Tüm alıştırma soruları

1598 soru

Soru 1141Soru

An application development team is establishing an automated local integration testing suite for a microservice that reads and writes documents using Cloud Datastore / Firestore in Native mode. To prevent test suites from interacting with live production or staging GCP resources and to avoid cloud usage costs, the team launches the local emulator within a CI pipeline container using the command `gcloud emulators firestore start --host-port=127.0.0.1:8080`. Which configuration step must be executed in the test execution container to ensure Google Cloud client SDKs automatically route all database requests to the local emulator without modifying application source code or requiring active GCP credentials?

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Cevap: Export the environment variable FIRESTORE_EMULATOR_HOST=127.0.0.1:8080 in the test execution container shell session before running test scripts.

Cevap

Export the environment variable FIRESTORE_EMULATOR_HOST=127.0.0.1:8080 in the test execution container shell session before running test scripts.
Exporting FIRESTORE_EMULATOR_HOST=127.0.0.1:8080 sets the standardized environment variable that Google Cloud SDK client libraries automatically detect. When populated, client libraries redirect calls to the local emulator address instead of live GCP endpoints and disable production authentication requirement.

Adım Adım Çözüm

1
Identify how Google Cloud client libraries discover local emulators.
Google Cloud client SDKs check standard environment variables (such as FIRESTORE_EMULATOR_HOST, PUBSUB_EMULATOR_HOST, or BIGTABLE_EMULATOR_HOST) upon initialization.
Client libraries are engineered to automatically bypass production authentication and TLS endpoints when host environment variables are set.
2
Configure the container execution environment.
Export FIRESTORE_EMULATOR_HOST matching the host and port defined during `gcloud emulators firestore start`.
This guarantees zero application source code changes while keeping testing completely localized and isolated from GCP.

Anahtar Kavram

Cloud Emulators and SDK Environment Variables
Tahmini Süre:2m 0s
Soru 1142Soru

A company runs an I/O-bound web service on a Compute Engine Managed Instance Group (MIG). During traffic spikes, request latency increases due to network socket exhaustion, while average CPU utilization on the instances remains below 30%. Which autoscaling configuration should you implement to scale the instances effectively?

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Cevap: Configure the MIG autoscaler using a custom Cloud Monitoring metric that measures request queue depth or active connections.

Cevap

Configure the MIG autoscaler using a custom Cloud Monitoring metric that measures request queue depth or active connections.
For I/O-bound applications, traditional CPU utilization metrics do not reflect true workload pressure. Exporting custom metrics such as request queue depth or connection counts to Cloud Monitoring enables the Managed Instance Group autoscaler to scale based on the actual resource bottleneck.

Adım Adım Çözüm

1
Identify the workload bottleneck.
The application is I/O-bound (network connections/queue depth), so CPU usage remains low during saturation.
Standard CPU metrics will not trigger scale-out events when CPU utilization stays below threshold.
2
Select an appropriate autoscaling signal.
Export application queue depth or active connection metrics to Cloud Monitoring and target those metrics in the MIG autoscaling policy.
Custom metrics accurately signal load for I/O-bound services and trigger timely scale-out events.

Anahtar Kavram

Custom metric autoscaling for I/O-bound workloads
Soru 1143Soru

A cloud architect at an enterprise logistics company is advising a development team on establishing secure deployment practices. The team's automated CI/CD pipeline deploys application workloads onto Compute Engine virtual machines that execute under a dedicated runtime application service account. To enable the CI/CD pipeline identity to attach this runtime service account to newly created virtual machine instances during deployment, developers plan to assign the CI/CD service account the Service Account Admin role. Which recommendation should the architect provide to adhere to Google Cloud security best practices and the principle of least privilege?

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Cevap: Grant the CI/CD pipeline service account the Service Account User role on the specific runtime application service account.

Cevap

Grant the CI/CD pipeline service account the Service Account User role (roles/iam.serviceAccountUser) on the specific runtime application service account.
The correct recommendation is to grant the Service Account User role (roles/iam.serviceAccountUser) to the CI/CD pipeline service account on the specific runtime application service account. This allows the pipeline identity to pass the runtime service account to Compute Engine instances without granting administrative privileges to alter service accounts or project IAM policies.

Adım Adım Çözüm

1
Analyze the operational permission requirement
The deployment pipeline identity only needs permission to attach (impersonate/use) an existing runtime service account when provisioning compute resources.
Creating or modifying service accounts is not required during standard instance provisioning.
2
Evaluate IAM roles according to least privilege
The Service Account User role (roles/iam.serviceAccountUser) grants the exact permission (iam.serviceAccounts.actAs) necessary to attach the service account to compute instances.
Administrative roles such as Service Account Admin or primitive roles grant overprivileged access.
3
Formulate the architect's recommendation
Advise granting roles/iam.serviceAccountUser scoped specifically to the target runtime service account resource.
Resource-level scoping enforces minimal operational exposure while supporting deployment automation.

