Tüm alıştırma soruları

1598 soru

Soru 141Soru

An enterprise logistics company plans to store sensitive customer shipment records in Cloud Storage and analyze them using BigQuery. Security compliance mandates that the data must be protected at rest using Customer-Managed Encryption Keys (CMEK) managed via Cloud KMS. The security team requires strict adherence to least privilege, explicit separation of duties, and continuous compliance automation without giving service accounts administrative key control. Which TWO architectural design decisions and IAM configurations should you implement to meet these requirements?

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Cevap: Grant the Cloud KMS CryptoKey Encrypter/Decrypter role on the specific Cloud KMS key to the service agents for Cloud Storage and BigQuery.; Enable automated key rotation schedules on the Cloud KMS CryptoKey used by Cloud Storage and BigQuery.

Cevap

Grant the Cloud KMS CryptoKey Encrypter/Decrypter role specifically to the GCP service agents for Cloud Storage and BigQuery, and enable automated key rotation schedules on the Cloud KMS key.
Configuring CMEK via Cloud KMS with automated key rotation and granting the Cloud KMS CryptoKey Encrypter/Decrypter role to service agents fulfills all enterprise compliance requirements for key management, separation of duties, and least privilege access.

Adım Adım Çözüm

1
Identify key management requirements for Cloud Storage and BigQuery CMEK.
Cloud Storage and BigQuery both support CMEK integrated with Cloud KMS.
CMEK allows central management of encryption keys within Cloud KMS while delegating cryptographic operations to Google Cloud service agents.
2
Apply least privilege and separation of duties for Cloud KMS permissions.
Grant `roles/cloudkms.cryptoKeyEncrypterDecrypter` to the service agents.
Service agents require encryption/decryption permissions on the key, but should not have admin roles (`roles/cloudkms.admin`) or primitive permissions (`roles/owner`).
3
Configure key rotation according to compliance policy.
Enable automated key rotation in Cloud KMS.
Cloud KMS handles key rotation automatically, generating new key versions while keeping older versions active for decryption of existing data blocks.

Anahtar Kavram

Customer-Managed Encryption Keys (CMEK) and IAM Separation of Duties in Cloud KMS
Soru 142Soru

A cloud architecture team is refactoring an event-driven data pipeline that processes real-time transaction events via Cloud Pub/Sub and persists aggregated records into Cloud Bigtable. To cut cloud costs and accelerate CI/CD build execution, the team must run unit and integration tests completely offline without interacting with real Google Cloud API endpoints. Which TWO configuration steps must be implemented to ensure the client SDKs route all network requests to the locally running emulators? (Select TWO.)

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Cevap: Export the environment variable PUBSUB_EMULATOR_HOST set to the host and port of the running Pub/Sub emulator before initializing the application client.; Export the environment variable BIGTABLE_EMULATOR_HOST pointing to the socket of the local Bigtable emulator prior to invoking the test runner.

Cevap

To route client SDK requests to local emulators offline, developers must export the PUBSUB_EMULATOR_HOST environment variable for Cloud Pub/Sub and the BIGTABLE_EMULATOR_HOST environment variable for Cloud Bigtable.
Google Cloud client SDKs for Cloud Pub/Sub and Cloud Bigtable automatically detect the PUBSUB_EMULATOR_HOST and BIGTABLE_EMULATOR_HOST environment variables. When set, client libraries automatically redirect API calls to local emulator sockets and disable network calls to production Google Cloud endpoints.

Adım Adım Çözüm

1
Identify the standard host environment variables defined by Google Cloud Client Libraries for service emulators.
PUBSUB_EMULATOR_HOST and BIGTABLE_EMULATOR_HOST are recognized automatically by GCP client libraries.
When these environment variables are populated with host:port values, client SDKs override default Google service endpoints and direct traffic to local emulator sockets.
2
Evaluate local emulator execution requirements against live GCP infrastructure provisioning.
Local environment variables allow completely offline, cost-free, high-speed test execution.
Emulators do not require active Cloud IAM roles, live service account credentials, or provisioned GKE compute clusters.

Anahtar Kavram

Google Cloud Client Library emulator auto-discovery via host environment variables
Soru 143Soru

A healthcare enterprise is designing a secure CI/CD pipeline on Google Cloud using Cloud Build to automate infrastructure changes via Terraform and deploy containerized services. The security architect specifies that the build process must prevent privilege escalation, avoid local configuration state loss, and restrict network traffic to internal resources. Which TWO architectural practices should the team implement to satisfy these security and reliability requirements? (Select TWO.)

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Cevap: Execute pipeline builds using Cloud Build private pools peered with a VPC network to prevent public internet access and enforce network perimeter controls.; Configure a Cloud Storage remote backend with Object Versioning for Terraform state and require Cloud Build to use service account impersonation with fine-grained roles.

Cevap

The team should execute builds using Cloud Build private pools peered to a VPC network and store Terraform state in a Cloud Storage remote backend with Object Versioning while enforcing service account impersonation.
Executing pipeline builds in Cloud Build private pools connected via VPC Network Peering guarantees private connectivity and strict perimeter security controls. Additionally, using Cloud Storage as a remote Terraform backend with versioning ensures durable, concurrent state tracking, while leveraging service account impersonation guarantees least-privilege security.

