Google Certified DevOps Engineer Exam Dumps and Braindumps

Your team at LumenStream operates a latency sensitive API on Google Kubernetes Engine and you must keep performance steady. The workload reacts poorly to changes in available CPU and memory when the cluster runs automatic node pool upgrades. What should you implement to preserve the expected performance during these upgrade cycles?

• ❏ A. Enable “Vertical Pod Autoscaling” to automatically adjust CPU and memory requests for the pods based on observed usage

• ❏ B. Turn on “surge upgrades” for the GKE node pool so new nodes come online before existing nodes are drained

• ❏ C. Use “node affinity” rules so the pods schedule onto nodes with specific labels that indicate dedicated capacity

• ❏ D. Define a “PodDisruptionBudget” that limits the number of pods that can be evicted during maintenance

Which repository layout supports GitOps for Terraform infrastructure, Anthos Config Management, and application code across dev, test, and prod on GKE?

• ❏ A. One Terraform repo with environment branches, one ACM repo with per environment overlays, and one app monorepo with feature directories

• ❏ B. One Terraform repo with environment directories, one ACM repo with per environment overlays, and separate app repos with feature branches

• ❏ C. Separate Terraform and ACM repos per environment, and separate app repos that use only a single main branch

A regional fintech firm named SilverPine Payments has just finished moving its workloads to Google Cloud. They run roughly 180 Compute Engine virtual machines across development, testing, and production, using both managed instance groups and a few standalone servers. Over the last 90 days they observed that some services show a stable baseline while others experience sharp traffic bursts during business hours. They want to reduce spend by applying committed use discounts where it makes sense and by improving utilization. What strategy should you recommend?

• ❏ A. Move all workloads to Spot VMs and do not purchase any commitments

• ❏ B. Purchase three year committed use discounts for every machine type they have in inventory without reviewing utilization

• ❏ C. Enable autoscaling on all instance groups and buy commitments that cover the average usage across the entire fleet

• ❏ D. Analyze per service utilization and purchase commitments only for workloads with a high and predictable baseline while handling spikes with on demand instances and autoscaling

Which sequence best introduces a new API version and retires the old version with minimal disruption for external consumers?

• ❏ A. Introduce the new version, notify old-version users, announce deprecation later, then disable the old one

• ❏ B. Use Apigee to rewrite all requests to the new version immediately and delete the legacy proxies

• ❏ C. Launch the new API, announce deprecation of the legacy version, contact remaining consumers, provide best effort migration help, then retire the legacy endpoints

• ❏ D. Announce deprecation first, ship the new version, disable the old one, then contact remaining users

A digital media company named Blue Harbor Studios is migrating its workloads to Google Cloud. The team will run services on Compute Engine and Cloud Run. As the DevOps engineer you need to ensure that every category of audit logging produced by these services is captured in Cloud Logging. What should you do?

• ❏ A. Enable the Admin Activity audit logs for all services

• ❏ B. Enable the Data Access audit logs for the services that you will use

• ❏ C. Enable Access Transparency for the project

• ❏ D. Create a Log Router sink that exports all logs to BigQuery

How should you use Google Cloud Deploy to cut average lead time from 30 minutes to 10 and reduce repetitive manual work in the release process? (Choose 2)

• ❏ A. Use Cloud Build manual triggers for promotions

• ❏ B. Modularize Cloud Deploy pipelines with reusable templates

• ❏ C. Hire more engineers for manual checks

• ❏ D. Enable automated approvals in Cloud Deploy for stage promotions

• ❏ E. Create Cloud Monitoring alert to page on-call for manual promotions

Aurora Media has a three year committed use contract for Compute Engine vCPU and memory in Google Cloud, and halfway through the term the team plans to resize many virtual machines to different machine families to better match new workload patterns. How will the committed use discount apply for the rest of the term?

• ❏ A. The contract is terminated automatically and all usage is billed at the standard on demand price

• ❏ B. The discount continues only if the instances remain on the exact same machine type as before the resize

• ❏ C. The committed discount still applies to the resized instances as long as the aggregate vCPU and memory consumption stays within the committed amounts

• ❏ D. Sustained use discounts replace the committed discount after resizing and the committed discount resumes only when the original shapes are restored

A GKE service is unreachable in one region of a multi region deployment. Following SRE practices, what is the first action to restore user access?

