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Soru 141Soru →

A healthcare research organization runs daily genomics analysis batch jobs on Google Cloud. Each batch job execution requires an uninterrupted 4-hour run window and cannot tolerate instance preemptions. Resource profiling indicates that each worker task requires precisely 10 vCPUs and 20 GB of memory to run efficiently while minimizing per-vCPU software licensing costs. Which Compute Engine resource planning strategy meets these operational requirements at the lowest cost?

Cevabı ve açıklamayı göster

Cevap: Deploy standard Compute Engine VM instances using a custom machine type configured with 10 vCPUs and 20 GB of memory.

Cevap

Deploy standard Compute Engine VM instances using a custom machine type configured with 10 vCPUs and 20 GB of memory.
Configuring a custom Compute Engine machine type allows exact sizing of 10 vCPUs and 20 GB RAM, preventing cost overruns from over-provisioned predefined machine types and software licenses. Using standard VM instances guarantees that the 4-hour job executes without preemption.

Adım Adım Çözüm

1
Analyze workload continuity requirements.
The batch job requires 4 hours of uninterrupted runtime and cannot tolerate preemptions.
Spot VMs may be terminated by GCP at any time with a 30-second notice, making them incompatible with non-fault-tolerant long-running tasks.
2
Analyze compute resource ratio and licensing impact.
The workload requires a non-standard 10 vCPUs to 20 GB RAM ratio.
Predefined machine types (such as standard, highmem, or highcpu families) force over-provisioning vCPUs or memory, increasing compute and third-party software licensing costs.
3
Select the optimal Compute Engine machine configuration.
Configuring a custom machine type with 10 vCPUs and 20 GB memory on standard VMs provides exact sizing without interruption risk.
Compute Engine custom machine types allow tailoring vCPU counts and memory capacity to fit exact workload demands.

Anahtar Kavram

Custom Machine Types and Spot VM Suitability
Soru 142Soru →

A lead software engineer is designing the serverless architecture for a financial transaction verification service. The workload receives real-time gRPC calls, requires handling up to 80 concurrent requests per instance to optimize resource usage, and executes periodic reconciliation routines lasting up to 45 minutes. The application is packaged as a custom Docker container built on an unsupported language runtime. Which Google Cloud serverless compute option should be recommended to satisfy these requirements?

Cevabı ve açıklamayı göster

Cevap: Deploy the application to Cloud Run, because it natively supports custom container images, gRPC requests, multi-request concurrency per instance, and request timeouts up to 60 minutes.

Cevap

Deploy the application to Cloud Run, because it natively supports custom container images, gRPC requests, multi-request concurrency per instance, and request timeouts up to 60 minutes.
The choice emphasizing deployment to Cloud Run is correct because Cloud Run allows developers to deploy arbitrary stateless container images, natively supports gRPC protocol connections, allows setting concurrency up to 1000 requests per instance, and supports request timeouts up to 60 minutes, matching all operational constraints.

Adım Adım Çözüm

1
Analyze the workload requirements
Identified key constraints: custom Docker container runtime, gRPC protocol support, high request concurrency (80 requests per instance), 45-minute execution timeout, and serverless deployment.
Choosing the appropriate serverless compute option requires evaluating platform capabilities against execution limits and runtime requirements.
2
Evaluate Cloud Run against constraints
Cloud Run supports arbitrary custom container images, handles gRPC natively, allows configuring up to 1000 concurrent requests per instance, and allows setting request timeouts up to 60 minutes.
Cloud Run is designed specifically for containerized microservices requiring concurrency and extended execution windows.
3
Evaluate Cloud Functions limitations
Cloud Functions (1st gen) caps execution at 9 minutes and handles requests serially. While 2nd gen runs on Cloud Run infrastructure, Cloud Run directly provides full control over container image definitions, concurrency settings, and gRPC endpoints.
Direct deployment to Cloud Run satisfies all container, protocol, concurrency, and timeout constraints cleanly.

Anahtar Kavram

Planning Serverless Compute Options (Cloud Run vs Cloud Functions)
Soru 143Soru →

An administrator is designing the resource structure for a company migrating to Google Cloud. Which of the following statements correctly describe the characteristics of the Google Cloud resource hierarchy? (Select TWO answers.)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Folders allow flexible grouping of resources and can contain both projects and other sub-folders.; IAM access policies granted at higher levels of the resource hierarchy are inherited by child resources beneath them.

Cevap

The statements correctly describing the resource hierarchy are: 'Folders allow flexible grouping of resources and can contain both projects and other sub-folders' and 'IAM access policies granted at higher levels of the resource hierarchy are inherited by child resources beneath them'.
The correct statements correctly describe that folders can hold both projects and child sub-folders, and that IAM access policies defined at parent levels of the resource hierarchy flow down to child resources.

Adım Adım Çözüm

1
Review the structural components of the Google Cloud resource hierarchy.
Identify that the hierarchy follows Organization -> Folders -> Projects -> Resources.
Folders provide structural boundaries and can contain both projects and nested folders.
2
Analyze how IAM permissions flow through the hierarchy.
Confirm that policies propagate downward to child resources.
IAM inheritance is inherited from parent nodes and is purely additive.

Anahtar Kavram

Google Cloud Resource Hierarchy Structure and IAM Policy Inheritance
Soru 144Soru →

A system administrator is planning the Compute Engine provisioning strategy for two distinct company workloads: a fault-tolerant nightly batch data processing job and a steady-state web frontend server that operates continuously 24/7 throughout the year. Which TWO provisioning and pricing strategies should the administrator choose to optimize compute costs while satisfying operational requirements? (Select TWO)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Provision Spot VMs for the nightly batch processing workload to reduce resource costs.; Purchase Committed Use Discounts (CUDs) for the steady-state web frontend instances.

