Atlassian Cloud Platforms & Architecture interview questions

Most candidates can sell serverless. The question asks you to argue against it, which needs a real understanding of the execution model rather than the marketing.

Sessions, locks and pub/sub are three very different contracts with the same server. Knowing which guarantees each one lacks is the difference between a cache and an outage.

Rapid function scaling can overwhelm a shared dependency. Concurrency limits, reserved capacity and queue buffering are the containment toolkit.

Origin access control pins the bucket to your distribution, and signed URLs or cookies decide who plays what. Get the expiry wrong and the leak comes back with a signature on it.

Account-per-team is the right instinct, but hand-rolled account setup drifts within a month. Vending turns governance into a pipeline with an SLA instead of a ticket queue.

Managed Kubernetes offers several responsibility models. Application availability remains yours even when the provider also manages nodes and networking, and the interview tests whether you know where that line sits.

Asked in almost every cloud screen, and most candidates recite the table without ever naming the failure it causes. The ephemeral port range is the whole answer.

Turns the standard autoscaling definition into the problem it actually causes. Almost every candidate can define an HPA; far fewer can say why theirs did not save them.

The question that separates console users from people who have debugged a permissions failure across accounts. Evaluation order is deterministic and knowing it makes AccessDenied readable.

A design question with a clear answer at this scale, and the reasoning is what scores. The three options solve different problems and peering and Transit Gateway connect networks while PrivateLink exposes services.

Trace the route and return path before changing rules. First establish whether the failing connection uses IPv4, IPv6 or a private service endpoint.

The junior-filter question that still separates candidates who understand the model from those who memorised a definition. The answer is three mechanical differences and one economic one.

S3 access failures are rarely about the bucket policy alone. The caller, object ownership and network path determine which authorization checks apply. Start with the failed request and trace changes across those boundaries.

Database redundancy still needs reconnecting clients. Distinguish the deployment type, then measure how promotion and DNS changes affect application requests.

Spreading instances across availability zones is not the same as surviving an AZ failure. The candidates who know this can name the four coupling points that quietly re-unify your blast radius.

The services-and-enterprise interview staple. There is a defensible answer and it is not 'always re-platform': the decision is a function of change tolerance, deadline reality and what the workload is worth.

DNS failover is a blunt instrument with three delays stacked on top of each other: detection, propagation and client caching. Knowing their sizes tells you when DNS is the right layer at all.

The cheapest storage class is rarely the cheapest answer. Retrieval pricing, minimum durations and per-transition fees decide whether a lifecycle rule saves money or manufactures waste.

Large object uploads are a reliability design problem, not a bandwidth one. Parts, retries and the completion call decide whether a flaky link costs one chunk or the whole night.

Volume choice is IO-shape matching: burst buckets, provisioned IOPS and throughput designs behave very differently under mixed OLTP-plus-batch load. Average latency graphs hide the failure entirely.

Ask whether Multi-AZ means a DB instance or a DB cluster. One has a non-readable standby; the other has two readers. That distinction decides whether the proposal can relieve the primary.