• ❏ A. Cloud Logging

• ❏ B. Shift traffic to healthy regions with Cloud Load Balancing

• ❏ C. Update Cloud DNS to route to healthy regions

A national credit union operates in a tightly regulated industry and must keep every organizational log for eight years. You want to use managed services to simplify operations and you need to eliminate the risk of losing log ingestion or stored data because of configuration mistakes or human error. What should you implement?

• ❏ A. Create an aggregated sink at the organization scope in Cloud Logging that exports every log to a BigQuery dataset

• ❏ B. Configure a log export sink in every project that sends all logs to a BigQuery dataset

• ❏ C. Set up an aggregated sink at the organization scope in Cloud Logging that exports every log to Cloud Storage with an eight year retention policy and Bucket Lock enabled

• ❏ D. Configure a log export sink in every project that sends all logs to Cloud Storage with an eight year retention policy and Bucket Lock

Which Cloud Build approach reliably produces the deployable artifact and delivers it to a GKE cluster?

• ❏ A. Use Cloud Deploy to upload source files and release to GKE without a registry

• ❏ B. Trigger Cloud Build with cloudbuild.yaml to build an image and push to Artifact Registry, then GKE pulls it

• ❏ C. Use Cloud Build to compile to a native binary, upload to Cloud Storage, and deploy that file to GKE

• ❏ D. Run the Python interpreter to package the app and push files directly to GKE

All questions come from my GCP Certified Engineer Udemy course and certificationexams.pro

You work for BrightWave Labs which is preparing a production launch of a new API service on Google Cloud. The service must autoscale by using a Managed Instance Group and it must run in at least three different regions for high availability. Each VM instance requires substantial CPU and memory so you need to plan capacity carefully before rollout. What should you do?

• ❏ A. Enable predictive autoscaling on the Managed Instance Groups and configure a very high maximum number of instances

• ❏ B. Use Cloud Trace to evaluate performance data after initial deployment and adjust resource sizes from those findings

• ❏ C. Verify that your required vCPU memory and static IP quotas are available in each target region and submit quota increase requests as needed

• ❏ D. Deploy the service in a single region and place a global external HTTP load balancer in front of it

Which design routes users to the nearest Cloud Run region while presenting a single global address?

• ❏ A. Use Cloud DNS geo routing to return region specific VIPs that point at regional external HTTP(S) load balancers in front of Cloud Run

• ❏ B. Use Cloud CDN in front of a single regional Cloud Run service

• ❏ C. Run Cloud Run in multiple regions behind a global external HTTP(S) load balancer using serverless network endpoint groups and a single anycast IP

You are the DevOps lead at BrightTrail Apps, and the team follows a feature branch workflow for a microservices application running on Google Kubernetes Engine. You need to provision ephemeral environments for each branch that can be created and removed automatically while maintaining sensible resource limits and isolation and keeping costs low. Which approach should you implement in GKE?

• ❏ A. Create an individual GKE cluster for every feature branch and use VPC Service Controls for isolation

• ❏ B. Provision a separate GKE cluster for every feature branch and then create namespaces within each cluster to segregate resources

• ❏ C. Run one shared GKE cluster for all environments and create a dedicated Kubernetes namespace for each feature branch

• ❏ D. Use a single GKE cluster and rely on VPC Service Controls to isolate each feature branch environment at the network boundary

Which GCP configuration enables Data Access audit logs for Cloud Storage and BigQuery and exports them to a Cloud Storage bucket with 30 day retention?

• ❏ A. Create a sink to Pub/Sub and set 30 day retention in Pub/Sub

• ❏ B. Enable only Admin Activity logs and export to Cloud Storage

• ❏ C. Enable Data Access logs and a Log Router sink to Cloud Storage with a 30 day lifecycle delete rule

• ❏ D. Use Cloud Logging log buckets with 30 day retention and no export

At Apex Media Labs you serve as the Site Reliability Engineer responsible for data ingestion and analytics platforms on Google Cloud. Workloads can spike up to six times during live events and you must keep ingestion spend predictable. An internal platform team is creating a new managed ingestion service that will run on GCP. You want to shape its architecture with operational guidance that covers reliability targets, scalability and cost controls as early as possible. What should you do?

• ❏ A. After the first build passes QA and compliance checks, roll it out to a preproduction project and share logs and performance metrics with the engineers

• ❏ B. Spin up the early prototype in an isolated test project, run stress and spike tests, and send the results to the product team

• ❏ C. Meet with the product team during the design phase to review architecture and incorporate reliability, scalability and cost considerations

• ❏ D. Wait for the first production rollout and then use Cloud Operations suite dashboards to collect and share telemetry

Which approach enables automatic deployment when a new image is pushed to Artifact Registry while minimizing setup effort?

