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Working with Streams and Lambda ExpressionsmediumMultiple ChoiceObjective-mapped

1Z0-829 Working with Streams and Lambda Expressions Practice Question

A team needs to process a large collection of orders to calculate total revenue per region. They decide to use parallel streams to improve performance. Which statement about using parallel streams for this task is true?

⚠ Common exam trap

It's easy for candidates to assume parallel streams are always faster (Option C) or that they cannot use custom thread pools (Option D), but the core exam focus is on the requirement for stateless, non-interfering lambdas to ensure correctness in parallel processing.

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

Using a parallel stream with a stateful lambda operation can lead to incorrect results.

Parallel streams split the workload across multiple threads, and if the lambda operation is stateful (e.g., modifying a shared variable like a counter or a non-thread-safe collection), it can cause race conditions and produce incorrect results. The Streams API documentation explicitly warns against using stateful lambdas with parallel streams to avoid data integrity issues.

Answer analysis

Option-by-option breakdown

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

  • The stream() method returns a parallel stream by default.

    Why it's wrong here

    stream() returns a sequential stream; parallelStream() returns a parallel stream.

  • Using a parallel stream with a stateful lambda operation can lead to incorrect results.

    Why this is correct

    Stateful lambdas (e.g., accumulating into a non-thread-safe collection) cause race conditions in parallel pipelines.

  • Parallel streams always provide better performance than sequential streams.

    Why it's wrong here

    Parallelism has overhead; it only improves performance if tasks are CPU-bound and splittable.

  • Parallel streams cannot be used with custom thread pools.

    Why it's wrong here

    Parallel streams use the common ForkJoinPool by default, but custom pools can be used by submitting the stream task to your own ForkJoinPool.

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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