Anahtar Kavram

Applying Least Privilege with Service Account User Roles in Deployment Pipelines
Tahmini Süre:1m 30s
Soru 1144Soru

A software engineer is developing a Python microservice running on Compute Engine instances that programmatically uploads reports to Cloud Storage and publishes events to Cloud Pub/Sub. Enterprise security policy strictly prohibits storing downloadable service account JSON keys on virtual machine disks. Additionally, the microservice must handle high-volume API requests without failing due to transient API quota limits. Which TWO design patterns should the developer implement to satisfy security policy and handle API interactions resiliently? (Select TWO)

Geçerli olan tümünü seçin

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Cevap: Configure the microservice SDK client to utilize Application Default Credentials (ADC) to retrieve identity tokens directly from the Compute Engine metadata server.; Implement exponential backoff retry algorithms with randomized jitter when receiving HTTP 429 rate limit responses from GCP APIs.

Cevap

The developer should configure the microservice SDK to use Application Default Credentials (ADC) fetching identity from the metadata server, and implement exponential backoff with randomized jitter for API retry handling.
The correct approach combines credential-less authentication via Application Default Credentials (ADC) leveraging the VM metadata server, and resilient client-side API error handling using exponential backoff with randomized jitter for rate limits.

Adım Adım Çözüm

1
Evaluate authentication requirements against security constraints.
Using ADC allows Google Cloud Client Libraries to implicitly retrieve short-lived OAuth 2.0 access tokens from the Compute Engine metadata server without requiring JSON service account key files.
This satisfies the requirement forbidding stored credentials on virtual machine disks.
2
Address high-volume API rate limiting and resilience requirements.
Implementing truncated exponential backoff combined with randomized jitter spreads retries over time when HTTP 429 (Too Many Requests) or transient errors occur.
This prevents retry storms and ensures resilient programmatic interaction with Google Cloud APIs.

Anahtar Kavram

Programmatic Authentication and Resilient API Design
Soru 1145Soru

An enterprise healthcare organization hosts a mission-critical API gateway on Google Cloud for processing patient records. The operations team needs to establish an automated incident management pipeline that alerts on-call engineers and creates incident tickets during genuine operational disruptions. The system must notify teams only when sustained error rates consume the monthly reliability budget, while ignoring brief 1-minute network glitches to prevent alert fatigue. Which strategy should the team implement in Google Cloud Monitoring?

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Cevap: Create an alerting policy using multi-window error budget burn-rate conditions tied to the service SLO, routed via Cloud Pub/Sub to trigger an automated ticket creation service.

Cevap

Create an alerting policy using multi-window error budget burn-rate conditions tied to the service SLO, routed via Cloud Pub/Sub to trigger an automated ticket creation service.
The correct approach uses multi-window error budget burn-rate alerting in Google Cloud Monitoring. Burn-rate alerts evaluate both short-term and long-term consumption rates of the Service Level Objective (SLO) error budget. Routing alerts through Cloud Pub/Sub ensures reliable, asynchronous execution of downstream ticket creation without exposing services to tight coupling or alert floods.

Adım Adım Çözüm

1
Identify the operational requirement for incident management.
Alerts must trigger on sustained errors consuming the error budget while suppressing transient spikes.
Prevents alert fatigue while ensuring critical reliability incidents are escalated prompt.
2
Select the appropriate alerting condition in Google Cloud Monitoring.
Use multi-window, multi-burn-rate alerting policies tied to Service Level Objectives (SLOs).
Burn-rate alerting calculates the percentage of error budget consumed over short and long lookback windows.
3
Configure the automated incident notification channel.
Publish alert events to a Cloud Pub/Sub topic connected to automated ticketing systems.
Decouples notification delivery from third-party APIs and allows reliable asynchronous processing of incident tickets.

Anahtar Kavram

Multi-window error budget burn-rate alerting for SRE incident management
Soru 1146Soru

A financial technology company operates an on-premises data processing engine that must securely ingest daily batch files into a Google Cloud Storage bucket in Project A. To enable access, a developer generated a service account JSON key file and deployed it directly to the on-premises servers. The lead security architect mandates that long-lived credentials must be completely eliminated while ensuring the on-premises application adheres strictly to the principle of least privilege. Which architectural solution should be implemented?

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Cevap: Set up Workload Identity Federation between the on-premises identity provider and Google Cloud, allowing the application to exchange short-lived tokens to impersonate a service account granted the Storage Object Creator role.

Cevap

Configure Workload Identity Federation with an on-premises identity provider to exchange short-lived tokens for service account impersonation using the Storage Object Creator role.
Workload Identity Federation enables external on-premises workloads to authenticate to Google Cloud APIs by exchanging external credentials for short-lived OAuth 2.0 access tokens. Granting the Storage Object Creator role satisfies the principle of least privilege by allowing file upload capabilities without full bucket management access.

Adım Adım Çözüm

1
Identify authentication mechanism requirement
Recognize that static downloadable service account keys must be replaced with keyless short-lived credential federation.
Security best practices recommend eliminating long-lived service account keys for external workloads.
2
Configure identity federation
Establish Workload Identity Federation between the on-premises identity provider (OIDC/SAML) and Google Cloud.
This allows the external server to authenticate without downloading key files.
3
Apply least privilege IAM permissions
Bind the federated identity to a Google Cloud service account with only the Storage Object Creator role.
The workload only requires permission to write daily batch files to Cloud Storage.