• ❏ A. Eventarc to Cloud Run job

• ❏ B. Cloud Pub/Sub to custom GKE controller

• ❏ C. Spinnaker pipeline via Cloud Pub/Sub on Artifact Registry push

• ❏ D. Cloud Deploy with Cloud Build trigger

Your security operations team at Cobalt Insurance must have read-only access to Data Access audit logs that are stored in the Cloud Logging bucket named “_Required”. You want to follow the principle of least privilege and Google’s recommended approach and you also want access to remain simple to administer as team membership changes. What should you do?

• ❏ A. Grant the roles/logging.viewer role to a Google Group that contains all members of the security team

• ❏ B. Grant the roles/logging.privateLogViewer role to each individual member of the security team

• ❏ C. Grant the roles/logging.privateLogViewer role to a Google Group that includes all members of the security team

• ❏ D. Create a log view on the “_Required” bucket that filters Data Access entries and grant roles/logging.viewAccessor to the security team group

How should you upgrade a GKE node pool for a stateless service while keeping the service available with no downtime?

• ❏ A. GKE release channel set to “Stable”

• ❏ B. Deployment RollingUpdate with maxUnavailable 0 and maxSurge 2

• ❏ C. Enable node pool surge upgrades with maxSurge 3 and maxUnavailable 0

• ❏ D. Enable cluster autoscaler

At Nimbus Outfitters you use Cloud Build to run continuous integration and delivery for a Flask service written in Python. The engineering manager asks you to prevent deployments that include third party libraries with known vulnerabilities and to keep the process automated in the pipeline. What is the most effective way to protect your Python dependencies?

• ❏ A. Configure pip to always install the latest available releases during the build

• ❏ B. Binary Authorization

• ❏ C. Add an automated vulnerability scan of Python dependencies in Cloud Build using a tool such as OSV Scanner or Safety and fail the build if issues are found

• ❏ D. Manually check each dependency against security advisories before approving a release

Should you upgrade API availability from 99.9% to 99.99% with an annual cost of $1,700 and annual revenue of $1,500,000 by comparing the value of reduced downtime to the cost?

• ❏ A. Estimate the value of the added availability at about $1,500 and decide that the spend is justified

• ❏ B. Estimate the value of the added availability at about $1,350 and decide that the spend is not justified

• ❏ C. Estimate the value of the added availability at about $1,350 and decide that the spend is justified

The correct option is Turn on “surge upgrades” for the GKE node pool so new nodes come online before existing nodes are drained.

This feature brings up replacement nodes first and only then drains the old nodes, which preserves overall cluster capacity during maintenance. By keeping resource headroom through the max surge and max unavailable settings, the scheduler can move pods without shrinking CPU or memory available to the workload. This prevents the sudden resource variability that hurts a latency sensitive API and keeps performance steady throughout the upgrade.

Enable “Vertical Pod Autoscaling” to automatically adjust CPU and memory requests for the pods based on observed usage focuses on right sizing pods and can trigger pod restarts when it updates requests. It does not address node draining during upgrades and it does not add capacity, so it does not prevent the resource fluctuations that cause performance issues.

Use “node affinity” rules so the pods schedule onto nodes with specific labels that indicate dedicated capacity only influences where pods run. It cannot stop node drains during upgrades or increase available resources, and it may reduce scheduling flexibility which can worsen disruption.

Define a “PodDisruptionBudget” that limits the number of pods that can be evicted during maintenance restricts how many replicas can be disrupted at once, but it does not maintain cluster capacity and can prolong upgrades if budgets are tight. It does not prevent performance dips that come from a temporary reduction in CPU or memory during node replacement.

The correct option is One Terraform repo with environment directories, one ACM repo with per environment overlays, and separate app repos with feature branches.

This layout supports GitOps because infrastructure changes for each environment live in clearly separated directories that are promoted by pull requests. It keeps state and variables isolated for dev, test, and prod while preserving a single source of truth and an auditable review trail. Teams can reuse modules consistently and automate promotion between environments with confidence.

Anthos Config Management works well with per environment overlays since Config Sync can fetch a central configuration repository where base policies and templates are inherited and environment overlays provide the differences for dev, test, and prod. This structure enables policy consistency, safer drift correction, and simple rollbacks.

