far more expensive than redrawing it between two modules in one codebase.

When triggers do fire
Extract incrementally using the strangler pattern (pulling one bounded, high-value piece out from behind the existing interface at a time), named here without re-deriving its mechanics, and check that team structure already matches the boundary being proposed (Conway's Law, named only): if a small team doesn't already own the candidate service end to end, extracting it just relocates the coordination problem onto the network.

Worked example

A 25-person engineering org split into four product teams sees average deploy lead time climb past 45 minutes as all four teams queue behind one release train, and in the last quarter, three of nine production incidents were an unrelated team's change breaking a different team's feature through shared code. That's two of the four triggers above (deploy lead time, blast radius) firing at once, on an org that already has team boundaries to extract along (the third trigger). This combination, not any single signal alone, is what justifies picking one bounded, high-value capability, say the search or recommendations code, since it is already the most independently used and owned piece, as the first strangler-pattern extraction, rather than a big-bang rewrite of the whole system into services.

Trade-offs & pitfalls

• Extracting the first service based on which code is oldest or ugliest rather than which extraction actually relieves a measured trigger.
• Splitting without the operational maturity (CI/CD automation, monitoring, on-call ownership) to run more than one deployable thing, which adds cost with no offsetting benefit.
• Treating "we might need to scale eventually" as a trigger on its own; without a load number or a deploy-lead-time number attached, it's speculation, not evidence.
• What separates a senior answer: naming the first service to extract and why, based on a specific measured pain point, rather than describing microservices in the abstract.

Direct answer. Don't attempt a wholesale replacement; introduce modern ownership incrementally at module boundaries, converting the highest-risk (most leak-and-crash-prone) areas first behind tests that pin current behavior, while leaving stable, rarely-touched raw-pointer code alone until it needs to change anyway.

Why a full rewrite is the wrong first move

A legacy C++ service with 'sporadic memory bugs' by definition has memory-safety issues you don't fully understand yet; a wholesale rewrite risks introducing NEW bugs in code you didn't need to touch, while the actual crash-causing code might be a small fraction of the codebase. Prioritize by where the actual bugs are, not by 'convert everything to modern C++.'

A phased plan

1. Instrument first: add crash reporting/AddressSanitizer (or Valgrind) runs in CI and on a canary population to identify WHERE the sporadic bugs actually originate, rather than guessing.
2. Establish characterization tests around the highest-risk modules before changing them, so a refactor that accidentally changes behavior is caught immediately.
3. Convert ownership at module boundaries first: wrap raw pointers crossing an API boundary in std::unique_ptr/std::shared_ptr as appropriate, which fixes the highest-leverage bugs (use-after-free across module boundaries) without requiring every internal raw pointer to be touched simultaneously.
4. Introduce a style/lint rule going forward (banning bare new/delete in new code, requiring smart pointers) so the codebase stops GROWING the problem while the existing backlog is worked down.
5. Convert remaining raw-pointer code opportunistically: whenever a module needs a change anyway (a bug fix, a feature), take the opportunity to modernize its ownership as part of that change, rather than scheduling a separate 'convert everything' project that competes with feature work indefinitely.

Reducing risk while modernizing

• Prefer std::unique_ptr by default (single, clear ownership) and reserve std::shared_ptr for cases with genuinely shared ownership -- reaching for shared_ptr everywhere just because it's 'safer' than raw pointers often just relocates the bugs into reference-cycle leaks instead.
• Where full RAII conversion of a subsystem is too risky to do at once, a smart-pointer WRAPPER around the existing raw-pointer API can buy safety at the boundary while internals are migrated later.
• Track 'sporadic' bugs with sanitizer tooling in a staging environment under load, since many use-after-free/double-free bugs only manifest under specific timing or allocator conditions that a quick manual test won't reproduce.

Trade-offs and pitfalls

• Mixing raw pointers and smart pointers during the transition is itself a hazard: a raw pointer obtained from a unique_ptr::get() that outlives the unique_ptr is a new use-after-free waiting to happen -- be explicit about which code owns what during the coexistence period.
• Don't declare victory once compilation is clean; a memory-safety migration needs sanitizer-backed testing under realistic load to actually confirm the sporadic bugs are gone, not just that the code still builds.

Direct answer
I treat a company's published culture and values as a claim to be tested, not a fact to accept, and I look for evidence in three places: how people describe real, specific incidents (not slogans) when I ask about them, whether the org's actual structures and incentives would make the stated behavior easy or hard to practice, and whether the story is consistent across different people I talk to in the process.