Adım Adım Çözüm

1
Evaluate network isolation requirements for build execution.
Using Cloud Build private pools connects build workers privately to internal VPC networks and prevents exfiltration over public endpoints.
Default Cloud Build runners operate in a shared public environment, which does not satisfy strict network perimeter requirements.
2
Evaluate infrastructure state management and IAM access strategy.
Centralizing state in Cloud Storage with object versioning ensures durable state tracking and disaster recovery, while service account impersonation enforces strict privilege boundaries.
Ephemeral local storage leads to lost state and corruption, and granting primitive roles grants excessive permissions.

Anahtar Kavram

Designing secure and resilient CI/CD pipelines with Cloud Build private pools, remote Terraform state locking, and least-privilege IAM impersonation.
Soru 144Soru

A software development team is building a microservices-based event processing application on Google Cloud that relies heavily on Cloud Pub/Sub and Cloud Datastore. The team leads report high Google Cloud charges during early development iterations and security risks due to developers creating service account keys on their local workstations to authenticate against live Cloud resources during local testing. As a Cloud Architect advising the development and operations teams, which TWO actions should you recommend to establish a secure, cost-effective local development and integration testing workflow?

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Cevap: Advise developers to run Cloud Pub/Sub and Datastore local emulators on their workstations for local unit and integration testing.; Configure local environment variables to point application client libraries to the local emulator endpoints instead of live Google Cloud API endpoints.

Cevap

The development and operations teams should be advised to run Cloud Pub/Sub and Cloud Datastore local emulators on developer workstations and configure local environment variables to direct client libraries to the emulator endpoints.
Recommending Google Cloud local emulators allows developers to perform fast, isolated integration testing locally on their workstations without incurring live cloud consumption charges or requiring service account keys. Redirecting Google Cloud client libraries via environment variables ensures that application code remains environment-agnostic without requiring code changes.

Adım Adım Çözüm

1
Identify the causes of cloud costs and security vulnerabilities during local development
Connecting local dev environments to live cloud services generates resource consumption costs and requires managing long-lived IAM service account keys.
Direct cloud resource connection from local machines introduces security risks and unnecessary expenses.
2
Evaluate GCP emulator capability for local development workflows
Google Cloud provides local emulators (e.g., gcloud emulators pubsub, datastore) that simulate GCP service behaviors locally without network calls to GCP endpoints.
Emulators eliminate GCP resource usage costs and eliminate the need for GCP authentication credentials during local testing.
3
Configure environment variables for client library redirection
Setting environment variables (such as PUBSUB_EMULATOR_HOST) automatically redirects Google Cloud SDK client calls to local localhost ports.
Client libraries natively support emulator redirection variables, keeping application code clean and identical between dev and production environments.

Anahtar Kavram

Advising Development and Operation Teams on Local Emulators and Environment Workflows
Soru 145Soru

A digital media publishing company receives irregular batches of user-generated promotional video uploads. The raw video files must be processed and converted into multiple streaming formats. The processing workloads are stateless, highly parallelizable, and tolerant of temporary interruptions or delayed retries. The company wants to minimize compute costs for this batch processing pipeline while ensuring that raw uploads are automatically transitioned to lower-cost storage after 30 days. Which two architecture decisions should you recommend to satisfy these business requirements? (Select TWO.)

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Cevap: Execute the video transcoding jobs using Cloud Run jobs configured to leverage Spot VM capacity pricing (or Spot placement where available) / preemptible compute execution.; Configure Cloud Storage Object Lifecycle Management rules on the raw video bucket to transition objects from Standard to Archive storage after 30 days.

Cevap

To optimize costs for fault-tolerant, unpredictable video processing and storage, the organization should execute containerized batch jobs using Spot compute capacity and configure Cloud Storage Object Lifecycle Management rules to automatically transition older raw files to low-cost archival storage classes after 30 days.
The combination of using Spot-backed serverless batch job execution and automated Cloud Storage Object Lifecycle Management directly satisfies the business goals of reducing compute costs for fault-tolerant jobs and eliminating manual overhead when archiving aging media assets.

Adım Adım Çözüm

1
Evaluate compute requirement and cost profile
Workloads are bursty, stateless, and tolerant of interruptions, making them an ideal candidate for Spot VM / low-cost preemptible container execution rather than continuously running clusters or long-term baseline commitments.
Spot capacity offers significant cost savings (up to 60-91%) for workloads that do not require guaranteed immediate execution or zero-interruption SLAs.
2
Evaluate storage lifecycle management requirement
Configuring automated Cloud Storage Object Lifecycle Management rules handles the transition of raw objects to Coldline or Archive storage after 30 days automatically.
Automated lifecycle policy rules reduce human intervention and prevent ongoing storage cost accumulation for aged assets.

Anahtar Kavram

Cost Optimization for Batch Compute and Storage Lifecycle Management
Tahmini Süre:1m 30s
Soru 146Soru

A online gaming studio runs its real-time multiplayer backend in region `us-west1` (primary) and maintains a pilot light disaster recovery (DR) setup in region `us-east1`. During a simulated regional disaster recovery test, the team successfully promotes the cross-region database replica in `us-east1`. However, when the automated scaling scripts attempt to rapidly scale up the Managed Instance Groups (MIGs) in `us-east1` to handle the full production load, instance creation fails due to exceeding regional CPU limits (`QUOTA_EXCEEDED`). Which operational measure should the cloud architect implement to ensure reliable business continuity and failover execution?