Keeping applications in separate repositories with feature branches allows independent team workflows and targeted access control. Continuous integration builds versioned images per application and GitOps updates the environment overlays or manifests to roll forward through dev, test, and prod using pull requests.

One Terraform repo with environment branches, one ACM repo with per environment overlays, and one app monorepo with feature directories is not ideal because branches to model environments make promotion and drift management harder and reduce the clarity of change history. A single application monorepo with feature directories mixes unrelated services which complicates ownership, access control, and build isolation.

Separate Terraform and ACM repos per environment, and separate app repos that use only a single main branch leads to duplication of code and policy across environments which increases the chance of configuration drift and inconsistent governance. Requiring only a main branch for applications removes the feature branch workflow that enables safe reviews and incremental delivery.

The correct option is Analyze per service utilization and purchase commitments only for workloads with a high and predictable baseline while handling spikes with on demand instances and autoscaling.

This strategy aligns committed use discounts with the steady core of each service so the commitment is fully utilized and consistently applied. Committed use discounts deliver the best savings when the underlying usage is stable over time and you can validate that by reviewing per service metrics and billing data across the last 90 days. Handle bursty traffic with managed instance groups and autoscaling so the fleet expands only when needed and contracts when demand falls. Use on demand capacity for those variable portions so you avoid paying for unused committed capacity.

By separating predictable baseline from spikes you reduce the risk of overcommitting on machine types or regions and you preserve flexibility to rightsize and optimize instance shapes over time. This approach also works for both managed instance groups and the few standalone servers since you commit only where the utilization pattern is consistently high.

Move all workloads to Spot VMs and do not purchase any commitments is risky for production and other essential services because these instances can be preempted at any time and there is no availability guarantee. It also ignores clear savings from commitments for steady usage that must be available continuously.

Purchase three year committed use discounts for every machine type they have in inventory without reviewing utilization is unsafe because it can lock the company into underused commitments if workloads change or are right sized. Committing broadly by machine type without analysis often leads to waste when usage does not meet the commitment each hour.

Enable autoscaling on all instance groups and buy commitments that cover the average usage across the entire fleet mixes spiky and steady consumption which can cause portions of the commitment to go unused during low periods. Commitments should target the stable baseline of individual services rather than a fleetwide average that hides variability across environments and regions.

The correct option is Launch the new API, announce deprecation of the legacy version, contact remaining consumers, provide best effort migration help, then retire the legacy endpoints.

This sequence minimizes disruption because it introduces the new version first while keeping the legacy endpoints available, then clearly communicates deprecation with a timeline. It gives consumers overlap to test and migrate without breaking changes and it includes proactive outreach and support to help remaining users. Only after a reasonable migration period are the legacy endpoints retired, which aligns with standard API lifecycle and compatibility guidance.

Introduce the new version, notify old-version users, announce deprecation later, then disable the old one is weaker because delaying the deprecation announcement reduces the time consumers have to plan and schedule migration. Without an early and formal deprecation notice with a timeline, clients may be surprised by retirement dates and the risk of disruption increases.

Use Apigee to rewrite all requests to the new version immediately and delete the legacy proxies causes unexpected behavior changes and removes the safety of a side by side period. It breaks the contract for clients that rely on legacy semantics and it eliminates rollback paths by deleting the legacy proxies, which can lead to outages and loss of trust.

Announce deprecation first, ship the new version, disable the old one, then contact remaining users front loads deprecation without a viable alternative available, which creates uncertainty and can force clients into a holding pattern. Disabling the old version before reaching remaining users contradicts the goal of minimizing disruption and can break integrations.

The correct option is Enable the Data Access audit logs for the services that you will use.

Google Cloud produces several audit log categories. Admin Activity, System Event, and Policy Denied are written by default and are always captured in Cloud Logging. Data Access audit logs are not fully enabled by default for most services and they require explicit activation at the project, folder, or organization level for the specific services and log types. Since the goal is to ensure that every audit logging category produced by Compute Engine and Cloud Run is captured, you must enable Data Access for those services so that read and write access to user data is logged along with the default categories.

Enable the Admin Activity audit logs for all services is not correct because Admin Activity logs are already enabled by default and cannot be disabled. Turning them on again is not possible and it would still not capture the Data Access category that must be enabled separately.

Enable Access Transparency for the project is not correct because Access Transparency provides logs of Google personnel access to your content and it does not control or replace your project’s audit log categories for your own service activity.