Cevap

Select the option recommending Spot VMs for the fault-tolerant nightly batch processing workload and the option recommending Committed Use Discounts (CUDs) for the continuous 24/7 web frontend instances.
Spot VMs provide significant cost reductions for stateless, fault-tolerant batch workloads that can tolerate unexpected interruptions. Meanwhile, Committed Use Discounts (CUDs) deliver substantial savings for continuous 24/7 steady-state resources without risk of preemption.

Adım Adım Çözüm

1
Analyze the workload characteristics for the batch processing job.
Identified that the nightly batch job is fault-tolerant and transient, making it an ideal candidate for low-cost Spot VM instances.
Spot VMs provide large discounts for workloads that can handle preemption.
2
Analyze the workload characteristics for the continuous web frontend.
Identified that the web frontend runs 24/7 throughout the year, requiring reliable availability and predictable baseline resource capacity.
Committed Use Discounts (CUDs) require a commitment to a consistent level of vCPU and memory usage over 1 or 3 years in exchange for significant cost savings.

Anahtar Kavram

Compute Engine Resource Planning: Spot VMs for Fault-Tolerant Workloads vs Committed Use Discounts for Steady-State Workloads
Soru 145Soru →

An enterprise financial analytics company is designing a Google Cloud compute strategy for two new application workloads:

1. Workload 1: A stateless web API that experiences unpredictable traffic spikes throughout the day and requires zero infrastructure management overhead along with automatic scale-to-zero capabilities during idle periods.
2. Workload 2: A 3-hour nightly transactional risk batch processing job that is stateless, fully fault-tolerant, and designed to resume smoothly if interrupted.

Which TWO compute resource deployment strategies should the Cloud Engineer recommend to satisfy these technical requirements while optimizing overall compute costs? (Select TWO.)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Deploy Workload 1 on Cloud Run to support automatic scaling down to zero instances during idle periods.; Deploy Workload 2 on Compute Engine using Spot Virtual Machines (VMs) to minimize processing costs.

Cevap

Deploying Workload 1 on Cloud Run and Workload 2 on Compute Engine using Spot VMs provides the optimal architectural and cost-effective solution.
Cloud Run is the optimal serverless execution engine for stateless web APIs requiring zero management and automatic scaling to zero when idle. For the fault-tolerant 3-hour batch processing job, Compute Engine Spot VMs provide up to 90% cost savings while accommodating potential interruptions.

Adım Adım Çözüm

1
Analyze Workload 1 requirements
Identified stateless web API needing zero infrastructure management, auto-scaling up to handle spikes, and scaling to zero when idle.
Cloud Run natively provisions containerized stateless applications, handles HTTP autoscaling, and reduces cost to zero during periods without traffic.
2
Analyze Workload 2 requirements
Identified a 3-hour long-running batch job that is fault-tolerant and stateless.
Because the batch job runs for 3 hours, it exceeds short-lived serverless function limits. Because it is fault-tolerant, Compute Engine Spot VMs offer massive cost discounts (up to 90%) while fulfilling the job requirement.
3
Evaluate and eliminate non-optimal alternatives
Fixed MIGs waste money during idle times, Cloud Functions timeout before 3 hours, and standard GKE Autopilot nodes incur higher baseline costs for a workload that tolerates preemptions.
Matching compute options directly to workload lifecycle and fault tolerance ensures optimal financial and operational trade-offs.

Anahtar Kavram

Selecting GCP compute services based on statefulness, execution duration, auto-scaling characteristics, and fault tolerance to optimize cost and management overhead.
Soru 146Soru →

An organization is designing a serverless HTTP microservice to process uploaded financial documents. The workload requires running a specialized C++ processing tool packaged inside a custom container image, serving up to 50 concurrent HTTP requests per instance to optimize cost, and supporting request execution timeouts up to 30 minutes. Which Google Cloud serverless compute option should you recommend?

Cevabı ve açıklamayı göster

Cevap: Cloud Run, because it natively supports custom container images, multi-request concurrency per instance, and HTTP request timeouts up to 60 minutes.

Cevap

Cloud Run, because it natively supports custom container images, multi-request concurrency per instance, and HTTP request timeouts up to 60 minutes.
Cloud Run is the ideal choice for stateless containerized workloads that require custom Linux binaries, multi-request concurrency per instance to optimize operational costs, and HTTP timeouts up to 60 minutes.

Adım Adım Çözüm

1
Analyze the workload requirements
Identified requirements: custom container image with a C++ binary, multi-request concurrency (50 requests/instance), and a 30-minute HTTP request execution timeout.
Choosing the appropriate serverless compute product requires matching runtime constraints against platform capabilities.
2
Evaluate Google Cloud serverless options against the requirements
Cloud Run allows deploying arbitrary container images, configuring concurrency up to 1000 requests per instance, and setting HTTP timeouts up to 60 minutes.
Cloud Run is tailored for containerized web applications needing high concurrency and long HTTP timeouts.
3
Select the correct compute service
Recommend Cloud Run as the optimal serverless compute option.
It satisfies all architectural, concurrency, containerization, and timeout requirements while maintaining a serverless operational model.