Anahtar Kavram

Workload Identity Federation for keyless authentication and service account least privilege
Tahmini Süre:1m 30s
Soru 1147Soru

A retail enterprise manages dozens of Google Cloud projects across multiple autonomous development teams. To implement FinOps governance, the central FinOps team needs to enforce cost allocation by ensuring all new resources are tagged with required environment and cost-center labels. Additionally, they want to establish proactive threshold alerts when project expenditures reach 80% and 100% of budgeted limits without abruptly interrupting running production workloads. Which architectural approach best meets these cost governance requirements?

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Cevap: Enforce mandatory resource labeling using Organization Policies with tag/label constraints, and configure Cloud Billing budgets with threshold rules at 80% and 100% sending email and Pub/Sub notifications.

Cevap

Enforce mandatory resource labeling using Organization Policies with tag/label constraints, and configure Cloud Billing budgets with threshold rules at 80% and 100% sending email and Pub/Sub notifications.
Combining GCP Organization Policies for tag enforcement with Cloud Billing budget threshold alerts provides automated governance and cost visibility. Organization policies natively block non-compliant provisioning, while Cloud Billing budgets inform teams at 80% and 100% thresholds without risking unexpected production service outages.

Adım Adım Çözüm

1
Identify the mechanism for enforcing resource labeling governance at scale across an organization.
Organization Policies with required tags/labels prevent non-compliant resource provisioning automatically at the API level.
Preventing non-compliant resource creation natively is more secure and efficient than manual auditing or reactive cleanup scripts.
2
Select the appropriate mechanism for budget monitoring and alerting.
Cloud Billing budget alerts configured at specific percentages (80% and 100%) send proactive alerts to stakeholders via Email or Pub/Sub topics.
Budget notifications alert teams before cost overruns occur while keeping production infrastructure operational.

Anahtar Kavram

FinOps Governance: Resource Labeling Enforcement and Billing Budget Alerts
Tahmini Süre:1m 30s
Soru 1148Soru

A global financial service platform hosts a mission-critical payment processing engine spanning Google Cloud `us-central1` (primary) and `us-east4` (secondary). The architecture connects to an on-premises mainframe transferring up to 15 Gbps of state data. Business requirements mandate a Recovery Point Objective (RPO) of under 1 minute and a Recovery Time Objective (RTO) of under 15 minutes during a total regional failover. Compute workloads run on Compute Engine Managed Instance Groups (MIGs), and relational data uses Cloud SQL for PostgreSQL with a cross-region read replica in `us-east4`. During a catastrophic regional outage in `us-central1`, which disaster recovery failover procedure should the cloud architecture team execute to ensure operational continuity within compliance bounds?

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Cevap: Promote the cross-region Cloud SQL read replica in `us-east4` to standalone primary, redirect on-premises hybrid traffic through pre-provisioned Dedicated Interconnect VLAN attachments in `us-east4`, and scale the secondary region MIGs to handle production load via Cloud Load Balancing.

Cevap

Promote the cross-region Cloud SQL read replica in us-east4 to standalone primary, redirect on-premises hybrid traffic through pre-provisioned Dedicated Interconnect VLAN attachments in us-east4, and scale the secondary region MIGs to handle production load via Cloud Load Balancing.
Executing failover by promoting a cross-region read replica satisfies sub-minute RPO, pre-provisioned Dedicated Interconnect VLAN attachments in the secondary region sustain 15 Gbps hybrid traffic without tunnel throughput limits, and warm-standby MIG scaling ensures recovery well within the 15-minute RTO envelope.

Adım Adım Çözüm

1
Analyze RPO, RTO, and bandwidth requirements.
RPO < 1 min, RTO < 15 min, Bandwidth = 15 Gbps between GCP and on-premises.
Bandwidth demands above 10 Gbps mandate Dedicated Interconnect rather than HA VPN. Sub-minute RPO requires asynchronous database replication rather than cold backup/snapshot restoration.
2
Evaluate network routing constraints.
Transitive routing over VPC Peering is unsupported.
VPC Network Peering cannot pass traffic transitively to an on-premises interconnect located in another peered VPC.
3
Select the compliant failover procedure.
Promote cross-region replica, leverage secondary pre-provisioned Dedicated Interconnect, and scale secondary MIGs.
Meets all operational SLAs, hybrid throughput limits, and GCP network architecture rules.

Anahtar Kavram

Disaster Recovery Execution and Hybrid Network Routing under Strict RPO/RTO
Soru 1149Soru

A global fintech payment processing company is modernizing its legacy core transaction ledger by migrating workloads to Google Cloud. Executive leadership requires zero unplanned downtime and strict compliance, while the legacy operations team expresses concern over a lack of familiarity with cloud-native tooling and governance. During initial staging simulations, large-scale load tests failed because required compute quotas were not requested in advance, and engineering leads requested broad project Owner permissions to troubleshoot failures rapidly. As the Principal Cloud Architect leading organizational change and technical strategy, which approach best addresses stakeholder requirements while ensuring smooth operational transition?