Create a Log Router sink that exports all logs to BigQuery is not correct because a sink only exports logs that already exist. It does not enable any audit log category and it does not affect whether audit logs are captured in Cloud Logging in the first place.

The correct options are Enable automated approvals in Cloud Deploy for stage promotions and Modularize Cloud Deploy pipelines with reusable templates.

Automations in Cloud Deploy can approve and promote between stages based on predefined rules and conditions. This removes human wait time, enforces consistent gates, and preserves an audit trail. By eliminating handoffs and repetitive approval clicks, you reduce average lead time and cut toil while keeping control and visibility.

Reusable pipeline templates let you define a standard sequence of targets and policies once and apply it across many services. This reduces duplication and human error, speeds new pipeline creation, and allows centralized updates that immediately improve delivery speed across teams.

Use Cloud Build manual triggers for promotions is incorrect because it adds manual steps and does not leverage Cloud Deploy orchestration to automate stage promotions, which increases toil and does not reduce lead time.

Hire more engineers for manual checks is incorrect because adding people to manual reviews does not address the bottleneck and often increases coordination overhead, which works against the goal of reducing repetitive work and lead time.

Create Cloud Monitoring alert to page on-call for manual promotions is incorrect because paging humans introduces delay and context switching, which increases operational load and prolongs lead time rather than reducing it.

The correct option is The committed discount still applies to the resized instances as long as the aggregate vCPU and memory consumption stays within the committed amounts.

Compute Engine vCPU and memory commitments are resource based and are applied at the region level. The discount is tied to the total eligible vCPU and memory usage rather than to a specific instance shape or machine family. You can resize or switch machine families and the commitment continues to reduce costs as long as your aggregate covered usage stays within the committed amounts. If you use more than you committed then the excess is billed at on demand rates and if you use less you still pay for the full commitment.

The contract is terminated automatically and all usage is billed at the standard on demand price is incorrect because commitments do not auto cancel when you resize instances and they continue through the agreed term.

The discount continues only if the instances remain on the exact same machine type as before the resize is incorrect because resource based commitments are not tied to a single machine type and they apply across machine families and custom shapes within the region.

Sustained use discounts replace the committed discount after resizing and the committed discount resumes only when the original shapes are restored is incorrect because sustained use discounts do not override a commitment. The committed pricing applies first up to the committed amounts and sustained use may apply only to any remaining on demand usage.

The correct action is Shift traffic to healthy regions with Cloud Load Balancing.

Shift traffic to healthy regions with Cloud Load Balancing aligns with SRE guidance to mitigate user impact first. A global external HTTP(S) load balancer fronts your GKE services and continuously checks backend health. When one region fails, the load balancer automatically or manually steers traffic to healthy regional backends which restores user access quickly without waiting for configuration propagation.

Cloud Logging is valuable for diagnosis and post incident analysis, but reading or querying logs does not restore user access. It does not move traffic away from the failing region, so it is not the first step when you need to recover service availability.

Update Cloud DNS to route to healthy regions is slower and less predictable because DNS changes are subject to client and resolver caching and TTLs. Many users would continue to resolve the old records for some time. The global load balancer provides immediate failover without DNS updates, so changing DNS is not the first action to restore access.

The correct choice is Set up an aggregated sink at the organization scope in Cloud Logging that exports every log to Cloud Storage with an eight year retention policy and Bucket Lock enabled.

This approach captures logs from all existing and future projects because an organization level aggregated sink routes every log without requiring per project configuration. Exporting to Cloud Storage with an eight year retention policy meets the regulatory retention requirement. Enabling Bucket Lock enforces write once read many protections and prevents shortening retention or deleting objects which removes the risk of data loss due to human error or misconfiguration.

Create an aggregated sink at the organization scope in Cloud Logging that exports every log to a BigQuery dataset is not suitable for immutable long term retention because BigQuery does not provide write once read many controls like Bucket Lock. Users could still alter or delete data and this does not eliminate the risk of loss from mistakes.

Configure a log export sink in every project that sends all logs to a BigQuery dataset increases operational overhead and risks gaps when new projects are created or when a sink is misconfigured. It also uses BigQuery which lacks write once read many protections and therefore does not satisfy the requirement to eliminate the risk of loss from human error.

Configure a log export sink in every project that sends all logs to Cloud Storage with an eight year retention policy and Bucket Lock still relies on per project configuration which is error prone and can miss new or reorganized projects. An organization level aggregated sink is needed to ensure complete coverage and to simplify operations.

The correct option is Trigger Cloud Build with cloudbuild.yaml to build an image and push to Artifact Registry, then GKE pulls it.

