Question 1
Difficulty: medium
Tell me about a Google Cloud project you designed or supported end to end. What decisions did you make and why?
Sample answer
In my last role, I helped migrate a customer-facing application from on-premises infrastructure to Google Cloud. I started by mapping the application dependencies, traffic patterns, and compliance needs, then designed a landing zone with separate projects for dev, test, and production. I chose a shared VPC model to centralize networking controls while still giving teams enough independence. For compute, we used GKE for the services that needed portability and autoscaling, and Cloud Run for a few stateless APIs where we wanted faster deployment cycles. I also worked closely with security to define IAM roles, service accounts, and audit logging from the start rather than bolting them on later. The biggest win was reducing deployment time from hours to minutes and improving reliability through managed services, clear monitoring, and infrastructure as code. What I learned is that cloud design is rarely just about picking products; it’s about balancing speed, governance, and long-term operational simplicity.
Question 2
Difficulty: medium
How do you design a secure Google Cloud environment for multiple teams with different access needs?
Sample answer
I usually start with the principle of least privilege and build the environment around it. In Google Cloud, that means using separate projects or folders for different teams and workloads so access boundaries are clear. I define IAM roles as narrowly as possible, preferring predefined roles when they fit and custom roles when they don’t. I also rely on service accounts for workloads instead of sharing user credentials, and I make sure access is reviewed regularly. On the network side, I use shared VPCs, firewall rules, and private access where appropriate to reduce exposure. For sensitive data, I pair IAM with Cloud KMS, Secret Manager, and data classification policies. I’m also careful with logging and monitoring because security without visibility is incomplete. I’ve found that if you design the environment well upfront, teams can move quickly without constantly requesting exceptions. Good security in Google Cloud should feel structured, not restrictive.
Question 3
Difficulty: hard
Describe a time you had to troubleshoot a production issue in Google Cloud. How did you approach it?
Sample answer
We had a production incident where API latency suddenly increased, but only for certain regions. I treated it as a layered problem and started with Cloud Monitoring dashboards to identify whether the bottleneck was compute, network, or downstream services. The metrics showed that request volume was normal, but error rates were rising on one service in GKE. I checked logs in Cloud Logging and found that the service was timing out when calling a third-party endpoint. The root cause turned out to be a change in DNS resolution and a poorly tuned timeout setting after a deployment. I rolled back the change, increased visibility with better tracing, and worked with the team to add circuit-breaking and retries with backoff. After the incident, I added more alerting around latency and external dependency health. The key for me was staying methodical: verify the symptom, isolate the layer, confirm with logs and metrics, then fix the immediate issue and harden the system afterward.
Question 4
Difficulty: medium
How would you choose between Compute Engine, GKE, Cloud Run, and App Engine for a new workload?
Sample answer
I’d choose based on operational control, scalability needs, deployment style, and team expertise. If the workload needs full OS-level control, custom networking, or specialized software, Compute Engine is often the right choice. If the application is containerized and the team wants orchestration, autoscaling, and more control over runtime behavior, GKE is strong, especially for more complex microservices architectures. For stateless services, APIs, or event-driven workloads where I want minimal operational overhead, Cloud Run is usually my first option because it’s simple, fast to deploy, and scales very well. App Engine can still make sense for teams that want a more opinionated platform and have a straightforward web application. In practice, I don’t pick based on hype; I look at the service’s lifecycle, traffic patterns, statefulness, and how much the team wants to manage. I also think about cost and whether the platform will make future maintenance easier, not just the first deployment.
Question 5
Difficulty: medium
What is your approach to infrastructure as code in Google Cloud?
Sample answer
I treat infrastructure as code as the foundation for consistency and change control. In Google Cloud, I’ve used Terraform extensively because it gives me a clean way to manage projects, IAM, networks, service accounts, and even Kubernetes resources in a repeatable format. My approach is to keep modules small and reusable, so teams can consume standardized building blocks rather than copy-pasting configurations. I also make sure code is reviewed like application code, with version control, pull requests, and automated validation before anything is applied. For sensitive environments, I separate state management carefully and restrict who can change production resources. I’ve found that IaC helps most when paired with clear naming conventions and environment separation, because then it becomes much easier to audit and troubleshoot. The biggest benefit is reducing drift. If something changes manually, it eventually becomes a support issue, so I prefer to encode the desired state and let automation enforce it.
Question 6
Difficulty: hard
A team wants to move a monolithic application into Google Cloud quickly. How would you plan the migration?