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Cevap: Proactively request and maintain sufficient compute resource quotas in the secondary region (`us-east1`) to cover full production capacity prior to initiating any DR failover.

Cevap

Proactively request and maintain sufficient compute resource quotas in the secondary region (`us-east1`) to cover full production capacity prior to initiating any DR failover.
Ensuring operational reliability during a Disaster Recovery (DR) failover requires proactive capacity and quota planning. Because GCP quotas are enforced independently per region and project, a warm standby or pilot light architecture will fail to scale during a regional outage if the secondary region's compute quota is insufficient. Securing compute quota limits in advance ensures that scaling scripts can launch required instances immediately without encountering `QUOTA_EXCEEDED` errors.

Adım Adım Çözüm

1
Identify the root cause of the DR failover failure.
The failover failed because the target DR region (`us-east1`) did not have adequate compute CPU quota requested in advance to accommodate full production instance scaling.
GCP enforces resource quotas on a per-region, per-project basis independently of active production workloads.
2
Evaluate operational requirements for disaster recovery execution.
To achieve target RTO during a pilot light failover, compute resources must be able to scale up instantly without reaching administrative quota caps.
Quota increases require evaluation and cannot be relied upon in real time during an active regional incident.
3
Select the correct mitigation practice.
Request regional quota increases in advance so that the target DR region's quota limits accommodate peak production capacity.
Pre-approved quota ensures seamless compute expansion during DR execution.

Anahtar Kavram

Disaster Recovery Quota Planning and Execution Reliability
Soru 147Soru

A digital healthcare company runs patient telemetry and microservice workloads across Google Kubernetes Engine (GKE) clusters and on-premises infrastructure. The enterprise operations team needs to establish a centralized observability architecture. Requirements dictate that all administrative audit logs and application error events must be securely routed to a BigQuery dataset located in a dedicated compliance project for long-term retention. Additionally, operational metrics must stream into Cloud Monitoring, and non-essential debug logs must be filtered out without inadvertently dropping high-severity error events or audit records. Which architecture best meets these business and operational requirements following Google Cloud recommended practices?

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Cevap: Deploy OpenTelemetry Collectors using standard Cloud Logging and Cloud Monitoring exporters. Configure an Organization-level Log Router aggregated sink with explicit inclusion filters targeting the compliance BigQuery dataset, while defining exclusion filters scoped strictly to low-severity debug logs.

Cevap

Deploy OpenTelemetry Collectors using standard Cloud Logging and Cloud Monitoring exporters. Configure an Organization-level Log Router aggregated sink with explicit inclusion filters targeting the compliance BigQuery dataset, while defining exclusion filters scoped strictly to low-severity debug logs.
The solution using OpenTelemetry Collectors with standard exporters alongside an Organization-level Log Router aggregated sink ensures centralized compliance export to BigQuery while precisely filtering only low-severity debug logs to control costs without losing vital operational or audit data.

Adım Adım Çözüm

1
Analyze observability and telemetry ingestion requirements
Identify that metrics require Cloud Monitoring integration while audit logs and errors require central BigQuery long-term retention.
Different operational telemetry types require distinct destination pipelines for monitoring vs compliance storage.
2
Evaluate central log routing and cost management strategies
Configure Organization-level Log Router aggregated sinks to centralize logs into BigQuery while restricting exclusion filters to debug logs.
Overbroad exclusion filters risk dropping high-severity error and audit logs needed for compliance and incident response.
3
Apply security and identity best practices
Avoid primitive IAM roles and rely on security perimeters combined with fine-grained IAM roles for audit data protection.
Primitive roles grant excessive privileges, and IAM alone cannot guarantee exfiltration protection without VPC Service Controls.

Anahtar Kavram

Centralized Log Routing, Observability Integration, and Cost Optimization Guardrails
Tahmini Süre:2m 0s
Soru 148Soru

An enterprise Site Reliability Engineering (SRE) team is implementing an automated incident response and notification suppression pipeline on Google Cloud for a mission-critical financial settlement service. The pipeline must detect SLO degradation, prevent alert storms during active outages, execute automated self-healing without relying on long-lived service account keys or primitive IAM roles, and archive operational telemetry for post-mortems. In what chronological sequence should the SRE team structure the operational steps for this automated incident lifecycle?

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Cevap

The correct sequence begins with configuring multi-window multi-burn-rate SLO alerting, followed by publishing incident payloads to Pub/Sub while enabling Cloud Monitoring Muting Rules, invoking a Workload Identity-authenticated Cloud Run remediation handler for diagnostic log extraction, executing targeted recovery using granular IAM roles, and concluding with metric stabilization verification, un-muting alerts, and exporting incident telemetry to BigQuery.
The proper operational order reflects Google Cloud SRE best practices: (1) Detection using multi-window multi-burn-rate SLO alerts ensures precise identification of error budget depletion; (2) Event decoupling via Pub/Sub paired with Cloud Monitoring Muting Rules prevents alert storms during active mitigation; (3) Automated triage workers leverage Workload Identity to inspect correlation logs securely; (4) Targeted remediation executes using least-privilege IAM roles; and (5) Metrics are verified for recovery before resolving the incident, removing muting rules, and streaming telemetry to BigQuery for post-mortem analysis.