This approach uses a declarative Cloud Build configuration to produce a container image and store it in Artifact Registry. A GKE Deployment references the image by its registry path so the nodes pull the image during rollout. This creates a reliable and repeatable supply chain because images are immutable and versioned and Kubernetes knows how to fetch them from a registry.

Use Cloud Deploy to upload source files and release to GKE without a registry is wrong because Cloud Deploy orchestrates releases but it expects a built artifact such as a container image stored in a registry. It does not accept raw source files as the deployable unit and it does not eliminate the need for a registry.

Use Cloud Build to compile to a native binary, upload to Cloud Storage, and deploy that file to GKE is wrong because GKE runs containers that reference images in a registry. Cloud Storage is not a container registry and Kubernetes cannot deploy an arbitrary binary from a bucket as a workload.

Run the Python interpreter to package the app and push files directly to GKE is wrong because GKE does not accept direct file pushes. You must build a container image and push it to a registry then reference that image in Kubernetes manifests.

The correct option is Verify that your required vCPU memory and static IP quotas are available in each target region and submit quota increase requests as needed.

Autoscaling Managed Instance Groups can only add instances when the project has enough quota for the requested machine types and related resources. Because the service must run in at least three regions you need to confirm capacity in each region before launch. Checking vCPU and custom memory quotas ensures the MIG can create the selected machine shapes at the desired scale. Reviewing static IP address quotas helps you reserve addresses for load balancing or other networking components. Some quotas are regional while others are global so you should verify both scopes and request increases ahead of production to prevent failed instance creation during spikes.

Enable predictive autoscaling on the Managed Instance Groups and configure a very high maximum number of instances is not sufficient because autoscaling cannot bypass quota limits. Predictive autoscaling can warm capacity but creation still fails when regional vCPU or address quotas are exhausted and a high maximum does not guarantee available resources.

Use Cloud Trace to evaluate performance data after initial deployment and adjust resource sizes from those findings focuses on post deployment analysis rather than pre launch capacity planning. You must validate and secure quotas before rollout to ensure instances can be created at the needed scale.

Deploy the service in a single region and place a global external HTTP load balancer in front of it does not meet the requirement to run in at least three regions. A single region backend cannot provide regional high availability even if fronted by a global load balancer.

The correct design is Run Cloud Run in multiple regions behind a global external HTTP(S) load balancer using serverless network endpoint groups and a single anycast IP. This meets the requirement for a single global address and directs users to the nearest healthy region.

This design uses a global external HTTP(S) load balancer with an anycast IP so users connect to the closest Google edge and are routed to the nearest regional Cloud Run backend. Serverless network endpoint groups let the load balancer target Cloud Run services in multiple regions, which enables proximity routing and automatic failover while still exposing one global address.

Use Cloud DNS geo routing to return region specific VIPs that point at regional external HTTP(S) load balancers in front of Cloud Run is not correct because it returns different regional VIPs rather than a single global address. DNS based steering is also affected by caching and does not provide the same fast failover and proximity routing that the global external HTTP(S) load balancer offers.

Use Cloud CDN in front of a single regional Cloud Run service is not correct because CDN does not select among multiple regional origins. Dynamic requests still go to one regional backend and this design does not provide nearest region routing. It also relies on a single region and therefore does not satisfy the requirement for routing users to the nearest Cloud Run region through one global address.

The correct option is Run one shared GKE cluster for all environments and create a dedicated Kubernetes namespace for each feature branch.

A shared cluster with per branch namespaces gives you fast and lightweight environment provisioning that is well suited to ephemeral feature branches. Namespaces are quick to create and delete and they integrate naturally with CI systems that can apply labels and manifests when a branch is opened and remove them when it is merged. You can apply ResourceQuota and LimitRange to each namespace to cap CPU, memory, and object counts so teams get isolation with sensible limits and predictable costs. NetworkPolicy and RBAC can restrict traffic and permissions between namespaces which improves isolation while keeping everything inside one manageable cluster. Cluster autoscaling and right sizing of requests further keep costs low because idle namespaces do not force extra clusters to stay running.

This approach aligns with GKE multi tenancy guidance because it uses namespace boundaries for soft isolation and policy controls for security and governance. You can also use per namespace service accounts with Workload Identity and separate Ingress hosts or paths for branch specific routing which keeps environments independent while sharing the same underlying infrastructure.

