1Z0-829 Working with Streams and Lambda Expressions Practice Question
A team implements a stream pipeline that processes a large dataset in parallel. They use a stateful lambda expression inside the map operation to maintain a count. What is the most likely outcome?
⚠ Common exam trap
Many candidates assume synchronization or thread-safety fixes the issue, but the Streams API's contract requires stateless lambdas for correctness in parallel pipelines, and synchronization does not restore deterministic behavior or guarantee correct results.
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
✓
The pipeline will produce non-deterministic results due to race conditions.
Using a stateful lambda (one that mutates shared mutable state) inside a parallel stream's map operation introduces race conditions. The map operation is intended to be stateless and non-interfering; when multiple threads concurrently update a shared counter without proper synchronization, the result becomes non-deterministic and unpredictable.
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 pipeline will produce correct results if the stateful lambda is synchronized.
Why it's wrong here
Synchronization can cause contention and degrade performance, but it may work; however, it is not a recommended approach and may still have ordering issues.
- ✓
The pipeline will produce non-deterministic results due to race conditions.
Why this is correct
Stateful lambdas in parallel streams are discouraged because they introduce shared mutable state that leads to race conditions and unpredictable results.
- ✗
The pipeline will throw a ConcurrentModificationException.
Why it's wrong here
ConcurrentModificationException occurs when the stream source is modified during iteration, not due to stateful lambda.
- ✗
The pipeline will always produce the same result regardless of parallelism.
Why it's wrong here
Parallel stream processing with stateful lambda is inherently non-deterministic.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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