Anahtar Kavram

Planning Serverless Compute Options (Cloud Run vs. Cloud Functions)
Tahmini Süre:1m 30s
Soru 147Soru →

A principal security architect is configuring the Google Cloud resource hierarchy for a multi-regional organization. The hierarchy consists of an Organization node, under which sits a top-level folder named 'Production-Workloads' containing two sub-folders: 'App-Services' and 'Data-Analytics'. A DevOps engineer has been assigned the 'Project Creator' role (`roles/resourcemanager.projectCreator`) at the 'Production-Workloads' folder level, and the 'Billing Account User' role (`roles/billing.user`) on the corporate Billing Account. The engineer needs to create a new project named 'analytics-pipeline-prod' under the nested 'Data-Analytics' sub-folder and associate it with the corporate Billing Account. However, an explicit IAM deny policy or missing permission is suspected of blocking the deployment. Based on the Google Cloud resource hierarchy IAM inheritance model and billing requirements, which statement correctly describes the permission behavior for this scenario?

Cevabı ve açıklamayı göster

Cevap: The project creation will succeed because 'roles/resourcemanager.projectCreator' granted at the parent folder ('Production-Workloads') automatically inherits down to the nested 'Data-Analytics' sub-folder, fulfilling the project creation requirement alongside the Billing Account User role.

Cevap

The project creation will succeed because 'roles/resourcemanager.projectCreator' granted at the parent folder level inherits transitively down to the nested sub-folder, providing the necessary project creation rights in combination with the Billing Account User role on the billing account.
In Google Cloud, IAM policies set at parent nodes in the resource hierarchy (such as an Organization or parent Folder) are inherited transitively by all descendant nodes, including nested sub-folders and projects. Because the DevOps engineer has 'roles/resourcemanager.projectCreator' assigned at the 'Production-Workloads' parent folder level, this role inherits down to the 'Data-Analytics' sub-folder. Combined with the 'roles/billing.user' role assigned directly on the Billing Account resource, the engineer possesses all required authorizations to create the project and link it to billing.

Adım Adım Çözüm

1
Analyze IAM Policy Inheritance in Google Cloud Resource Hierarchy
Permissions granted at higher hierarchy levels (Organization or parent Folder) automatically propagate down to all child nodes, including nested sub-folders and projects.
Google Cloud IAM follows a transitive inheritance model down the hierarchy tree.
2
Verify Permissions Required for Project Creation and Billing Account Linkage
Creating a project in a folder requires 'resourcemanager.projects.create' (included in 'roles/resourcemanager.projectCreator') on the target folder node. Linking to a billing account requires 'roles/billing.user' on the target Billing Account resource.
These two permissions fulfill the complete requirements for establishing a billed project in the target folder.
3
Evaluate the DevOps Engineer's Effective Rights at the Target Sub-Folder
The engineer holds Project Creator inherited at the 'Data-Analytics' sub-folder and Billing Account User on the Billing Account, enabling successful execution.
Inherited permissions provide full authorization without requiring explicit duplicate role assignments at every sub-folder.

Anahtar Kavram

Resource Hierarchy Policy Inheritance and Billing Account Linking Permissions
Soru 148Soru →

A financial analytics company is planning to deploy a high-performance quantitative risk simulation service on Google Cloud. The application runs as a custom containerized background process requiring custom Linux sysctl kernel parameters to optimize network socket buffers. The workload operates continuously 24/7, requires persistent block storage for local scratch caching, and cannot tolerate sudden instance preemption or termination. The cloud engineering team wants to select the most suitable compute platform while satisfying all operational requirements. Which compute strategy should the team choose?

Cevabı ve açıklamayı göster

Cevap: Deploy the workload on Google Kubernetes Engine (GKE) Standard node pools composed of standard Compute Engine virtual machines.

Cevap

Deploy the workload on Google Kubernetes Engine (GKE) Standard node pools composed of standard Compute Engine virtual machines.
Google Kubernetes Engine (GKE) Standard node pools using standard Compute Engine VMs offer the necessary flexibility to customize host OS settings—such as modifying Linux sysctl kernel parameters—while providing reliable, continuous 24/7 compute capacity without risk of abrupt preemption.

Adım Adım Çözüm

1
Analyze workload requirements and non-negotiable technical constraints.
Identified constraints: continuous 24/7 execution (no abrupt preemption allowed), custom Linux sysctl kernel parameter tuning requirement, containerized runtime, and persistent block storage.
Defining strict technical constraints prevents choosing serverless or preemptible options that violate kernel or availability mandates.
2
Evaluate serverless and fully managed compute platforms (Cloud Run and GKE Autopilot).
Cloud Run and GKE Autopilot are eliminated because fully managed serverless environments restrict low-level host OS kernel configuration (sysctl tuning).
GKE Autopilot enforces strict node security baselines preventing custom sysctl flags, and Cloud Run completely abstracts host kernel configurations.
3
Evaluate VM provisioning types (Spot vs Standard VMs).
Spot VMs are eliminated because they can be preempted at any time with a 30-second warning, violating the requirement for uninterrupted 24/7 operation.
Spot VMs are designed strictly for fault-tolerant, stateless, or batch processing applications.
4
Select the optimal compute architecture.
GKE Standard with standard Compute Engine virtual machines provides container orchestration while permitting custom node OS kernel configuration and non-preemptible 24/7 availability.
GKE Standard node pools support custom sysctl settings through system configurations or daemonsets on standard VM instances.