Create an individual GKE cluster for every feature branch and use VPC Service Controls for isolation is not appropriate because spinning up a full cluster per branch is slow and expensive. VPC Service Controls protect access to Google managed APIs and reduce data exfiltration risk but they do not isolate workloads inside your Kubernetes cluster and they do not solve per branch tenancy requirements.

Provision a separate GKE cluster for every feature branch and then create namespaces within each cluster to segregate resources adds unnecessary complexity and cost. You would pay for many clusters that duplicate control plane and node overhead and you still rely on namespaces for segregation which means the extra clusters provide little benefit for ephemeral environments.

Use a single GKE cluster and rely on VPC Service Controls to isolate each feature branch environment at the network boundary is ineffective because VPC Service Controls are designed to secure access to Google APIs and services rather than govern traffic and permissions between pods and namespaces in your cluster. Isolation for branch environments should be implemented with Kubernetes constructs such as namespaces, RBAC, ResourceQuota, and NetworkPolicy.

The correct option is Enable Data Access logs and a Log Router sink to Cloud Storage with a 30 day lifecycle delete rule.

Data Access audit logs capture read and write access to user data in services such as Cloud Storage and BigQuery. They are not enabled by default, so you must turn them on for those services. After enabling them, create a Log Router sink that routes the matching audit log entries to a Cloud Storage bucket. Apply a lifecycle delete rule on that bucket to remove objects after 30 days. This setup satisfies the requirement to export the logs to a Cloud Storage bucket and to maintain a 30 day retention period by automatically deleting older objects.

Create a sink to Pub/Sub and set 30 day retention in Pub/Sub is incorrect because Pub/Sub message retention does not store logs in a Cloud Storage bucket and is intended for short term message delivery rather than bucket based retention. It does not meet the requirement to export to a Cloud Storage bucket with 30 day retention.

Enable only Admin Activity logs and export to Cloud Storage is incorrect because Admin Activity logs do not include Data Access events. The question specifically requires Data Access audit logs for Cloud Storage and BigQuery.

Use Cloud Logging log buckets with 30 day retention and no export is incorrect because this keeps the logs in Cloud Logging rather than exporting them to a Cloud Storage bucket, which the question requires.

The correct option is Meet with the product team during the design phase to review architecture and incorporate reliability, scalability and cost considerations.

Engaging during design lets you define SLOs and SLIs, agree on error budgets and capacity plans, and choose scalable patterns that can handle six times traffic while keeping spend predictable. Early collaboration enables you to select autoscaling strategies, quotas, budgets and alerts, and load shedding approaches before code and infrastructure harden. It also allows you to integrate observability requirements into the architecture so that telemetry, tracing, and meaningful dashboards are in place from the start. This is aligned with architectural best practices that emphasize reliability and cost as first class design goals rather than afterthoughts.

After the first build passes QA and compliance checks, roll it out to a preproduction project and share logs and performance metrics with the engineers is too late to influence foundational decisions. You can observe behavior in preproduction, yet major choices that affect reliability, scalability, and unit economics have already been made which limits your ability to control costs and meet targets during live event spikes.

Spin up the early prototype in an isolated test project, run stress and spike tests, and send the results to the product team produces useful data but does not ensure architectural changes happen. Testing in isolation can miss production realities such as quotas, multi region design, and shared platform limits. Simply sending results is not the same as partnering during design to embed cost controls, SLOs, and scaling policies.

Wait for the first production rollout and then use Cloud Operations suite dashboards to collect and share telemetry defers reliability and cost decisions until the highest risk point. Observability is essential, yet waiting until production risks overspend and SLO violations during peak loads and it misses the chance to build the right controls into the system.

The correct option is Spinnaker pipeline via Cloud Pub/Sub on Artifact Registry push.

This choice aligns with how Artifact Registry emits notifications because it publishes events to Pub/Sub when images are pushed. Spinnaker can subscribe to that topic and use a Pub/Sub trigger to start a deployment pipeline automatically. You configure a topic and subscription and a pipeline trigger, then the pipeline performs the rollout, which keeps setup effort low and avoids custom glue code.

Eventarc to Cloud Run job is not ideal because it would require you to build and maintain a job that performs the deployment using scripts or commands. This adds more components and configuration and does not provide a native deployment pipeline experience.

Cloud Pub/Sub to custom GKE controller would work but it demands writing and operating a bespoke controller. That increases complexity and maintenance and therefore does not minimize effort.