Structured elaboration

1. Ask for a specific recent incident, not a description of the value. A question like "tell me about a time the team had to choose between shipping fast and following the documented review process" forces a real story; a question like "how would you describe the engineering culture here" invites a rehearsed, values-page-adjacent answer that tells you little.
2. Check whether the org's structure actually supports the stated value, independent of what anyone says. If a company claims to value psychological safety but every interviewer you meet is visibly guarded about naming any team problem, or if a company claims strong autonomy but every technical decision in the loop turns out to require a director's sign-off, the structural evidence contradicts the claim regardless of the wording used to describe it.
3. Triangulate across multiple people, ideally at different levels and tenures. A single enthusiastic interviewer proves little; a hiring manager, a peer-level engineer, and someone from a different function independently describing the same specific behavior (not the same slogan) is much stronger evidence.
4. Ask what the company would do differently if it stopped believing the value, and watch for a concrete, structural answer versus a vague one. People who work inside a genuinely lived value can usually name a real trade-off it costs them; people describing marketing usually cannot.
5. Treat your own discomfort as data. If a described norm (pace, feedback directness, decision-making style) makes you visibly uneasy during the process itself, that is a more reliable signal about fit than anything printed on the careers page, because it is your own live reaction rather than a claim you are being asked to evaluate secondhand.

Worked example
Suppose a company's careers page says it "empowers engineers with high autonomy." During the loop, ask the hiring manager for a specific recent example: "Tell me about the last time an engineer on this team made a production architecture decision without it going through a review committee first." A genuine, lived-autonomy answer sounds like: "Last quarter one of our engineers decided independently to switch a service from synchronous to async processing after noticing latency complaints; she looped in two people for a sanity check, shipped it, and reported the outcome in the next team sync." A marketing-only answer sounds like: "We really believe in empowering our engineers," repeated with no specific incident when pressed twice. If a peer engineer you speak to separately can also describe a comparable specific incident in their own words, that consistency is strong corroborating evidence; if the hiring manager's story turns out to be the ONLY example anyone can produce company-wide, that is itself informative about how common the behavior actually is.

Trade-offs & pitfalls
The main failure mode is accepting an interviewer's fluent, confident description of the culture as sufficient evidence on its own; confidence and specificity are not the same thing, and a well-rehearsed answer to a values-page question is exactly what a company under-delivering on its stated culture is most likely to have prepared. A second pitfall is over-weighting a single glowing anecdote from one enthusiastic interviewer without checking whether it generalizes; one great story is an anecdote, not a pattern. A third is treating any inconsistency you find as automatically disqualifying: it is normal for a large or growing organization to have real variance across teams, so the useful conclusion is usually about the SPECIFIC team and manager you'd actually join, not the company as a monolithic whole.

Direct answer

Eventual consistency with compensating transactions is not viable as the primary mechanism for an e-wallet's ledger. A compensating transaction undoes a wrong debit or credit after the fact, and for money that is a regulatory and trust failure, not merely a rough edge in the user experience. That leaves a real choice between a single-region serializable relational database with synchronous replicas, and a multi-region consensus database using a protocol such as Raft or Paxos (algorithms that get a group of replicas to agree on the order of operations even if some are slow or unreachable). The deciding question is the recovery point objective (RPO, the maximum amount of recently committed data the business can tolerate losing) if an entire region is lost. If losing zero committed transactions even when a whole region disappears is a hard requirement, choose multi-region consensus despite its latency cost. If a well-rehearsed failover to a synchronous standby in a nearby region is an acceptable answer to that same question, single-region-plus-synchronous-standby is simpler to operate correctly and faster on the common path.

Structured elaboration

Single-region serializable relational database with synchronous replicas. Run the ledger under the strictest available isolation level (serializable, which prevents subtle bugs such as two concurrent withdrawals both reading a balance before either has committed and both approving), with one or more synchronous standbys nearby that must acknowledge a write before it is considered committed. A primary failure that fails over successfully loses zero committed data. The remaining exposure is a disaster that takes out the primary and all synchronous standbys together, at which point failover falls back to an asynchronous, farther-away replica, and the last few in-flight transactions can be lost. That gap is the RPO this option accepts.

Multi-region consensus (Raft or Paxos). Every write replicates to a quorum (an agreeing majority of the replica set, enough that any two possible quorums must share at least one replica) of replicas spread across regions before it is acknowledged, so losing any single region, including the one currently holding the leader, cannot lose committed data. RPO is zero by construction. The cost is that every write pays the network round trip needed to reach that quorum, bounded by the slower of the required acknowledgments, not avoidable simply by adding more replicas within the same regions, and the team now operates a genuinely different topology: region-aware placement decisions and a different mental model for reasoning about failure.