Anahtar Kavram

Planning Compute Infrastructure and Evaluating OS/Workload Constraints
Soru 149Soru →

A digital media platform requires an architecturally optimized Google Cloud compute environment to process user-uploaded video files stored in a Cloud Storage bucket. The workload exhibits the following operational characteristics:

• Video processing tasks are triggered dynamically upon file upload.
• Each processing job runs containerized binaries (FFmpeg with custom plugins) taking between 10 to 45 minutes to finish.
• Memory requirements reach up to 16 GB per task execution.
• Traffic patterns fluctuate dramatically, experiencing extreme peak spikes during live events and near-zero activity overnight.
• The engineering team requires zero node management overhead and mandates paying strictly for active compute processing time without idle infrastructure spend.

Which compute solution should be recommended to satisfy these requirements?

Cevabı ve açıklamayı göster

Cevap: Deploy the containerized processing worker as Cloud Run Jobs, triggered via Eventarc events from Cloud Storage, with 16 GB memory allocated per task instance.

Cevap

Deploying the containerized processing worker as Cloud Run Jobs, triggered via Eventarc events from Cloud Storage, with 16 GB memory allocated per task instance is the optimal solution.
Cloud Run Jobs is designed specifically for containerized workloads that run to completion. It supports execution times up to 24 hours, memory allocations up to 32 GB (or more in current configurations), event-driven invocation via Eventarc from Cloud Storage, zero infrastructure management, and true pay-per-use billing that scales to zero during idle overnight hours.

Adım Adım Çözüm

1
Analyze workload execution duration and runtime dependencies.
Tasks run containerized binaries for 10–45 minutes with 16 GB RAM requirements.
Traditional event-driven functions (like 1st-Gen Cloud Functions) fail due to a 9-minute execution timeout. Cloud Run Jobs supports long-running containerized tasks up to 24 hours.
2
Evaluate infrastructure operational overhead and scaling constraints.
The requirement demands zero node management overhead and serverless billing.
Compute Engine Managed Instance Groups require OS maintenance, patch management, and complex scaling configuration, whereas Cloud Run Jobs is fully managed.
3
Assess cost trade-offs during idle period fluctuations.
Traffic drops to near zero overnight.
Sustained or Committed Use Discounts on 24/7 VM instances incur continuous costs regardless of utilization, whereas Cloud Run Jobs charges $0 when no jobs are executing.

Anahtar Kavram

Selecting serverless container execution paradigms (Cloud Run Jobs vs Cloud Functions vs Compute Engine) based on execution duration, operational overhead, and cost structure.
Soru 150Soru →

An administrator needs to enable the Kubernetes Engine API (`container.googleapis.com`) for a Google Cloud project named `analytics-prod` using the `gcloud` CLI. Which TWO of the following requirements or actions are necessary to successfully enable the service API on the targeted project?

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Cevabı ve açıklamayı göster

Cevap: Specify the target project `analytics-prod` explicitly in the `gcloud services enable` command or set it as the active default project in `gcloud config`.; Ensure the identity executing the command holds the Service Usage Admin (`roles/serviceusage.serviceUsageAdmin`) role or equivalent API enablement permissions on project `analytics-prod`.

Cevap

To enable a Google Cloud API on a specific project, you must target the correct project (via `gcloud` flags or configuration) and possess adequate permissions such as the Service Usage Admin role on that project.
Enabling a service API requires both administrative IAM permissions (such as Service Usage Admin) on the target project and directing the `gcloud` CLI command specifically to that project context.

Adım Adım Çözüm

1
Identify the required IAM permissions for enabling APIs.
The identity running the command requires permissions contained in roles like Service Usage Admin (`roles/serviceusage.serviceUsageAdmin`).
Enabling service APIs requires modifying project resource configurations via the `serviceusage.services.enable` permission.
2
Target the specific project where the API service should run.
The CLI command must include `--project=analytics-prod` or have `analytics-prod` configured as the default project.
Google Cloud service APIs are enabled at the individual project scope, not globally or hierarchically across the organization.

Anahtar Kavram

API Enablement Scoping and IAM Requirements
Tahmini Süre:1m 0s
Soru 151Soru →

An enterprise is migrating a specialized healthcare analytics platform to Google Cloud Compute Engine. The system architecture requires planning for two distinct compute workloads:

1. Core Data Store: A memory-intensive, stateful relational database running continuously 24/7 with steady, predictable resource requirements for a planned 3-year operational period.
2. Log Processing Pipeline: A stateless, highly fault-tolerant batch job that executes nightly and can resume processing seamlessly if interrupted.

Which TWO compute provisioning decisions should you make to optimize overall costs while maintaining workload availability requirements? (Select 2 answers.)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Select a memory-optimized machine family for the Core Data Store and attach a 3-year Committed Use Discount (CUD) to cover the baseline capacity.; Provision Spot Virtual Machines (VMs) for the batch Log Processing Pipeline instances.

Cevap

The two correct provisioning choices are selecting a memory-optimized machine family with a 3-year Committed Use Discount (CUD) for the Core Data Store, and using Spot Virtual Machines (VMs) for the Log Processing Pipeline.
Selecting a memory-optimized machine type with a 3-year Committed Use Discount (CUD) accurately addresses the memory-bound, continuous stateful database requirements with maximum cost savings. Provisioning Spot VMs for the batch log processing pipeline leverages massive discounts for stateless, fault-tolerant workloads that easily handle interruptions.

Adım Adım Çözüm

1
Analyze the resource and persistence requirements of the Core Data Store.
The database is memory-bound, stateful, and runs continuously 24/7 for 3 years.
Memory-bound stateful applications require memory-optimized VM families to ensure performance, while continuous 24/7 usage over 3 years qualifies for 3-year Committed Use Discounts (CUDs).
2
Analyze the operational requirements of the Log Processing Pipeline.
The batch job is stateless, fault-tolerant, runs periodically, and tolerates unexpected interruptions.
Fault-tolerant batch jobs that can restart or resume seamlessly are prime candidates for Spot VMs, which offer deep discounts in exchange for preemptibility.
3
Evaluate potential anti-patterns for database and cluster management.
Reject Spot VMs for stateful databases due to preemptibility risks, and reject GKE Autopilot when custom kernel parameters are required.
Spot VM preemptions break database availability SLAs, and GKE Autopilot locks down host node OS kernel settings.