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Cevap: Establish a Cloud Center of Excellence (CCoE) to upskill team members with fine-grained predefined IAM roles, and perform proactive regional quota planning with GCP Support prior to migration milestones.

Cevap

Establish a Cloud Center of Excellence (CCoE) to upskill team members with fine-grained predefined IAM roles, and perform proactive regional quota planning with GCP Support prior to migration milestones.
Establishing a Cloud Center of Excellence (CCoE) directly addresses stakeholder resistance by providing structured upskilling and clear governance. Coupling this with least-privilege predefined IAM roles satisfies compliance needs, while conducting proactive regional quota planning ensures technical capacity is available when scaling during migration.

Adım Adım Çözüm

1
Assess technical capacity requirements and request GCP quotas
Ensure regional Compute Engine and networking quotas are increased prior to migration phases to prevent service failure during scaling.
Quota increases require lead time and approval from Google Cloud Support.
2
Address organizational change management and skill enablement
Form a Cloud Center of Excellence (CCoE) to provide training, governance templates, and structured migration support.
Empowers legacy operations teams to build confidence with cloud-native tooling and reduces friction.
3
Apply least-privilege IAM security controls
Assign predefined or fine-grained custom roles tailored to specific operational duties rather than broad primitive roles.
Protects sensitive financial environments while giving operations teams the exact permissions required to perform their duties.

Anahtar Kavram

Organizational Change Management & Quota Planning in Enterprise GCP Migrations
Soru 1150Soru

An enterprise architecture team is designing security controls for a financial reporting application deployed on a Google Kubernetes Engine (GKE) cluster in project-app. The application needs to securely retrieve sensitive third-party API credentials stored in GCP Secret Manager within a central governance project named project-sec. The architecture requirements mandate eliminating long-lived service account JSON keys and enforcing strict principle of least privilege. Which TWO actions should the security team implement to fulfill these security requirements?

Geçerli olan tümünü seçin

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Cevap: Configure GKE Workload Identity by mapping the Kubernetes ServiceAccount in the cluster namespace to a dedicated Google Cloud Service Account in project-app.; Grant the Secret Manager Secret Accessor role (roles/secretmanager.secretAccessor) on the specific secret resource in project-sec to the dedicated Google Cloud Service Account.

Cevap

To securely access secrets across GCP projects without using long-lived keys, the team must configure GKE Workload Identity to bind the Kubernetes service account to a dedicated Google Cloud service account, and grant that Google Cloud service account the Secret Manager Secret Accessor role on the specific secret resource.
Combining GKE Workload Identity with resource-level secret IAM bindings provides a completely keyless architecture that adheres to Google-recommended security standards. Workload Identity bridges Kubernetes service accounts to GCP service accounts automatically, and granting the Secret Manager Secret Accessor role directly on the secret resource ensures the workload cannot inspect or modify other secrets in the security project.

Adım Adım Çözüm

1
Establish keyless identity authentication for the GKE workload.
GKE Workload Identity securely binds the Kubernetes ServiceAccount to a Google Cloud Service Account without managing or storing long-lived JSON keys.
Eliminates secret leakage vectors associated with static credentials.
2
Configure fine-grained access permissions across projects.
The dedicated Google Cloud Service Account in project-app is granted the roles/secretmanager.secretAccessor role on the specific secret in project-sec.
Enforces least privilege access restricted to the exact secret needed.

Anahtar Kavram

GKE Workload Identity and Least Privilege Secret Access
Soru 1151Soru

An e-commerce platform processes checkout transactions through a payment gateway microservice hosted on Google Cloud. During brief, transient database latency spikes, the Site Reliability Engineering (SRE) team receives excessive false-positive incident alerts, while genuine sustained error budget depletion is occasionally detected too late. The team needs to redesign their Cloud Monitoring automated alerting strategy to reliably detect severe outages quickly while preventing alert fatigue from transient spikes. Which architecture approach should be implemented?

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Cevap: Configure Cloud Monitoring alerting policies using multi-window, multi-threshold burn rate monitoring based on the service SLO.

Cevap

Configure Cloud Monitoring alerting policies using multi-window, multi-threshold burn rate monitoring based on the service SLO.
Configuring multi-window, multi-threshold burn rate alerting in Cloud Monitoring is the Google Cloud recommended best practice for SRE incident management. It evaluates error budget consumption across multiple time windows (such as a 5-minute window for high burn rates and a 1-hour window for sustained lower burn rates), ensuring rapid notification for true incidents while filtering out noise from brief, transient downstream spikes.