Adım Adım Çözüm

1
Detect error budget depletion via Cloud Monitoring multi-window multi-burn-rate alerting policies.
High-fidelity incident alert triggers upon identifying sustained consumption of the service error budget.
SLO multi-burn-rate logic prevents false positives and detects rapidly evolving outages as the initial entry point of the incident lifecycle.
2
Publish alert event to Cloud Pub/Sub and enable Cloud Monitoring Muting Rules.
Incident event dispatches to automated pipelines while downstream notification channels are suppressed.
Alert suppression prevents alert fatigue and notification storms across operations teams while automated remediation is underway.
3
Trigger Cloud Run triage container using Workload Identity federation to inspect Cloud Logging traces.
Diagnostic context is securely fetched without static service account keys or primitive roles.
Automated handlers must query log correlation IDs to confirm failure modes prior to executing stateful remediation actions.
4
Perform infrastructure mitigation using least-privilege IAM service account permissions.
Service baseline health is restored via automated traffic re-routing or canary rollback.
Remediation acts on confirmed telemetry to stabilize the application layer.
5
Verify metric stabilization in Cloud Monitoring, lift muting rules, and stream incident telemetry to BigQuery.
The incident state automatically resolves and historical telemetry is stored for SRE post-mortems.
Formal incident closure requires verifying recovery in Monitoring baselines before resetting alert mutes and capturing post-mortem data.

Anahtar Kavram

End-to-end GCP incident management lifecycle using SLO multi-burn-rate alerting, Pub/Sub orchestration, Muting Rules for alert fatigue mitigation, Workload Identity security, and post-incident analytics.
Tahmini Süre:3m 0s
Soru 149Soru

A healthcare provider processes nightly patient diagnostic imaging datasets using batch processing worker VMs in Google Cloud Compute Engine. During peak overnight runs, the workload rapidly scales from 20 instances to 500 n2standard8n2-standard-8 instances to ensure completion before morning clinical operations. During a scaling dry run, instance creation failed after reaching 100 instances due to regional resource limits. Which strategy should the cloud architect implement to ensure reliable infrastructure scaling and capacity for these batch workloads?

Cevabı ve açıklamayı göster

Cevap: Submit a regional vCPU quota increase request in advance for the target region and configure Compute Engine capacity reservations for the required peak compute resources.

Cevap

The cloud architect should submit a regional vCPU quota increase request in advance for the target region and configure Compute Engine capacity reservations for the peak baseline instances.
Proactively requesting a regional vCPU quota increase ensures that the GCP project limits accommodate the 500 n2standard8n2-standard-8 instances (4,0004,000 vCPUs). Combining this with Compute Engine capacity reservations guarantees that the physical compute capacity is reserved and ready in the specified zone during the nightly batch window.

Adım Adım Çözüm

1
Identify the cause of the provisioning failure during the workload scale-up.
The failure was caused by exceeding default project regional vCPU resource quotas when attempting to provision 500 n2standard8n2-standard-8 instances (4,0004,000 vCPUs total).
Google Cloud enforces regional quotas on compute resources to prevent accidental overallocation.
2
Evaluate capacity planning requirements for large-scale compute workloads.
A quota increase allows the project to request the required vCPU ceiling, while Compute Engine capacity reservations ensure physical hardware availability in the selected zone.
Having sufficient quota does not guarantee hardware availability during sudden capacity spikes unless capacity is explicitly reserved.
3
Select the correct GCP capacity optimization approach.
Combining regional quota increases with zonal reservations guarantees both administrative permission and physical capacity for peak workload execution.
This directly fulfills both scaling permission and resource availability requirements.

Anahtar Kavram

Capacity Planning and Regional Quota Management
Soru 150Soru

A financial analytics firm provisions its multi-environment Google Cloud infrastructure using Terraform executed via Cloud Build CI/CD pipelines. The lead cloud architect must establish a centralized Infrastructure as Code (IaC) governance model that prevents concurrent state updates, protects state file integrity, and adheres to the principle of least privilege. Which strategy should the architect implement?

Cevabı ve açıklamayı göster

Cevap: Configure a remote Terraform backend using a Cloud Storage bucket with Object Versioning enabled, and execute Cloud Build pipelines using a dedicated service account granted fine-grained, predefined IAM roles.

Cevap

Configure a remote Terraform backend using a Cloud Storage bucket with Object Versioning enabled, and execute Cloud Build pipelines using a dedicated service account granted fine-grained, predefined IAM roles.
The correct solution configures a Cloud Storage remote backend with Object Versioning enabled to provide distributed state locking and state historical backup. Combining this backend with a dedicated Cloud Build service account assigned fine-grained predefined IAM roles follows Google Cloud recommended architectural practices for secure, scalable Infrastructure as Code deployment.

Adım Adım Çözüm

1
Establish secure remote state management.
Configuring Google Cloud Storage (GCS) as a remote backend provides built-in state locking and object versioning to protect against race conditions and concurrent modifications.
Remote state centralization prevents local state divergence and guards against corrupting environment definitions.
2
Scope permissions according to least privilege.
Assign fine-grained predefined roles (or custom roles) to the Cloud Build service account managing the infrastructure execution.
Avoid using primitive roles like Editor or Owner to minimize the blast radius of automated pipeline operations.
3
Enforce strict deployment workflow controls.
Ensure all modifications pass through automated CI/CD code reviews and plan/apply pipelines rather than manual operator edits.
Prevents configuration drift between actual cloud resources and the declarative code state.