Anahtar Kavram

Compute Engine Workload Matching and Cost Optimization
Tahmini Süre:1m 45s
Soru 152Soru →

A medical device company is evaluating Google Cloud compute solutions for two new application workloads:

1. Workload Alpha: An event-driven task that processes uploaded medical images whenever a new file arrives in a Cloud Storage bucket. Each processing task takes 10 to 15 seconds to execute a Python metadata extraction script.
2. Workload Beta: A legacy web service packaged as a container image that requires custom OS-level kernel tuning (sysctl parameters) and specific root privileges on the underlying host node.

Which TWO deployment choices should the team select to satisfy the technical requirements of these workloads while following Google Cloud architectural best practices? (Select TWO)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Deploy Workload Alpha on Cloud Functions triggered directly by Cloud Storage event notifications.; Deploy Workload Beta on Google Kubernetes Engine (GKE) Standard clusters using custom node pools.

Cevap

Deploying Workload Alpha on Cloud Functions and deploying Workload Beta on Google Kubernetes Engine (GKE) Standard clusters.
Cloud Functions is designed specifically for event-driven processing triggered by Cloud Storage events with zero infrastructure management. For workloads requiring explicit host OS kernel modifications such as custom sysctl settings, GKE Standard provides full operational control over node pools and underlying virtual machine configurations.

Adım Adım Çözüm

1
Analyze Workload Alpha requirements
Workload Alpha requires short (10-15 seconds) event-driven execution upon file creation in Cloud Storage.
Cloud Functions (or Cloud Run with Eventarc) is specifically designed for quick, event-triggered serverless execution without managing infrastructure.
2
Analyze Workload Beta requirements
Workload Beta requires custom OS-level kernel tuning (sysctl parameters) and host root access.
Fully managed environments like Cloud Run and GKE Autopilot restrict host node access and kernel configuration. GKE Standard provides administrator access to node configurations via custom node pools.
3
Combine the optimal service choices
Match Workload Alpha to Cloud Functions and Workload Beta to GKE Standard.
This pairing minimizes management overhead for the event-driven task while meeting the strict host-level customization demands of the legacy workload.

Anahtar Kavram

Selecting GCP Compute Services Based on Workload Granularity and Host OS Control Requirements
Tahmini Süre:1m 30s
Soru 153Soru →

An enterprise financial technology organization is designing the Compute Engine infrastructure for a compliance auditing system that runs two distinct workload components:

1. Workload 1: An overnight audit reconciliation process that is stateless, fully fault-tolerant, saves state progress to Cloud Storage every 10 minutes, and requires maximum cost optimization.
2. Workload 2: A core continuous transaction validation service that runs uninterrupted 24 hours a day, 365 days a year, cannot tolerate unexpected instance terminations, and requires a specific memory-to-vCPU ratio of 29 GB memory and 4 vCPUs to comply with third-party software license boundaries.

Which TWO Compute Engine resource planning and pricing decisions should you implement to satisfy both technical constraints and cost efficiency? (Select TWO choices)

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Cevabı ve açıklamayı göster

Cevap: Provision Spot Virtual Machines (VMs) for Workload 1 to minimize operational cost for checkpointed, fault-tolerant batch processing.; Configure a Custom Machine Type with 4 vCPUs and 29 GB memory backed by a Committed Use Discount (CUD) for Workload 2.

Cevap

Deploy Workload 1 using Spot Virtual Machines (VMs) and configure Workload 2 using a Custom Machine Type (4 vCPUs, 29 GB RAM) combined with a Committed Use Discount (CUD).
Spot VMs provide deep cost discounts for stateless, checkpointed batch applications that can gracefully handle instance preemption. For continuous 24/7 workloads with non-standard resource constraints, Custom Machine Types allow exact allocation of vCPUs and memory to satisfy licensing limits, and Committed Use Discounts (CUDs) maximize cost reduction for predictable, long-term operational commitments.

Adım Adım Çözüm

1
Analyze Workload 1 requirements (fault-tolerant, batch, checkpointed, cost-sensitive).
Identify that Spot VMs offer maximum discount for interruptible, fault-tolerant workloads.
Because progress is checkpointed every 10 minutes, preemption does not cause loss of work, making Spot VMs ideal.
2
Analyze Workload 2 resource constraints (non-standard 4 vCPUs / 29 GB RAM ratio).
Select a Custom Machine Type to match exact licensing boundaries without paying for unused predefined resources.
Predefined machine types (e.g., n2-standard-4 with 16 GB or n2-highmem-4 with 32 GB) either under-provision RAM or force over-payment for unnecessary capacity.
3
Analyze Workload 2 availability and billing requirements (24/7 continuous runtime, no unexpected terminations allowed).
Pair the Custom Machine Type with a 1-year or 3-year Committed Use Discount (CUD).
CUDs deliver substantial price reductions for predictable, steady-state enterprise capacity without risking preemption.