Adım Adım Çözüm

1
Analyze alerting requirements and operational goals
Identified the need to balance fast detection of catastrophic outages with prevention of false positives from short, transient spikes.
Traditional static threshold alerting suffers from high noise or delayed response depending on how the threshold is set.
2
Evaluate Google Cloud SRE alerting best practices
Selected multi-window, multi-threshold burn rate alerting on Service Level Indicators (SLIs) and Service Level Objectives (SLOs).
Multi-window burn rate alerts trigger when error budget consumption rates exceed defined ratios over both brief (e.g., 5-minute) and sustained (e.g., 1-hour) time frames.
3
Validate security and architecture constraints
Confirmed that multi-window burn rate alerting integrates natively with Cloud Monitoring without exposing backend databases to health check cascades or requiring excessive IAM privileges.
Avoids anti-patterns such as deep dependency health check probes and primitive IAM role assignments.

Anahtar Kavram

Multi-window multi-threshold SLO burn rate alerting in Incident Management
Soru 1152Soru

A DevOps engineer needs to configure a local development workstation to run Python scripts that interact programmatically with Cloud Storage APIs using short-lived credentials via Service Account Impersonation. Corporate security policy strictly prohibits downloading JSON service account keys to local workstations. Place the operational steps in the correct chronological order to configure Application Default Credentials (ADC) with service account impersonation.

Öğeleri doğru sıraya koymak için sürükleyin

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Cevap

The correct sequence for configuring service account impersonation for Application Default Credentials (ADC) is: 1) Authenticate user identity via `gcloud auth login`, 2) Grant the Service Account Token Creator role to the user identity on the target service account, 3) Generate impersonated ADC using `gcloud auth application-default login --impersonate-service-account=...`, and 4) Run the code using standard Google Cloud Client Libraries.
The sequence follows the standard IAM lifecycle for keyless developer access: first establish user identity, grant necessary IAM permissions (`roles/iam.serviceAccountTokenCreator`), configure local ADC for impersonation via the gcloud CLI flag, and finally launch the application code which seamlessly consumes ADC.

Adım Adım Çözüm

1
Authenticate user identity
The local environment obtains OAuth tokens for the individual user account.
Before requesting credentials on behalf of another entity, the operator's primary user identity must be authenticated with GCP.
2
Assign IAM impersonation permission
User account gains `roles/iam.serviceAccountTokenCreator` on the target service account resource.
IAM authorization is required to create short-lived OAuth tokens or signed JWTs for a target service account.
3
Generate impersonated ADC local config
Application Default Credentials file `application_default_credentials.json` is updated with impersonation parameters.
Using the `--impersonate-service-account` flag configures ADC to fetch short-lived tokens automatically instead of using static key files.
4
Execute application code with Cloud SDK/Client Libraries
Client libraries load ADC credentials and send API requests signed by the impersonated service account.
Google Cloud Client Libraries default to searching standard ADC paths, enabling secure keyless programmatic access.

Anahtar Kavram

Service Account Impersonation with Application Default Credentials (ADC)
Tahmini Süre:1m 30s
Soru 1153Soru

A digital media streaming platform relies on a stateless recommendations microservice hosted on Google Cloud Run, backed by a Cloud SQL for PostgreSQL database. The engineering team needs to roll out a major feature release that includes a breaking database schema change (renaming several existing database columns). The deployment must achieve zero downtime and allow for an immediate rollback if errors occur during release cutover. How should the cloud architect design the deployment and database migration pipeline?

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Cevap: Apply an expand-contract database migration strategy by first introducing backward-compatible schema changes, deploy the new Cloud Run revision alongside the existing revision, gradually split traffic to the new revision, and execute the contract phase to clean up obsolete columns only after the release is fully verified.

Cevap

Execute an expand-contract database migration strategy to maintain backward compatibility, allowing old and new Cloud Run revisions to serve traffic concurrently during traffic splitting before finalizing the schema cleanup.
The correct strategy uses the expand-contract (parallel change) database design pattern alongside Cloud Run revision traffic management. By ensuring the database schema remains backward-compatible during the transition, both the existing revision and the newly deployed revision can operate concurrently without errors. Once traffic is fully shifted and confirmed healthy, the old schema components can be safely retired.

Adım Adım Çözüm

1
Implement the Expand Phase for Database Schema
Database schema is updated to support both legacy and new application requirements simultaneously (e.g., adding new columns alongside old ones or creating views).
This guarantees that the currently active Cloud Run revision continues operating without SQL errors while preparing for the new revision.
2
Deploy New Cloud Run Revision and Traffic Split
Deploy the updated application revision to Cloud Run with 0% traffic initially, then gradually shift traffic (e.g., 10%, 50%, 100%) while monitoring key metrics and error budgets.
Cloud Run native revision traffic splitting allows real-time canary monitoring and instant rollback if issues arise.
3
Execute the Contract Phase for Database Schema
Once the new revision serves 100% of traffic stably, remove obsolete database columns and legacy schema elements.
Completing the contract phase cleans up database technical debt after verifying that rollback to the legacy application revision is no longer required.