Cloud Deploy with Cloud Build trigger can orchestrate releases well, yet triggering it directly from an Artifact Registry push typically requires wiring Pub/Sub to a Cloud Build trigger which then calls Cloud Deploy. That path adds more moving parts than the Spinnaker Pub/Sub trigger and therefore requires more setup.

The correct option is Grant the roles/logging.privateLogViewer role to a Google Group that includes all members of the security team.

Data Access audit logs are considered private logs in Cloud Logging and require the private log viewing permissions to read them. Assigning this role to a Google Group gives the team read-only access to the Data Access entries in the _Required bucket while following least privilege. Using a group also aligns with Google’s recommended practice because you manage membership in one place and the access updates automatically as people join or leave the team.

Grant the roles/logging.viewer role to a Google Group that contains all members of the security team is not sufficient because the logging.viewer role cannot read private logs such as Data Access audit logs.

Grant the roles/logging.privateLogViewer role to each individual member of the security team would work technically but it is not the recommended approach. Granting roles directly to individuals is harder to administer as membership changes and does not align with Google’s guidance to assign IAM roles to groups.

Create a log view on the _Required bucket that filters Data Access entries and grant roles/logging.viewAccessor to the security team group does not meet the requirement because a view can limit which logs are visible but the viewAccessor role alone cannot read private logs. You would still need the private log viewing permissions to access Data Access entries.

The correct option is Enable node pool surge upgrades with maxSurge 3 and maxUnavailable 0. This approach keeps the service available because it temporarily adds new nodes to provide extra capacity and only drains old nodes after the replacement capacity is ready, which prevents any reduction in available pods during the upgrade for a stateless workload.

With maxUnavailable set to zero the upgrade does not remove any existing nodes until new nodes have joined the cluster. With a surge of three nodes there is additional headroom to schedule replacement pods while older nodes are cordoned and drained. Stateless pods can be recreated on the new nodes without interruption to the service, so this method achieves an in place upgrade with no downtime.

GKE release channel set to “Stable” only selects the version track and cadence for cluster and node updates. It does not change how nodes are drained or replaced during an upgrade and it does not guarantee zero downtime for workloads.

Deployment RollingUpdate with maxUnavailable 0 and maxSurge 2 governs application rollout behavior for pods and not the behavior of node pool upgrades. Node upgrades still cordon and drain nodes, so this deployment strategy alone cannot ensure availability during the node replacement process.

Enable cluster autoscaler automatically scales node counts in response to pending pods, but it does not coordinate the sequencing of node upgrades. It cannot by itself prevent disruption when nodes are drained during an upgrade.

The correct option is Add an automated vulnerability scan of Python dependencies in Cloud Build using a tool such as OSV Scanner or Safety and fail the build if issues are found.

This approach integrates directly into Cloud Build so each run can invoke a dependency scanner that checks your requirements or lock files against known vulnerability databases. OSV Scanner uses the OSV database and Safety uses curated advisories for Python packages. Either tool can be run as a build step and configured to return a non zero exit code when vulnerabilities above your threshold are detected. That causes the build to fail which automatically blocks deployment and satisfies the need for both prevention and automation in the pipeline.

Configure pip to always install the latest available releases during the build does not ensure security because the newest version can still be vulnerable and it also reduces reproducibility which makes builds harder to audit and roll back.

Binary Authorization enforces deploy time policies for container images and can require attestations but it does not scan Python dependencies by itself. It is complementary to dependency scanning rather than a replacement for it.

Manually check each dependency against security advisories before approving a release is not automated and is error prone and it will slow delivery. It also fails to provide consistent enforcement across all builds.

The correct option is Estimate the value of the added availability at about $1,350 and decide that the spend is not justified.

Going from 99.9 percent availability to 99.99 percent reduces expected downtime from about 8.76 hours per year to about 0.876 hours per year. That saves roughly 7.884 hours of downtime. With annual revenue of 1,500,000 dollars spread evenly across the year the revenue rate is about 171 dollars per hour. Multiplying by the hours saved yields a benefit near 1,350 dollars. Since the upgrade costs 1,700 dollars the cost exceeds the estimated benefit and the spend is not justified.

Estimate the value of the added availability at about $1,500 and decide that the spend is justified is incorrect because it overestimates the value of the reduced downtime and it leads to the wrong decision to approve the spend.

Estimate the value of the added availability at about $1,350 and decide that the spend is justified is incorrect because the valuation is reasonable but the conclusion is wrong since the cost of 1,700 dollars is higher than the 1,350 dollar benefit.