Sample answer
I would avoid trying to “cloudify” everything at once. My first step would be to assess the application’s dependencies, deployment process, data stores, and risk profile. Then I’d choose a migration path based on urgency and business value. If speed matters most, I’d consider a lift-and-shift approach into Compute Engine or possibly GKE if the app is already container-friendly. That gives the team breathing room while we improve the architecture in phases. I’d also separate the database migration plan from the app migration because data usually carries the most risk. During the transition, I’d establish observability, backup and recovery, and a rollback plan before cutover. After the initial migration, I’d look for opportunities to modernize one component at a time, such as moving batch jobs to Cloud Run or replacing self-managed services with managed alternatives. In my experience, the best migrations are staged, measurable, and aligned to business priorities rather than driven purely by technical preference.
Question 7
Difficulty: medium
How do you monitor and optimize cost in Google Cloud without hurting performance or reliability?
Sample answer
I start by making cost visible to the teams that actually run the workloads. That means proper labeling, project-level budgets, and alerts so spend doesn’t become a surprise at the end of the month. From there, I look at usage patterns and identify whether resources are overprovisioned, idle, or misconfigured. For example, in GKE I’ll review cluster sizing, node pool utilization, autoscaling settings, and whether workloads can safely use spot or preemptible capacity. In Compute Engine, I check machine types, committed use discounts, and whether instances are left running unnecessarily. I’m careful not to make changes blindly, though. I always compare cost recommendations against latency, availability, and operational risk. Sometimes a slightly more expensive setup is justified if it reduces outages or support overhead. My goal is not simply to minimize spend, but to spend intentionally. The best savings usually come from better architecture, not from cutting resources until the system becomes fragile.
Question 8
Difficulty: hard
What steps would you take to improve reliability for a Google Cloud application serving critical users?
Sample answer
I would focus on removing single points of failure and making failure less visible to the user. That starts with architecture: use multiple zones or regions where appropriate, avoid tightly coupling services, and ensure databases and storage are configured for recovery. I’d also review autoscaling so the system can absorb spikes without manual intervention. Observability is another major piece: I want meaningful dashboards, SLOs, error budgets, and alerts tied to user impact rather than just raw resource metrics. For deployment reliability, I’d use gradual rollouts, health checks, and rollback automation so a bad release doesn’t take down the service. I also like to test disaster recovery, because a plan that hasn’t been practiced is usually incomplete. If the workload is truly critical, I’ll ask hard questions about RTO and RPO early and design to those targets. Reliability is not a single feature in Google Cloud; it’s the result of consistent design choices, testing, and operational discipline.
Question 9
Difficulty: medium
How do you work with developers, security, and operations teams when cloud requirements conflict?
Sample answer
I try to frame the conversation around business risk and delivery impact instead of team preferences. In cloud projects, conflicts usually happen because each group optimizes for something different: developers want speed, security wants control, and operations wants stability. My job is to translate those needs into an implementation that satisfies all three as much as possible. For example, if developers want broad permissions for convenience, I’ll look for ways to automate deployment through service accounts and CI/CD rather than granting direct access. If security is concerned about exposure, I’ll propose private networking, tighter IAM, or logging controls that reduce risk without blocking progress. I’ve found it helps to bring data into the discussion, like audit findings, incident history, or performance metrics, so the decision is not based on opinion alone. I also make sure tradeoffs are documented. Strong collaboration in cloud work means being practical, respectful, and willing to revisit decisions as the system matures.
Question 10
Difficulty: hard
If a Google Cloud service you rely on has an outage, how would you respond and communicate with stakeholders?
Sample answer
First, I’d confirm the scope and impact using Google Cloud’s status information, internal monitoring, and logs from our own environment. Then I’d determine whether the issue is a regional problem, a specific API degradation, or something in our configuration that is amplifying the outage. If there is a workaround, I’d prioritize service continuity by shifting traffic, disabling the affected dependency, or using a fallback path. At the same time, I’d communicate early and honestly to stakeholders: what we know, what users are seeing, what the temporary mitigation is, and when the next update will come. I don’t wait until I have the perfect answer before speaking up. During incidents, trust matters as much as technical recovery. After the event, I’d run a postmortem focused on lessons learned and action items, not blame. I’d also update our architecture if needed so the same dependency doesn’t create repeated pain. The goal is to respond calmly, keep people informed, and reduce repeat exposure.