Anahtar Kavram

Decoupled Blue-Green/Canary Deployments with Expand-Contract Database Schema Migration
Tahmini Süre:2m 0s
Soru 1154Soru

A healthcare telemetry platform hosted on Google Cloud Run and Cloud Bigtable processes real-time patient metrics. The Site Reliability Engineering (SRE) team defines an availability Service Level Objective (SLO) of 99.9%99.9\% measured over a rolling 30-day window (43,20043,200 minutes). During an unexpected deployment failure, the service suffered complete downtime for 1818 minutes. Later in the same rolling window, a database connection pool exhaustion caused a partial degradation for 6060 minutes, during which 40%40\% of all incoming telemetry requests failed. Assuming a constant request rate throughout the window, how many minutes of error budget remain for this 30-day period?

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Cevap: 1.2

Cevap

The remaining error budget for the rolling 30-day window is 1.21.2 minutes.
For a 30-day window (43,20043,200 minutes) with a 99.9%99.9\% SLO target, the total allowable error budget is 0.1%×43,200=43.20.1\% \times 43,200 = 43.2 minutes. The complete outage consumed 1818 minutes. The partial degradation consumed 60×0.40=2460 \times 0.40 = 24 minutes of equivalent downtime budget. Total consumed error budget is 18+24=4218 + 24 = 42 minutes. Therefore, the remaining error budget is 43.242=1.243.2 - 42 = 1.2 minutes.

Adım Adım Çözüm

1
Calculate the total allowable error budget in minutes for the 30-day window
Total error budget = 43,200 minutes×(10.999)=43.2 minutes43,200 \text{ minutes} \times (1 - 0.999) = 43.2 \text{ minutes}.
An SLO of 99.9%99.9\% allows for an unreliability margin (error budget) of 0.1%0.1\% (0.0010.001) over the 43,20043,200-minute window.
2
Determine error budget consumed during the full outage
Full outage budget consumed = 18 minutes×100%=18.0 minutes18 \text{ minutes} \times 100\% = 18.0 \text{ minutes}.
During complete downtime (100%100\% request failure rate), every minute counts fully against the error budget.
3
Determine error budget consumed during the partial degradation
Partial outage budget consumed = 60 minutes×40%=24.0 minutes60 \text{ minutes} \times 40\% = 24.0 \text{ minutes}.
Under constant traffic conditions, partial failures consume error budget proportionally to the error rate (60×0.40=24.060 \times 0.40 = 24.0 minutes).
4
Calculate total consumed error budget and remaining budget
Total consumed = 18.0+24.0=42.0 minutes18.0 + 24.0 = 42.0 \text{ minutes}. Remaining error budget = 43.242.0=1.2 minutes43.2 - 42.0 = 1.2 \text{ minutes}.
Subtracting the total consumed downtime equivalent (42.042.0 minutes) from the allowable budget (43.243.2 minutes) yields the remaining error budget.

Anahtar Kavram

Error Budget Calculation for Service Level Objectives
Tahmini Süre:2m 0s
Soru 1155Soru

A multinational e-commerce company manages its core infrastructure across multiple Google Cloud projects using Terraform executed through an automated CI/CD pipeline. Following a recent operational incident, an engineer manually updated Cloud Storage bucket IAM policies and Compute Engine instance metadata via the Google Cloud Console to restore connectivity. This manual intervention created configuration drift between the actual running resources and the version-controlled Terraform state. To enforce strict IaC governance, the lead cloud architect needs an automated strategy to continuously detect configuration drift, restrict manual resource modifications going forward, and safely re-align the environment with the canonical Terraform definitions. Which operational design best achieves this objective while adhering to Google Cloud recommended best practices?

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Cevap: Schedule recurring non-destructive pipeline jobs running `terraform plan -detailed-exitcode` to detect drift, enforce Organization Policies and IAM fine-grained role boundaries to block direct Console modifications, and reconcile verified drift by updating Terraform code or executing automated `terraform apply` pipelines.

Cevap

Schedule recurring non-destructive pipeline jobs running `terraform plan -detailed-exitcode` to detect drift, enforce Organization Policies and IAM fine-grained role boundaries to block direct Console modifications, and reconcile verified drift by updating Terraform code or executing automated `terraform apply` pipelines.
The correct option establishes automated drift detection using `terraform plan -detailed-exitcode` in a non-destructive continuous integration step while securing the environment using IAM least privilege and Organization Policies. Reconciling infrastructure via audited code updates preserves Terraform as the single source of truth and prevents unapproved manual modifications.

Adım Adım Çözüm

1
Establish Continuous Drift Detection
Configured automated CI/CD jobs executing `terraform plan -detailed-exitcode` to periodically evaluate deployed infrastructure against canonical state without modifying running resources.
Detecting drift early ensures that manual interventions or out-of-band updates are flagged automatically before causing deployment failures.
2
Enforce Administrative Console Restrictions
Applied restrictive IAM permissions (removing broad write roles) and Organization Policies to restrict engineers from modifying production resources directly via Console or gcloud.
Preventing manual modifications forces all operational changes through audited, version-controlled Infrastructure as Code pipelines.
3
Reconcile State and Infrastructure
Remediated detected drift by incorporating required manual configuration changes into HCL code templates or executing `terraform apply` to overwrite unapproved changes.
Ensures the Terraform configuration remains the single source of truth for all Google Cloud infrastructure components.