Anahtar Kavram

Terraform Remote Backend & IAM Least Privilege Governance
Tahmini Süre:1m 30s
Soru 151Soru

A telemetry organization ingests vehicle diagnostic logs into Cloud Storage. Once a week, an intensive batch processing job analyzes the aggregated files. The processing run requires substantial compute capacity for approximately 6 hours, can tolerate individual node interruptions, and must complete within a 12-hour SLA window. The business objective is to minimize total compute costs while avoiding unnecessary baseline infrastructure expenditure. Which architecture should you recommend?

Cevabı ve açıklamayı göster

Cevap: Deploy a Compute Engine Managed Instance Group (MIG) using Spot VMs with custom instance templates and queue-based autoscaling.

Cevap

Deploying a Compute Engine Managed Instance Group (MIG) using Spot VMs with queue-based autoscaling is the optimal choice to minimize compute costs for fault-tolerant, short-duration weekly batch processing.
Using a Compute Engine Managed Instance Group (MIG) configured with Spot VMs allows the system to scale compute resources dynamically for the 6-hour processing run at up to an 80% discount compared to standard rates. Because the batch job can tolerate node interruptions and has a 12-hour completion window, Spot VMs fulfill all business and SLA constraints at minimal cost.

Adım Adım Çözüm

1
Analyze workload characteristics and operational constraints
The batch job runs for 6 hours once per week, tolerates node termination, and has a 12-hour SLA window.
Fault tolerance and short operational duration make the workload ideal for pre-emptible, discounted infrastructure.
2
Evaluate GCP pricing models against usage patterns
Spot VMs offer up to 80% discount for interruptible compute, whereas CUDs and continuous On-Demand instances incur heavy costs for idle time during the remaining 162 hours of the week.
Paying continuous baseline pricing for intermittent periodic processing is financially inefficient.
3
Select compute deployment architecture
A Compute Engine MIG configured with Spot VMs scales up during the batch run and scales down to zero when idle.
This satisfies the requirement to minimize cost while meeting SLA bounds.

Anahtar Kavram

Cost optimization for fault-tolerant batch processing using Spot VMs and dynamic scaling.
Soru 152Soru

A multinational biomedical research organization is designing hybrid connectivity to transfer large genomic datasets between its on-premises laboratory center and Google Cloud VPCs. The architecture requires a minimum sustained dedicated bandwidth of 12 Gbps12\text{ Gbps}, consistent low latency, a 99.99%99.99\% availability SLA, and mandatory IPsec encryption for all data in transit across the hybrid connection. Which hybrid network architecture should the lead Cloud Architect recommend?

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Cevap: Provision a 99.99% HA Dedicated Interconnect topology using dual 10 Gbps links across two distinct edge availability domains (metropolitan locations), configured with Cloud Routers and HA VPN over Dedicated Interconnect.

Cevap

Deploy a 99.99% HA Dedicated Interconnect topology across two metropolitan locations using 10 Gbps circuits, integrated with HA VPN over Dedicated Interconnect for IPsec encryption.
The correct recommendation is to deploy a 99.99% HA Dedicated Interconnect topology across two metro locations using 10 Gbps circuits, integrated with HA VPN over Dedicated Interconnect. Dedicated Interconnect provides the required bandwidth (>10 Gbps), low latency, and 99.99% SLA when properly configured across dual edge availability domains. Running HA VPN over Dedicated Interconnect satisfies the requirement for IPsec encryption over the private link.

Adım Adım Çözüm

1
Analyze bandwidth and SLA constraints
The requirement of 12 Gbps12\text{ Gbps} continuous throughput exceeds standard single Cloud VPN tunnel capacity (3 Gbps3\text{ Gbps} per tunnel) and requires Dedicated Interconnect (10 Gbps or 100 Gbps circuits). To achieve a 99.99%99.99\% SLA, Google Cloud mandates dual interconnect attachments across two distinct metro locations.
Dedicated Interconnect is required for bandwidth >10 Gbps> 10\text{ Gbps} and guaranteed low latency/SLA.
2
Evaluate security and encryption constraints
Standard Dedicated Interconnect traffic travels unencrypted across private physical links. To satisfy the mandatory IPsec encryption requirement, HA VPN must be configured to run over the Dedicated Interconnect attachments.
HA VPN over Dedicated Interconnect combines high-capacity private circuits with IPsec encryption.
3
Validate network routing topology
Deploying dual Dedicated Interconnect circuits with HA VPN and Cloud Router dynamic BGP routing satisfies the 12 Gbps12\text{ Gbps} throughput requirement, 99.99%99.99\% SLA, and encryption compliance.
This configuration meets all business and architectural constraints without relying on unsupported non-transitive VPC peering behaviors.

Anahtar Kavram

Designing High-Availability Encrypted Hybrid Connectivity (Dedicated Interconnect + HA VPN)
Soru 153Soru

An enterprise financial platform hosts a high-throughput transaction processing API on Cloud Run backed by Cloud Spanner. The business alignment requires maintaining a rolling 30-day Service Level Objective (SLO) of 99.9% successful HTTP requests, while enabling feature teams to maintain rapid deployment velocity. The Site Reliability Engineering (SRE) team needs to establish an automated alerting strategy in Google Cloud Monitoring that minimizes alert fatigue from brief transient spikes while ensuring immediate paging for major outages that threaten the monthly error budget. Which alerting implementation should you configure?

