AWS & Cloud Architecture Interview Questions

Multi-AZ is the answer and the reason matters more than the acronym. Each tier fails differently, and the database is the one where high availability means a failover with a real gap, not zero interruption.

Four levels, and the two that matter operationally are the subscription, where quotas and billing live, and the resource group, which is the unit you delete. Getting the boundaries wrong is expensive to unpick later.

Service principal with a client secret is the answer people give and the one that leaves a credential in a config file. Managed identity is the same object with the secret handled by the platform.

One is layer 4 and regional, one is layer 7 and regional, one is layer 7 and global. Answer on those two axes and the choice makes itself, including when you need two of them together.

Two questions settle most of it: how much of the machine do you want to own, and is the workload request-shaped or long-running. Fargate is a way of running containers rather than a competitor to ECS and EKS, which is the distinction people get wrong.

Two structural differences matter more than any service mapping: the VPC is global rather than regional, and permissions are granted to members on resources through a hierarchy rather than attached to identities.

The comparison table is easy to recite and explains nothing about the failure the difference actually 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.

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.

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

A question that sounds introductory and turns on one distinction: what you stop operating, and what you start paying for instead. 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. What makes the difference is being able to name the four coupling points that quietly re-unify a blast radius you thought you had split.

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.

A busy date key can throttle while table capacity remains available. Check the actual key distribution and throttling reason before adding capacity or shards.

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.

Roles versus users looks like syntax trivia and is actually a secrets-management position. The follow-ups about instance profiles and IRSA separate practitioners from tutorial graduates.

Direct encryption hits a 4 KB wall and a shared throughput quota. Envelope encryption routes around both, and the key policy underneath is central to authorizing key use.

Fleet-wide replacement is a traffic-shaping problem: bake once, wave through in controlled batches, watch the alarms, and keep the exit open. In-place patching is the alternative and usually the regret.

Cross-account access is easy to grant and easy to get wrong. External IDs, scoped roles and audit trails separate a controlled bridge from an open door any tenant can walk through.

Service control policies cap what anyone in a member account can do, no matter what their IAM admins wrote. The craft is choosing a handful of denials that age well.

Gateway endpoints avoid NAT processing for supported traffic. Interface endpoints have hourly and processing fees; cost savings depend on traffic, while access restrictions need explicit policies.

Hybrid DNS fails in both directions independently. Outbound rules carry VPC queries to your data centre, inbound endpoints bring on-prem queries back here, and both halves need wiring.

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.

Queue, log and router solve different problems that all get called events. Picking by workload shape beats picking by whichever service the team already knows.

Orchestration buys a visible state machine with declared retries; choreography buys independence and coupling debt. Money paths usually deserve the state machine.

Postgres spends memory on a backend process per connection, and serverless fan-out treats that as an invitation. Pooling at the proxy layer is the standard cure; pinning is the catch.

Strip the marketing and Aurora is a rewrite of the storage layer: the log is the database, committed to a quorum across three AZs. Know when that machinery pays and when it just bills.

Instance metadata is a local credential vending machine and SSRF is the coin slot. IMDSv2 raises the bar at the host; layered defences and blast-radius limits finish the job.

Residency is not a checkbox on the primary datastore. Where bytes sit, who holds keys, and where logs, backups and analytics quietly replicate decide whether the deal signs.

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.

Four tiers, four bills. Stating RTO and RPO per tier turns a vague resilience wish into a decision leadership can actually price, and the testing story is where candidates fold.

Latency budgets decide architecture, and most teams buy edge compute before earning it with caching and protocol work. Know which rung of the ladder your problem actually needs.

Quotas are invisible until launch day. Treating them as tracked inventory with utilization alerts beats discovering them in a postmortem.

Blue-green works for stateless apps because instances are disposable. Databases are shared state, so safe migrations ride on backward compatibility and an explicit rollback boundary.

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.

A dead-letter queue with no owner is a landfill with a nicer name. Triage discipline separates teams that recover messages from teams that archive regret.

Guaranteed capacity and discounted capacity are different products. Peak season needs both, arranged in layers and verified with a game day before the traffic arrives.

Allow-listing egress starts free with security groups and ends with TLS-breaking appliances. Each rung costs latency, money and political capital, and knowing where to stop is the actual test.

Active-active replication moves writes but not truth. Last-writer-wins quietly discards one of the two orders your finance team expects both to exist.

Data transfer is charged in several unrelated ways and reported as one line, so the first job is decomposition. Most surprises are cross-zone chatter between services that never needed to be on different nodes.

Four options with genuinely different costs, and the requirement is usually narrower than it first sounds. Establish whether it is about the network path, the data residency, or the provider's retention terms, because those need different answers.

The private endpoint can be healthy while an office resolver still returns the public address. Trace the lookup from the failing client before changing access rules.

Moving a Kubernetes service account changes its token subject. Separate pod mutation, federation trust and Key Vault authorization before adding permissions.

A cluster boundary does not always create a new cloud identity. Inspect the workload pool and principal identifier before assuming a leaked key.