← Salesforce Interview Insights
This one felt broad at first and I wasn't sure how deep to go.
Start by defining the three models in terms of abstraction level and operational responsibility, then map equivalent services on GCP and AWS. Compare them across key dimensions like control, scalability, cost, and use cases, and finish with concrete examples of when you'd choose each, ideally tied to Salesforce's multi-cloud environment.
Pro tip: Emphasize that the choice is rarely about technology alone—it's about team maturity, operational overhead, and business constraints. Mention that serverless isn't always cheaper at scale and that containers often hit the sweet spot for portability and efficiency.
Briefly explain VMs (full control, heavy ops), containers (isolated, portable, moderate ops), and serverless (event-driven, zero ops, limited control).
List equivalents: VMs (GCE vs EC2), containers (GKE, Cloud Run vs EKS, ECS, Fargate), serverless (Cloud Functions, Cloud Run vs Lambda).
Discuss control, scalability, cold starts, pricing model, vendor lock-in, and operational burden for each model.
Give scenarios: VMs for legacy or specialized workloads, containers for microservices and portability, serverless for sporadic or event-driven tasks.
Summarize when to pick each, considering team expertise, budget, and long-term strategy, and note that hybrid approaches are common.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Gave a reasonable answer but I glossed over chaos testing because I've never actually run a chaos experiment in prod.
Structure your answer around the testing pyramid, starting with unit tests for individual components, then integration tests for service interactions, followed by load testing for performance and scalability, and finally chaos testing for resilience. Emphasize how these strategies work together to ensure reliability in cloud environments, and provide concrete examples from your experience.
Pro tip: Highlight the importance of observability and monitoring in production to complement pre-production testing, and mention how you use canary deployments and feature flags to safely roll out changes.
Focus on testing individual functions or modules in isolation, mocking external dependencies to ensure fast and reliable tests. Use frameworks like JUnit, pytest, or Jest.
Test interactions between services, databases, and external APIs in a staging environment that mirrors production. Use tools like Testcontainers or WireMock to simulate dependencies.
Simulate expected and peak traffic to measure performance, scalability, and resource usage. Use tools like JMeter, Locust, or Gatling, and define SLIs/SLOs.
Intentionally inject failures (e.g., network latency, instance crashes) to verify system resilience and recovery. Use tools like Chaos Monkey or Gremlin, and start in non-production environments.
Integrate all testing types into CI/CD pipelines and monitor production with observability tools to catch issues early and feed insights back into testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly separating the control plane and worker node responsibilities, then map each kubectl command to the underlying component it interacts with. Use a concrete deployment example to demonstrate the full lifecycle: deploy, scale, roll back, and debug. Keep the explanation structured and tie each action back to the architecture to show deep understanding.
Pro tip: Mention that kubectl talks to the API server, which is the only component that directly interacts with etcd—this shows you understand the control plane's internal communication flow. Also, highlight that rollbacks rely on ReplicaSet history, not on re-applying old YAML, which demonstrates practical maturity.
List the control plane components: kube-apiserver, etcd, kube-scheduler, kube-controller-manager, and cloud-controller-manager. Explain that they make global decisions and maintain cluster state.
List the worker node components: kubelet, kube-proxy, and container runtime (e.g., containerd). Explain that they run and manage pods and networking on each node.
Describe how kubectl apply sends a manifest to the API server, which persists it to etcd and triggers the scheduler and controller manager to create pods. For scaling, use kubectl scale to adjust replicas, which updates the Deployment's desired state.
For rollback, use kubectl rollout undo, which reverts to a previous ReplicaSet revision stored in the Deployment history. For debugging, use kubectl get, describe, logs, and exec to inspect pod status, events, and application behavior.
Summarize how each kubectl command interacts with specific control plane components, reinforcing the relationship between the architecture and operational tasks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing multi-tenancy as a layered security problem: isolation at the control plane, network, and data layers. Then walk through each layer—RBAC, network policies, secrets management, and encryption—explaining how they work together to prevent cross-tenant access. Emphasize that security is not just about tools but also about operational practices and continuous auditing.
Pro tip: Mention that true multi-tenancy often requires a combination of Kubernetes-native controls and external tools (e.g., service mesh, external secret stores), and that you must consider the trade-offs between isolation strength and operational complexity. Also, highlight the importance of regular security audits and penetration testing to validate your controls.
Clarify whether tenants share namespaces, clusters, or nodes, and identify the isolation requirements (e.g., compliance, data sensitivity). This determines the security controls you need.
Use Kubernetes RBAC to enforce least privilege, with roles and role bindings scoped to namespaces. Leverage admission controllers (e.g., OPA/Gatekeeper) to enforce policies like preventing privileged containers or hostPath mounts.
Apply default-deny network policies per namespace, then allow only necessary ingress/egress traffic. Use a CNI that supports network policies (e.g., Calico, Cilium) and consider service mesh for mTLS and L7 policies.
Avoid storing secrets in plaintext; use external secret stores (e.g., HashiCorp Vault, AWS Secrets Manager) with Kubernetes Secrets Store CSI driver. Enable encryption at rest for etcd and use TLS for all API communication.
Enable audit logging, monitor for anomalous activity, and regularly review RBAC permissions and network policies. Conduct penetration tests and stay updated on Kubernetes security best practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Long question and I think I spent too much time on the build and test stages and rushed the canary part at the end.
Start by clarifying requirements and constraints, then walk through the pipeline stages in order: build, test, scan, deploy. Emphasize how you achieve safe deployments with canary releases and rollback, and discuss trade-offs between speed, safety, and complexity.
Pro tip: Highlight that rollback should be automated based on health metrics, not manual, and mention using GitOps (e.g., Argo CD or Flux) to declaratively manage deployments and enable easy rollbacks.
Ask about scale, compliance needs, deployment frequency, and existing tooling. This shows you tailor solutions to context.
Describe using a CI tool (e.g., Jenkins, GitHub Actions) to build container images, run unit/integration tests, and push to a registry.
Explain scanning container images for vulnerabilities (e.g., Trivy, Clair) and enforcing policies before deployment.
Detail using a progressive delivery tool (e.g., Argo Rollouts, Flagger) to gradually shift traffic, monitor metrics, and automatically rollback on failure.
Cover trade-offs like speed vs. safety, and mention monitoring/alerting (e.g., Prometheus, Grafana) to validate deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.