Anahtar Kavram

Automated Drift Detection and Infrastructure Governance
Soru 1156Soru

An organization runs an automated database maintenance script on a Compute Engine virtual machine located in project `prod-workloads`. The script needs to retrieve a database password stored in Google Cloud Secret Manager within project `prod-secrets`. Company security policies strictly prohibit the creation and management of downloadable service account JSON keys. Which access management configuration follows Google-recommended best practices to allow the workload to authenticate while enforcing least privilege?

Cevabı ve açıklamayı göster

Cevap: Attach a dedicated service account to the Compute Engine instance in `prod-workloads` and grant that service account the Secret Manager Secret Accessor role (`roles/secretmanager.secretAccessor`) on the specific secret resource in `prod-secrets`.

Cevap

Attach a dedicated service account to the Compute Engine instance in `prod-workloads` and grant that service account the Secret Manager Secret Accessor role (`roles/secretmanager.secretAccessor`) on the specific secret resource in `prod-secrets`.
Attaching a dedicated service account to the VM instance allows the workload to automatically authenticate using Application Default Credentials (ADC) without requiring static JSON service account keys. Granting `roles/secretmanager.secretAccessor` directly on the targeted secret resource in the secret project enforces granular cross-project access following the principle of least privilege.

Adım Adım Çözüm

1
Identify the authentication mechanism that avoids downloading static service account keys.
Using built-in service accounts attached to Compute Engine instances enables keyless authentication via metadata server and Application Default Credentials (ADC).
Security policy forbids downloadable service account keys.
2
Determine the minimal IAM role required to read secret values in Secret Manager.
The `roles/secretmanager.secretAccessor` role grants permission to read secret payloads without administrative rights over the secret metadata.
Least privilege principles dictate granting accessor roles rather than admin or editor roles.
3
Determine the resource scope for binding the IAM role across projects.
Bind the role directly to the specific secret resource in `prod-secrets` rather than applying project-wide broad permissions.
Resource-level IAM bindings prevent unnecessary exposure to other secrets stored in the same project.

Anahtar Kavram

Cross-Project Secret Manager Access using Service Account Metadata and Resource-Level IAM Roles
Soru 1157Soru

An online education platform processes high-resolution video transcoding jobs using batch worker instances in a Compute Engine Managed Instance Group (MIG). During scheduled nationwide exam periods, job submission rates surge dramatically, leading to queue delays. The operations team notices that the MIG autoscaler fails to launch additional VM instances beyond a specific threshold during peak demand due to resource allocation failures. Which strategy should the Cloud Architect implement to optimize capacity planning and ensure seamless workload scaling?

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Cevap: Submit a regional vCPU quota increase request in advance of scheduled high-demand periods and configure scheduled scaling rules on the Managed Instance Group.

Cevap

Submit a regional vCPU quota increase request in advance of scheduled high-demand periods and configure scheduled scaling rules on the Managed Instance Group.
Proactive capacity planning for predictable high-demand events requires requesting regional vCPU quota increases prior to the event. Combining this with scheduled scaling on the Compute Engine MIG ensures compute resources are pre-provisioned and available before workload queues begin to back up.

Adım Adım Çözüm

1
Identify the bottleneck limiting infrastructure scaling during peak events.
Resource allocation failures during peak surges indicate hitting GCP regional resource quotas (e.g., regional vCPU quota).
MIG autoscalers cannot provision VMs beyond the project's enforced GCP resource quotas regardless of target metric configurations.
2
Select a proactive capacity management mechanism for predictable traffic surges.
Request quota increases in advance and use MIG scheduled scaling.
Quota increases require lead time for approval and provisioning. Scheduled scaling ensures capacity is ready before traffic spikes arrive.

Anahtar Kavram

Capacity Planning and Regional Resource Quota Management
Soru 1158Soru

A health technology enterprise stores sensitive patient medical analytics inside Google Cloud Storage buckets and BigQuery datasets. Authorized data engineers access these services from an on-premises data center connected via Dedicated Interconnect. The enterprise security policy strictly dictates that even users with legitimate administrative IAM permissions must be prevented from exfiltrating data by copying it to external Google Cloud Storage buckets or unapproved Google Cloud projects outside the enterprise organization. Which architectural control should the Cloud Architect implement to meet this requirement?

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Cevap: Define a VPC Service Controls service perimeter enclosing the project containing BigQuery and Cloud Storage, and use access levels to permit access only from the corporate network IP ranges.

Cevap

Define a VPC Service Controls service perimeter enclosing the project containing BigQuery and Cloud Storage, and use access levels to permit access only from the corporate network IP ranges.
VPC Service Controls allow organizations to set up perimeter security around sensitive Google Cloud resources such as Cloud Storage and BigQuery. This restricts service access to authorized network contexts (via Access Context Manager access levels) and blocks data egress to resources outside the perimeter boundary, effectively preventing data exfiltration even by compromised or malicious authorized accounts.