Eventual consistency with compensating transactions. Appropriate for the parts of a wallet product that are not the ledger itself, for example refreshing a cached risk score or propagating a promotional balance display, but not for the record of who owns what money. Even a well-designed saga (a multi-step transfer broken into a sequence of local transactions, each with its own compensating "undo" action, instead of one all-or-nothing global transaction) accepts a window where the system is visibly in an in-progress, not-yet-reconciled state, and for a ledger that window is exactly where double-spend and overdraft bugs live. Use this pattern only to orchestrate a multi-step transfer between two internally consistent ledgers, never as the consistency model of a ledger by itself.

Worked example

Absent any further constraint, default to single-region serializable with synchronous standbys: it is simpler to operate correctly, and a correctly operated simpler system beats a theoretically stronger but easily misconfigured one. Now quantify the alternative's cost. Light in optical fiber travels at roughly 68% of the speed of light in vacuum, about 203,900 km/s. For a route of roughly 5,900 km, a rough distance between the eastern United States and western Europe, one-way propagation alone is 5,900 / 203,900 * 1000 ~= 28.9 ms, so a round trip is ~57.9 ms before routing and serialization overhead, which typically adds further tens of milliseconds. If a regulator or a board-level continuity mandate genuinely requires that no committed transaction can be lost even if an entire region vanishes, paying roughly that extra 60 or so milliseconds of write latency, turning a 15-millisecond transfer into something closer to 75 milliseconds (15 ms + ~60 ms), is the acceptable price, and multi-region consensus is the correct call.

Trade-offs and pitfalls

A pitfall is choosing multi-region consensus reflexively because "more resilient" sounds safer, without pricing the latency into the product. A wallet transfer that now takes ten times longer is a real user-facing regression, and if the team cannot name the specific business requirement that extra time buys, that is a sign the requirement was never actually stated, just assumed.

A second pitfall is treating "serializable" as a checkbox rather than a specific property to verify. Some database engines implement serializable isolation through optimistic conflict detection, meaning transactions can abort and must be retried under contention, which changes what the application's error handling has to do, not just a configuration setting.

What would flip the recommendation further: a wallet operating in a single legal jurisdiction with no cross-region regulatory driver, and a documented, signed-off tolerance for losing the last few seconds of transactions in the vanishingly rare event of a total regional disaster. That is precisely when single-region-plus-synchronous-standby is not just acceptable but the better choice, because it is the option the team can run correctly with less operational complexity.

Direct answer

Alert fatigue is what happens when on-call engineers get so many low-value, noisy, or duplicate pages that they start treating all alerts as probably-not-real, including the ones that matter. It's a trust problem as much as a technical one: once someone has been paged repeatedly in a night for something that turned out to be nothing, the next page, which might be the real incident, gets a slower, more skeptical response.

How I'd prevent it on a team I'm leading

• Deduplication and grouping: alerts that share a root cause (same service, same error type) should collapse into a single incident with a count, not fire a separate page per occurrence. This is usually a config change in the alerting tool (fingerprinting by service and error signature) rather than a code change.
• Severity tuning tied to required response time: not every alert deserves a page. A three-tier split (page now, notify during business hours, dashboard-only) forces every new alert to justify why it needs to interrupt someone's sleep.
• Actionable-by-default policy: no new paging alert ships without a linked runbook and a clear "what to check first." An alert with no next step is a dashboard panel that accidentally has a pager attached.
• Automated remediation for known, safe, repeatable fixes: if the same alert reliably resolves by restarting a stuck worker or clearing a queue, and that action is safe and idempotent, automate it and only page if the automated fix fails.
• A regular noise review: periodically look at which alerts fired most often and whether they led to real action; alerts that never lead to action get tuned or removed, not left running indefinitely out of habit.

Worked example

Suppose a team's on-call rotation is getting paged for "queue depth over 100" on a background job processor, firing several times a week, always self-resolving within a few minutes without anyone doing anything. Applying the framework above: first, check whether these spikes line up with a predictable traffic pattern (a nightly batch job, say) and if so, either raise the threshold above that expected peak or add a time-of-day exception. Second, if the queue really can back up unpredictably but always self-resolves within a known window without intervention, the alert should require a longer sustain window (e.g. "queue depth over 100 for 15 minutes") so it only fires when it isn't going to resolve on its own. Third, if manual intervention when it does page is always the same action (scale up worker count), that's a strong automated-remediation candidate: auto-scale on the same threshold, and only page if depth is still elevated after the auto-scale has had time to take effect.

Trade-offs and pitfalls

• Automated remediation without an audit trail or human confirmation for higher-severity cases can turn a noisy-alert problem into a silent-failure problem: the system "fixes" itself repeatedly while masking a root cause that's getting worse.
• Tuning thresholds purely to reduce page volume, without checking against real past incidents, risks quietly increasing false negatives; the goal is signal-to-noise, not just fewer pages.
• Alert fatigue prevention is not a one-time project. It needs an ongoing review cadence, because new alerts get added faster than old noisy ones get cleaned up if nobody owns the process.