Anahtar Kavram

Selecting Compute Engine provisioning models (Spot vs. On-Demand/CUD) and machine specifications (Custom vs. Predefined) based on fault tolerance, non-standard memory-to-vCPU ratios, and runtime predictability.
Soru 154Soru →

A enterprise architecture team is designing a serverless solution on Google Cloud for two distinct backend services:

1. Service A: A legacy C++ application that processes incoming REST HTTP requests. It requires custom OS-level system packages, must handle up to 250 concurrent requests per container instance to minimize cost, and needs an execution timeout configured for up to 45 minutes to process batch operations.
2. Service B: A lightweight Node.js event handler that responds to real-time object finalized events in Cloud Storage and completes execution in less than 3 seconds per trigger.

Which TWO architectural decisions correctly align with Google Cloud serverless best practices for these workloads? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Deploy Service A to Cloud Run, because Cloud Run supports custom container images with system binaries, allows configurable request concurrency per instance, and supports execution timeouts up to 60 minutes for HTTP-triggered workloads.; Deploy Service B to Cloud Functions (2nd gen) or Cloud Run, using Eventarc or Cloud Storage event triggers to process event payloads with minimal cold-start overhead.

Cevap

Deploy Service A to Cloud Run due to custom container environment support, multi-concurrency capabilities, and 60-minute execution timeouts. Deploy Service B to Cloud Functions (2nd gen) or Cloud Run to efficiently process Cloud Storage events using Eventarc integration.
Cloud Run is the optimal choice for Service A because it runs arbitrary Docker containers (supporting custom C++ system libraries), handles up to 1000 concurrent requests per container instance, and allows timeouts up to 60 minutes for HTTP services. Cloud Functions (2nd gen) or Cloud Run is the optimal choice for Service B because both natively integrate with Eventarc to process Cloud Storage events with automatic scaling.

Adım Adım Çözüm

1
Analyze the requirements for Service A
Service A requires custom C++ OS binaries, 250 concurrent requests per instance, and a 45-minute request timeout.
Cloud Run natively supports custom container images (allowing custom C++ libraries), concurrency up to 1000 requests per container, and an execution timeout of up to 60 minutes for HTTP services.
2
Analyze the requirements for Service B
Service B is a lightweight event-driven function processing Cloud Storage triggers in under 3 seconds.
Cloud Functions (2nd gen) or Cloud Run with Eventarc provide native event-driven triggers for Cloud Storage, scaling to zero when idle and processing short bursts efficiently.
3
Evaluate and eliminate incorrect architectural choices
Cloud Functions (1st gen) lacks custom container OS support, cannot handle multiple concurrent requests per instance, and caps timeouts at 9 minutes. Spot VMs add unnecessary infrastructure management for simple event handling.
Matching workload requirements to appropriate serverless execution paradigms ensures operational efficiency and adherence to Google Cloud recommended practices.

Anahtar Kavram

Selecting between Cloud Run and Cloud Functions based on containerization, concurrency, timeout, and event-trigger constraints.
Soru 155Soru →

An enterprise engineering team is planning the serverless compute components for a data ingestion and reporting pipeline on Google Cloud. The system must meet two distinct operational requirements:

1. A containerized Go microservice that handles incoming HTTP webhooks, requires request concurrency (handling up to 80 requests simultaneously per instance), and processes long-running reporting jobs that can take up to 45 minutes per request.
2. A lightweight event-driven snippet written in Python that executes quickly whenever a new message is published to a Cloud Pub/Sub topic to log metadata in a database.

Which TWO serverless compute deployment decisions should the team make to satisfy these requirements? (Select TWO)

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Cevabı ve açıklamayı göster

Cevap: Deploy the containerized Go microservice to Cloud Run because it supports custom container images, request concurrency of multiple requests per instance, and HTTP execution timeouts of up to 60 minutes.; Deploy the Python script as a Cloud Function with a Cloud Pub/Sub trigger because it provides a simplified, event-driven execution environment ideal for single-purpose code snippets reacting to Pub/Sub events.

Cevap

The correct serverless compute design decisions are to deploy the containerized Go microservice to Cloud Run and deploy the Python event handler as a Cloud Function triggered by Cloud Pub/Sub.
Selecting Cloud Run for the Go microservice satisfies the containerization, concurrency (up to 80 requests/instance), and long-running execution (up to 60 minutes) requirements. Selecting Cloud Functions for the Python script provides an optimal serverless, event-driven architecture triggered directly by Pub/Sub messages without needing container build pipelines.

Adım Adım Çözüm

1
Analyze the requirements for the containerized Go microservice.
The service requires running a custom container image, handling up to 80 concurrent requests per instance, and supporting an execution timeout of 45 minutes.
Cloud Run allows custom containers, supports concurrency up to 1000 requests per instance, and permits request execution timeouts up to 60 minutes, matching all specified constraints.
2
Analyze the requirements for the lightweight Python event handler.
The service requires a quick, single-purpose code snippet triggered by Cloud Pub/Sub messages.
Cloud Functions is specifically built for lightweight, event-driven code snippets triggered natively by GCP services such as Pub/Sub without container overhead.

Anahtar Kavram

Selecting between Cloud Run and Cloud Functions based on concurrency, execution timeout limits, containerization needs, and event triggers.
Soru 156Soru →

An enterprise retail organization is planning the migration of its order fulfillment processing system to Google Cloud. The workload comprises two core services with distinct operational profiles:

1. Fulfillment API: A stateless HTTP web service packaged as a main application container alongside a sidecar telemetry container. It receives unpredictable burst traffic during flash sales events and experiences long idle periods, requiring automatic scaling down to zero instances to eliminate costs when idle.
2. Inventory Reconciliation Worker: An asynchronous, fault-tolerant batch processing application that runs overnight to reconcile inventory databases. Individual worker tasks are stateless and designed to checkpoint progress so interrupted tasks can resume safely without data loss.