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Cevap: Configure multi-window, multi-threshold burn-rate alerts that page on-call engineers when consuming a significant percentage of the error budget over short lookback windows (such as 2% over 1 hour) and create ticket alerts for sustained lower burn rates over longer windows (such as 5% over 6 hours).

Cevap

The SRE team should implement multi-window, multi-threshold burn-rate alerts based on error budget consumption rates.
Multi-window, multi-threshold burn rate alerting consumes metrics from Cloud Monitoring to calculate the exact speed at which the 30-day error budget is being depleted. By configuring a high burn rate threshold over a short window (e.g., 2% budget consumed in 1 hour, representing a 14.4x burn rate) for paging alerts, and a lower burn rate threshold over a longer window for ticket creation, the system balances rapid emergency incident response with protection against alert fatigue.

Adım Adım Çözüm

1
Define the Service Level Indicator (SLI) and Service Level Objective (SLO)
The SLI is the ratio of successful HTTP requests to total requests, and the SLO is set to 99.9% over a 30-day rolling window, yielding an allowable error budget of 0.1%.
Establishing explicit measurement metrics and error budgets provides the foundation for alerting logic.
2
Evaluate alerting methods against alert fatigue and responsiveness trade-offs
Static threshold alerts fire on minor transient events, whereas error budget burn-rate alerts quantify the urgency based on how fast the error budget is being consumed relative to time.
Burn rate monitoring protects deployment velocity and engineer focus by filtering non-critical incidents.
3
Configure multi-window multi-threshold alert policies in Cloud Monitoring
Short lookback windows with high burn rate thresholds catch severe outages immediately, while long lookback windows with lower burn rate thresholds capture persistent, slow budget leaks.
Combining short and long lookback windows eliminates alert reset delays and prevents missed incidents.

Anahtar Kavram

Error Budget Burn Rate Alerting in Google Cloud SRE Practices
Tahmini Süre:3m 0s
Soru 154Soru

A platform engineering team is deploying a Python application in an on-premises container environment. The application needs to programmatically create and manage resources across multiple Google Cloud projects using Google Cloud Client Libraries. Enterprise security policy strictly forbids downloading service account JSON key files and mandates short-lived credentials alongside fine-grained authorization. Which TWO configuration steps should the team implement to establish secure programmatic interaction with Google Cloud APIs?

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Cevabı ve açıklamayı göster

Cevap: Set up Workload Identity Federation using an on-premises OpenID Connect (OIDC) identity provider to allow the application to exchange short-lived federated tokens for Google Cloud service account tokens.; Initialize the Google Cloud Client Libraries using standard application code patterns that rely on Application Default Credentials (ADC) to auto-detect credential sources from the execution environment.

Cevap

The team should configure Workload Identity Federation using an on-premises OIDC identity provider for short-lived token exchange and initialize the Google Cloud Client Libraries using Application Default Credentials (ADC) for seamless credential discovery.
The combination of Workload Identity Federation and Application Default Credentials provides keyless authentication for on-premises container workloads. Workload Identity Federation trades external OIDC tokens for short-lived Google Cloud service account tokens, while ADC allows SDK client libraries to automatically discover and use those short-lived credentials without hardcoded configuration.

Adım Adım Çözüm

1
Establish federated identity trust with Google Cloud IAM
Configured Workload Identity Pool and Provider to trust external OIDC identity tokens.
Allows external workloads to authenticate programmatically without requiring downloadable service account keys.
2
Configure application environment for token exchange
Created a credential configuration file specifying the Workload Identity provider details and target service account.
Enables Application Default Credentials (ADC) to automatically exchange external tokens for short-lived Google Cloud access tokens.
3
Instantiate client libraries using implicit ADC loading
Client libraries automatically pick up credentials from the ADC environment location without hardcoded parameters.
Ensures clean, maintainable, and secure programmatic GCP interaction compliant with enterprise governance.

Anahtar Kavram

Programmatic GCP API authentication using Workload Identity Federation and Application Default Credentials (ADC)
Soru 155Soru

An enterprise deployment team uses an automated deployment service account to manage Compute Engine virtual machines in a dedicated Google Cloud folder named `App-Development`. The automated pipeline must be able to provision compute instances within this folder and assign a specific runtime service account (`app-runner-sa@`) to those instances. Following Google-recommended security practices and the principle of least privilege, which TWO IAM configuration steps should you execute?

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Grant the predefined `roles/compute.instanceAdmin.v1` role to the deployment service account bound at the `App-Development` folder level.; Grant the `roles/iam.serviceAccountUser` role to the deployment service account directly on the `app-runner-sa@` resource.

Cevap

The correct configurations are to grant the Compute Instance Admin role to the deployment service account at the folder level and grant the Service Account User role specifically on the runtime service account resource.
To provision Compute Engine instances within a folder structure while adhering to least privilege, permissions should be scoped at the specific folder level using predefined roles (`roles/compute.instanceAdmin.v1`). Furthermore, allowing an identity to attach a service account to a compute instance requires the `roles/iam.serviceAccountUser` role scoped specifically to the target service account identity.

