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Scaling with Google Cloud operationshardMultiple ChoiceObjective-mapped

Cloud Digital Leader Scaling with Google Cloud operations Practice Question

A company uses Cloud Functions (2nd gen) to process events from Pub/Sub. During traffic spikes, function instances scale but latency increases. They want to maximize throughput per instance. What should they configure?

⚠ Common exam trap

Google Cloud often tests the misconception that scaling out (max instances) or increasing resources (memory) is the primary way to handle throughput, when the key to per-instance efficiency is concurrency tuning.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Increase the concurrency setting.

Increasing the concurrency setting allows each Cloud Functions (2nd gen) instance to handle multiple requests simultaneously, maximizing throughput per instance during traffic spikes. By default, concurrency is 1, meaning each instance processes one event at a time; raising this value enables parallel processing within a single instance, reducing the need to scale out and lowering latency.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the concurrency setting.

    Why this is correct

    In Cloud Functions 2nd gen, the concurrency setting controls how many events a single instance can process simultaneously. By default it is 1, so each instance handles one request at a time. Increasing concurrency lets one instance multiplex many events, directly raising per-instance throughput and reducing the need to spin up additional instances.

  • Allocate more memory.

    Why it's wrong here

    Allocating more memory gives each instance greater resources for computation and caching, which can help memory-intensive functions run faster. However, it does not change how many requests an instance accepts at once — concurrency remains unchanged. The bottleneck of per-instance event processing is parallelism, not memory capacity, so this option does not address the throughput issue.

  • Increase the max instances limit.

    Why it's wrong here

    The max instances limit is a horizontal scaling bound — it caps how many function instances can coexist to handle traffic. Raising it allows Cloud Functions to create more instances under load, increasing total system throughput. But it has no effect on the number of events each individual instance can process concurrently, so per-instance efficiency remains the same.

  • Increase the function timeout.

    Why it's wrong here

    Function timeout defines the maximum execution duration for a single invocation before it is forcibly terminated. Extending it gives longer-running functions more time to finish, which can reduce failures for slow processes. It does not allow an instance to accept additional events in parallel, so it cannot increase the number of events processed concurrently per instance.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.