Which TWO compute platform deployment strategies should you recommend to satisfy these requirements while optimizing cost and minimizing operational overhead?

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Cevabı ve açıklamayı göster

Cevap: Deploy the Fulfillment API to Cloud Run using multi-container deployment support.; Deploy the Inventory Reconciliation Worker on Compute Engine Spot VM instances managed by a Managed Instance Group (MIG).

Cevap

The optimal deployment strategies are deploying the multi-container HTTP API service to Cloud Run and executing the fault-tolerant batch inventory worker on Compute Engine Spot VMs managed within a Managed Instance Group (MIG).
Cloud Run is the ideal choice for stateless containerized web applications needing multi-container (sidecar) support and automatic scaling down to zero when idle. For batch workloads that are fault-tolerant and implement state checkpointing, Compute Engine Spot VMs in a Managed Instance Group deliver massive cost savings with built-in resilience against preemption.

Adım Adım Çözüm

1
Assess compute platform options for the HTTP API service with sidecar logging.
Identify that Cloud Run supports multi-container instances (sidecars) and scales to zero when traffic stops, matching the stateless bursty HTTP API requirements.
Cloud Functions does not support multi-container sidecar deployments, whereas GKE would incur continuous cluster management overhead and baseline node costs during idle periods.
2
Assess compute options for the fault-tolerant nightly batch workload.
Select Compute Engine Spot VMs in a Managed Instance Group (MIG).
Because the batch application is stateless, fault-tolerant, and checkpoints its state, taking advantage of Spot VM discounts offers maximum cost savings without risk of data loss.

Anahtar Kavram

Selecting GCP Compute Services Based on Workload Characteristics and Cost Trade-offs
Tahmini Süre:2m 0s
Soru 157Soru →

A financial enterprise is establishing its Google Cloud resource hierarchy. A Cloud Operations team must configure access so that a group of developers can create new projects inside a specific folder named Analytics-Dev and link those newly created projects to the central corporate billing account 012345-6789AB-CDEF01. The solution must strictly adhere to the principle of least privilege without granting billing administration or organization-wide project creation rights. Which TWO IAM role assignments must be implemented to fulfill these requirements?

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Cevabı ve açıklamayı göster

Cevap: Grant the developer group the Project Creator (roles/resourcemanager.projectCreator) role on the Analytics-Dev folder.; Grant the developer group the Billing Account User (roles/billing.user) role directly on billing account 012345-6789AB-CDEF01.

Cevap

To allow developers to create projects under a specific folder and link them to a billing account, grant the Project Creator role on the target folder and the Billing Account User role directly on the billing account.
To create projects under a specific folder, principals require the Project Creator (roles/resourcemanager.projectCreator) role scoped to that folder. To attach those new projects to an enterprise billing account, principals also require the Billing Account User (roles/billing.user) role granted on the specific billing account. Together, these two roles grant the exact permissions needed to execute the workflow while maintaining least privilege.

Adım Adım Çözüm

1
Determine the minimal IAM role required for creating projects in a target folder.
Identify roles/resourcemanager.projectCreator applied at the Analytics-Dev folder resource level.
Permissions inherit down the hierarchy, so scope project creation to the folder level to avoid organization-wide creation rights.
2
Determine the minimal IAM role required for linking projects to a Google Cloud Billing Account.
Identify roles/billing.user applied directly to the target Billing Account resource.
Linking a project to a billing account requires resourcemanager.projects.create on the project container (or folder) AND billing.resourceAssociations.create on the billing account.
3
Validate against least privilege and resource hierarchy constraints.
Confirm that roles/resourcemanager.projectCreator on the folder and roles/billing.user on the billing account satisfy both conditions with minimal access.
This avoids primitive roles, excessive billing permissions, and structural IAM placement misconfigurations.

Anahtar Kavram

Project creation requires the Project Creator role on the parent resource container (folder or organization), and linking a billing account requires the Billing Account User role bound directly to the billing account.
Soru 158Soru →

An enterprise DevOps team is preparing to scale out a mission-critical workload across multiple regions within a Google Cloud production project. During deployment testing in `us-east1`, automated scripts fail because the requested N2 Virtual CPUs (vCPUs) and regional external IP addresses exceed the current project limits. The team needs to configure IAM permissions for quota operations and manage resource limits effectively. Which TWO statements accurately describe GCP resource quota behavior and the required procedure for requesting quota adjustments? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Resource quotas are enforced per project either regionally or globally, so obtaining a quota increase for N2 vCPUs in `us-central1` does not increase the N2 vCPU quota in `us-east1`.; Submitting a request for a quota increase in the Google Cloud Console or via gcloud requires the user or service account to have the Quota Administrator role (`roles/servicemanagement.quotaAdmin`) or an IAM role containing `serviceusage.quotas.update`.

Cevap

The correct statements are that resource quotas are enforced per project either regionally or globally (raising a quota in one region does not affect another), and submitting a quota increase request requires an IAM role with quota update permissions such as Quota Administrator (`roles/servicemanagement.quotaAdmin`).
GCP resource quotas for compute infrastructure are isolated by region and project. Increasing a quota in one region (such as `us-central1`) does not affect another region (`us-east1`). Additionally, performing quota increase requests via the Google Cloud Console or gcloud CLI requires specific permissions, namely `serviceusage.quotas.update`, which is included in the Quota Administrator role (`roles/servicemanagement.quotaAdmin`).