Adım Adım Çözüm

1
Determine compute management scope
Identify that `roles/compute.instanceAdmin.v1` should be assigned on the `App-Development` folder node to leverage resource hierarchy inheritance across child projects.
Granting permissions at the folder level enforces administrative boundary constraints and operational efficiency without granting organization-wide access.
2
Determine service account impersonation/use scope
Identify that the deployment pipeline needs to attach `app-runner-sa@` to new VMs, which requires `roles/iam.serviceAccountUser` on that specific service account identity.
Bounding `roles/iam.serviceAccountUser` to the target service account resource ensures the pipeline cannot attach other sensitive service accounts in the project.

Anahtar Kavram

Resource Hierarchy IAM Role Inheritance and Service Account User Granular Delegation
Tahmini Süre:2m 0s
Soru 156Soru

A municipal water utility enterprise is migrating its smart meter analytics platform to Google Cloud. Executive leadership requires seamless operational continuity, strict compliance controls against data exfiltration, and a structured change management strategy for legacy operational staff transitioning from on-premises tools. Which TWO actions should the Lead Cloud Architect take to address technical requirements while facilitating organizational change? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Establish role-based training programs for operational teams while submitting proactive regional resource quota increase requests prior to workload deployment.; Enforce least-privilege access using predefined IAM roles while configuring VPC Service Controls perimeters to protect sensitive telemetry data.

Cevap

The correct architecture and change management strategy involves establishing structured enablement programs paired with proactive regional quota requests, along with enforcing least-privilege predefined IAM roles complemented by VPC Service Controls for perimeter defense.
The correct strategy combines technical preparedness and stakeholder change enablement. Proactive regional quota requests ensure infrastructure capacity is available at launch while training eases employee adoption. Granular predefined IAM roles together with VPC Service Controls satisfy strict compliance governance against data exfiltration.

Adım Adım Çözüm

1
Evaluate organizational change management and operational readiness requirements.
Identified the need for staff enablement programs combined with early quota capacity planning.
Technical migrations fail if legacy personnel lack training or if deployment hits resource quota bottlenecks.
2
Assess security and compliance boundaries for data protection.
Selected fine-grained predefined IAM roles and VPC Service Controls.
VPC Service Controls protect against data exfiltration across storage boundaries that standard IAM policies cannot restrict alone.

Anahtar Kavram

Organizational Change Enablement & Cloud Security Perimeter Governance
Tahmini Süre:2m 0s
Soru 157Soru

A SaaS provider offering real-time document collaboration tools is planning a major enterprise product release expected to cause a 5x surge in API traffic overnight. The application tier runs on Compute Engine Managed Instance Groups (MIGs) handling I/O-intensive requests, backed by a standard Cloud SQL database. During load testing, engineers noticed that CPU load remains low even when network request queues back up, and compute capacity risks reaching default GCP project caps during peak hours. Which TWO actions should the Cloud Architect execute during capacity planning to ensure seamless infrastructure workload scaling? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Submit preemptive requests for regional Compute Engine vCPU quota increases across all target regions prior to the launch.; Reconfigure the Managed Instance Group autoscaling policies to scale based on HTTP request capacity or custom I/O queue metrics rather than CPU utilization.

Cevap

The Cloud Architect should request regional vCPU quota increases in advance and reconfigure the MIG autoscaling policy to use HTTP request rate or custom I/O queue metrics instead of CPU utilization.
Preemptive regional quota increases prevent automated scaling actions from failing when peak demand hits GCP quota limits. Simultaneously, aligning autoscaling metrics with request rates or custom I/O queue depth ensures that I/O-bound application tiers scale out promptly even when CPU utilization remains low.

Adım Adım Çözüm

1
Analyze workload scaling bottlenecks and quota limitations.
Identified that I/O-intensive workloads do not saturate CPU, making CPU-based autoscaling ineffective, and determined that default GCP regional quotas could cap maximum instance scaling during peak demand.
Ensures scaling triggers appropriately and prevents capacity failure due to quota caps.
2
Select appropriate capacity planning metrics and quota management actions.
Configured custom metrics for MIG autoscaling and submitted advance regional vCPU quota requests.
Allows infrastructure to scale seamlessly in response to actual request load while guaranteeing cloud resource headroom.

Anahtar Kavram

Capacity Planning and Infrastructure Workload Scaling Optimization
Soru 158Soru

A financial services organization runs a mission-critical payment processing workload on Google Cloud. To improve operational reliability and incident response, the SRE team needs to implement an automated alerting and self-healing incident management architecture. The solution must fulfill two requirements:
1. Alert the SRE team based on consumed service level objective (SLO) error budget rates to catch both rapid budget exhaustion and slow, persistent errors without triggering false positives from brief transient spikes.
2. Automatically trigger downstream remediation workflows securely during severe incidents without violating least-privilege principles.

Which TWO actions should the SRE team take to achieve these objectives?

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Cevabı ve açıklamayı göster

Cevap: Create Cloud Monitoring alerting policies utilizing multi-window multi-burn-rate conditions targeting the payment service SLO.; Configure Cloud Monitoring notification channels to publish alert events to a Cloud Pub/Sub topic that triggers a Cloud Function operating with fine-grained, minimal IAM roles.

Cevap

The SRE team should implement multi-window multi-burn-rate alerting policies targeting the service SLO and route alert notifications to Cloud Pub/Sub to trigger a Cloud Function using fine-grained, minimal IAM roles.
The correct architecture uses multi-window multi-burn-rate alerting policies to monitor SLO error budget consumption accurately without alert fatigue, combined with Cloud Pub/Sub and Cloud Functions running under custom, least-privilege service accounts to execute automated self-healing workflows securely.