Adım Adım Çözüm

1
Identify the core security requirement
The scenario demands preventing authorized users with valid IAM credentials from exfiltrating sensitive data to external Google Cloud resources.
IAM alone authorizes identity access to resources but does not enforce network boundaries against exfiltration across Google Cloud API boundaries.
2
Evaluate GCP perimeter security controls
VPC Service Controls form a logical perimeter around Google Cloud APIs and services (such as GCS and BigQuery) to prevent data exfiltration to non-approved projects.
VPC Service Controls inspect request contexts and prevent data movement across perimeter boundaries, even for users with elevated IAM roles.

Anahtar Kavram

VPC Service Controls Data Exfiltration Prevention
Tahmini Süre:1m 30s
Soru 1159Soru

An enterprise energy company is establishing an automated testing and release validation pipeline for a mission-critical IoT telemetry ingestion platform on Google Cloud. To ensure system stability, capacity readiness, and security compliance prior to production cutover, sequence the technical solution validation steps in the correct chronological order.

Öğeleri doğru sıraya koymak için sürükleyin

Cevabı ve açıklamayı göster

Cevap

The correct sequence for testing and validating the technical solution is: 1) Execute dry-run plan validations using IaC, 2) Audit regional GCP quotas and submit increase requests, 3) Provision the staging environment and execute synthetic load tests, and 4) Configure VPC Service Controls perimeters and IAM roles prior to cutover.
Validating technical cloud solutions requires a structured, multi-phase procedure. First, dry-run IaC checks ensure code correctness without modifying resources. Next, verifying and requesting regional quotas guarantees that cloud capacity is available. Once quota limits are secured, staging environments can be provisioned to run synthetic load and SLO validation tests. Finally, security perimeters and access controls are locked down immediately prior to production traffic cutover to safeguard data boundaries.

Adım Adım Çözüm

1
Perform static configuration and IaC plan validation.
Infrastructure templates and dependency graphs are validated without mutating environment state.
Catching configuration syntax errors early avoids invalid deployment attempts and state lock issues.
2
Verify and request regional quota capacity.
GCP quota limits for compute and network resources are confirmed and expanded.
GCP quota approval can require processing time; validating quotas before environment creation avoids mid-deployment quota exhaustion.
3
Deploy staging infrastructure and run automated load/stress testing.
System performance, auto-scaling capabilities, and error rates are evaluated against SLO targets.
Synthetic load validation requires a fully provisioned staging setup with guaranteed quota allocations.
4
Enforce security perimeters and access control policies.
VPC Service Controls boundaries and IAM policies are locked down.
Final security controls should be validated and activated right before live traffic cutover to restrict unauthorized data access.

Anahtar Kavram

Developing Procedures to Test and Validate Technical Solutions
Tahmini Süre:2m 0s
Soru 1160Soru

A retail enterprise operates a high-throughput checkout fraud scoring service deployed on Google Cloud Run backed by Cloud Firestore. The service has a defined availability Service Level Objective (SLO) of 99.9%99.9\% successful HTTP requests over a rolling 3030-day period. During flash sales, brief traffic spikes trigger transient CPU utilization spikes and temporary latency fluctuations that quickly self-heal without breaching the monthly SLO, yet they trigger frequent urgent page alerts for the operations team. The Site Reliability Engineering (SRE) team needs to reduce alert fatigue while ensuring they are promptly notified before significant portions of the error budget are consumed by genuine outages. Which alerting architecture should the team implement?

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Cevap: Implement multi-window, multi-burn-rate alerts that trigger based on the rate of error budget consumption over short and long time windows.

Cevap

Implement multi-window, multi-burn-rate alerts that trigger based on the rate of error budget consumption over short and long time windows.
Implementing multi-window, multi-burn-rate alerting is the SRE standard recommendation on GCP. It measures the consumption speed of the error budget across multiple time horizons. High burn rates over short windows detect critical incidents fast, while moderate burn rates over larger windows catch subtle sustained errors, effectively filtering out transient spikes that do not threaten the rolling 3030-day SLO.

Adım Adım Çözüm

1
Analyze the operational problem.
The current static alerting strategy causes alert fatigue because transient spikes trigger pages even when the overall 3030-day 99.9%99.9\% SLO is not at risk.
Static threshold alerts evaluate raw metrics over brief periods without accounting for total allowable downtime (error budget).
2
Evaluate SRE best practices for alert design on Google Cloud.
Error budget burn-rate alerting measures how quickly an incident is consuming the remaining error budget.
Multi-window, multi-burn-rate alerting combines fast-burn short windows (for severe outages requiring quick intervention) and slow-burn longer windows (for persistent minor errors), eliminating false-positive pages caused by brief self-healing spikes.
3
Identify the correct option aligning with Google SRE practices.
Selecting burn-rate alerting based on error budget consumption.
It directly protects the SLO and prevents alert fatigue.

Anahtar Kavram

Multi-window, multi-burn-rate alerting for SLO error budget management
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Tüm alıştırma soruları — Google Cloud Professional Cloud Architect | Examkin