Adım Adım Çözüm

1
Analyze the regional scope of GCP Compute Engine resource quotas.
Identify that Compute Engine quotas such as N2 vCPUs and static IP addresses are scoped to specific regions within a specific project. An increase in `us-central1` has zero impact on `us-east1`.
GCP enforces regional isolation and resource allocation per region per project.
2
Determine the IAM permissions required to request quota increases.
Confirm that the principal executing the request needs `serviceusage.quotas.update`, which is provided by `roles/servicemanagement.quotaAdmin`.
Least privilege principles and administrative security in GCP require specific quota permissions to alter resource caps.
3
Evaluate misconceptions regarding budget alerts and resource hierarchy inheritance.
Reject the ideas that budget alerts alter quota caps or that organizational settings automatically raise quota caps for all child projects.
Budgets only monitor costs and emit alerts, while quotas are project-level limits that do not cascade automatically down from organization policies.

Anahtar Kavram

GCP Resource Quotas regional isolation and IAM Quota Administrator permissions
Soru 159Soru →

You are tasked with linking a newly created Google Cloud project to an existing corporate Cloud Billing account. Following Google Cloud's principle of least privilege, which pair of IAM roles must be granted to your user account to complete this task?

Cevabı ve açıklamayı göster

Cevap: Billing Account User on the Cloud Billing account and Project Billing Manager on the project

Cevap

To link a project to a Cloud Billing account under least privilege, you must have the Billing Account User role on the billing account and the Project Billing Manager role on the project.
Linking a Google Cloud project to a Cloud Billing account requires permissions on both entities. The Billing Account User role (roles/billing.user) grants the ability to associate projects with the billing account, and the Project Billing Manager role (roles/billing.projectManager) grants the ability to manage billing associations on the specific project. Together, these predefined roles fulfill the task while adhering to the principle of least privilege.

Adım Adım Çözüm

1
Identify the resource permission requirements for linking a project to a billing account.
Linking requires permissions on two distinct Google Cloud resources: the Cloud Billing account and the Cloud Project.
Google Cloud IAM evaluates access control separately for billing account objects and project hierarchy objects.
2
Select the least-privilege predefined role for the Cloud Billing account.
Billing Account User (roles/billing.user) provides the resourcemanager.projects.createBillingAssignment permission without granting full administration rights.
Billing Account Administrator grants broad administrative rights over payment methods and agreements, which are unnecessary for simple project linking.
3
Select the least-privilege predefined role for the Cloud Project.
Project Billing Manager (roles/billing.projectManager) allows linking and unlinking the project to a billing account.
Using Project Billing Manager avoids granting broad primitive Owner or Editor permissions on the project.

Anahtar Kavram

Project to Billing Account Linking Permissions
Tahmini Süre:50s
Soru 160Soru →

An enterprise health-tech company is designing a serverless telemetry and diagnostic platform on Google Cloud. The system consists of two distinct workload components:

1. Workload 1: A diagnostic image processing microservice that relies on custom-compiled C++ binaries, requires handling up to 50 concurrent HTTP/2 requests per instance, and executes batch image transformations lasting up to 30 minutes.
2. Workload 2: A lightweight Node.js event handler that parses metadata whenever a diagnostic JSON report is uploaded to a Cloud Storage bucket and saves the parsed data to Cloud Firestore.

Which serverless compute configurations should the architecture team choose to meet these requirements while optimizing for operational efficiency? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Deploy Workload 1 to Cloud Run because it supports custom container images containing native binary dependencies, HTTP/2 multi-concurrency, and request timeouts up to 60 minutes.; Deploy Workload 2 to Cloud Functions because it provides a fully managed, event-driven execution environment for lightweight code snippets triggered directly by Cloud Storage bucket events without requiring container management.

Cevap

Deploy Workload 1 to Cloud Run to satisfy custom container image, HTTP/2 concurrency, and 30-minute execution timeout demands; deploy Workload 2 to Cloud Functions to handle Cloud Storage events with minimal operational overhead.
Cloud Run is ideal for Workload 1 because it allows deploying arbitrary stateless container images containing custom C++ binaries, supports up to 1000 concurrent requests per instance, and supports request execution timeouts up to 60 minutes. Cloud Functions is ideal for Workload 2 because it offers a simplified, source-code-based deployment model designed for lightweight event handlers that trigger directly from Cloud Storage bucket mutations.

Adım Adım Çözüm

1
Analyze Workload 1 requirements for containerization, concurrency, and timeouts.
Workload 1 requires custom C++ binaries, 50 concurrent HTTP/2 requests per instance, and a 30-minute execution window. These capabilities match Cloud Run, which allows custom container images and supports up to 60-minute request timeouts and multi-concurrency.
Cloud Functions source-based deployment does not easily accommodate custom compiled C++ binary runtimes or container customization required for this workload.
2
Analyze Workload 2 requirements for event triggering and operational overhead.
Workload 2 is a lightweight Node.js snippet triggered by Cloud Storage object creation. Cloud Functions natively integrates with Cloud Storage events and executes code without needing a Docker container pipeline.
Using Cloud Functions for small event-driven snippets minimizes operational maintenance compared to managing containers or VM instances.
3
Select the correct pair of serverless compute options.
Choosing Cloud Run for Workload 1 and Cloud Functions for Workload 2 satisfies all architectural constraints.
This combination aligns each workload with its optimal serverless execution paradigm on Google Cloud.

Anahtar Kavram

Planning serverless compute options by evaluating containerization needs, request concurrency, execution timeout limits, and event trigger mechanisms.
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Tüm alıştırma soruları — Google Cloud Associate Cloud Engineer | Examkin