Adım Adım Çözüm

1
Select alerting mechanism for SLO error budget management
Identify multi-window multi-burn-rate conditions in Cloud Monitoring as the recommended GCP standard for monitoring SLO budget consumption accurately.
Static threshold alerts cause alert fatigue or miss slow budget erosion, whereas multi-burn-rate monitoring prevents false positives and ensures timely alerts.
2
Select secure automated remediation mechanism
Publish Cloud Monitoring alert notifications to Pub/Sub to trigger Cloud Functions running under dedicated service accounts with least-privilege roles.
Decoupling automated incident remediation through Pub/Sub and Cloud Functions ensures reliable execution, and using minimal IAM permissions satisfies security standards.

Anahtar Kavram

Incident Management and Automated Alerting via SLO Burn Rates and Pub/Sub Remediation Workflows
Soru 159Soru

A global gaming platform deployed on Google Cloud experiences periodic database latency spikes. These spikes cause temporary downstream HTTP 500 error bursts across Compute Engine backend instance groups. During these incidents, Cloud Monitoring generates hundreds of redundant notification emails per minute, causing severe alert fatigue. Furthermore, automated remediation scripts triggered directly by raw error thresholds attempt to reboot instance group VMs, leading to prolonged service degradation because load balancer health checks are configured to execute deep database queries. Which incident management and automated alerting architecture should you implement to eliminate alert storms and prevent cascading service outages during transient database latency?

Cevabı ve açıklamayı göster

Cevap: Transition alerting policies to multi-window burn rate conditions based on latency and error rate Service Level Indicators (SLIs), route alert notifications through Cloud Pub/Sub to trigger a Cloud Run remediation service that dynamically applies alerting silences during active incidents, and update backend load balancer health checks to query shallow HTTP health endpoints.

Cevap

Implement multi-window error budget burn rate alerting policies paired with Cloud Pub/Sub and Cloud Run for automated incident muting, and switch load balancer health checks to shallow local endpoints.
The solution addresses both root causes: using multi-window SLO burn rate alerting prevents alert storms caused by transient spikes, while decoupling backend health checks (using shallow local endpoints) prevents load balancers from tearing down healthy compute nodes when downstream databases experience transient latency. Automated event handling via Pub/Sub and Cloud Run enables controlled alert suppression and remediation.

Adım Adım Çözüm

1
Redesign alerting logic using Service Level Objectives (SLOs) and multi-window burn rate alerts in Cloud Monitoring.
Alerts trigger only when error budget consumption threatens the monthly target, eliminating alert storms from short transient spikes.
Multi-window burn rate alerting correlates short-term and long-term consumption windows to prevent false positives while remaining responsive to major outages.
2
Decouple health check probes from downstream backend dependencies.
Load balancers inspect shallow HTTP endpoints (e.g., local application process status) instead of executing deep database queries.
Deep dependency health checks cause load balancers to falsely mark instances as dead during transient database slowdowns, triggering cascading instance failures.
3
Automate incident notification routing and silence management via Cloud Pub/Sub and Cloud Run.
Notifications publish to Pub/Sub topics, triggering Cloud Run services that suppress duplicate alerts during active incidents.
Programmatic incident handling via Cloud Pub/Sub avoids uncoordinated script execution and prevents alert fatigue.

Anahtar Kavram

Multi-window SLO burn rate alerting and decoupled shallow health checking in enterprise GCP incident response.
Soru 160Soru

A telemetry platform team operating a global IoT data ingestion service on Google Cloud is restructuring their deployment pipeline and environment governance. Currently, engineers perform manual infrastructure modifications through the Cloud Console and authenticate CI/CD jobs using long-lived service account keys saved locally. As a Principal Cloud Architect, you are advising the development and operations teams on implementing GCP best practices for automation, security, and state management. Which TWO practices should you recommend? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Authenticate external CI/CD deployment pipelines using Workload Identity Federation rather than exporting long-lived service account JSON keys.; Store Terraform state files in a Cloud Storage bucket configured with object versioning and state locking enabled.

Cevap

The correct recommendations are authenticating external CI/CD deployment pipelines using Workload Identity Federation instead of long-lived keys, and storing Terraform state files in a Cloud Storage bucket configured with object versioning and state locking.
Advising DevOps teams effectively involves enforcing key security and operational principles. Authenticating pipelines using Workload Identity Federation avoids storing long-lived credentials, and using Cloud Storage for Terraform state management ensures centralized, locked, and versioned state file tracking.

Adım Adım Çözüm

1
Evaluate identity and authentication requirements for deployment automation.
Workload Identity Federation replaces long-lived key management with short-lived, federated credentials.
Eliminating exported service account keys mitigates credential exfiltration risk and management overhead.
2
Establish secure Infrastructure as Code (IaC) state management standards.
Remote state in Cloud Storage with versioning and object locking guarantees state consistency.
Prevents state file corruption and race conditions across multi-developer teams.

Anahtar Kavram

Advising Development and Operation Teams on Secure Automation and IaC Best Practices
ÖncekiSayfa 8 / 80Sonraki
Tüm alıştırma soruları — Google Cloud Professional Cloud Architect | Examkin