A company runs a Java 17 microservice that processes real-time financial transactions. The application receives a large number of transactions per second, each with a timestamp, amount, and type. The current implementation uses a sequential stream to filter and aggregate transactions into a Map . The team observes high latency and CPU spikes during peak loads. They suspect the stream pipeline is inefficient. The pipeline code is: Map stats = transactions.stream() .filter(t -> t.getTimestamp().isAfter(Instant.now().minusSeconds(60))) .collect(Collectors.groupingBy(Transaction::getType, Collectors.summarizingDouble(Transaction::getAmount))); The transactions list is an ArrayList that is frequently modified by other threads (adding new transactions). The system has multiple CPU cores available. Which of the following changes is the MOST effective way to improve performance while maintaining correctness?

Use .stream().parallel() with a custom thread pool and ensure the stream source is not modified during operation.

Replace ArrayList with CopyOnWriteArrayList and use .parallelStream() with the same collector.

Replace the collector with Collectors.groupingByConcurrent() and use .parallelStream() directly on the original list.

Question 188 of 509

Working with Streams and Lambda Expressions →hardMultiple ChoiceObjective-mapped

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

This 1Z0-829 practice question tests your understanding of working with streams and lambda expressions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A company runs a Java 17 microservice that processes real-time financial transactions. The application receives a large number of transactions per second, each with a timestamp, amount, and type. The current implementation uses a sequential stream to filter and aggregate transactions into a Map<TransactionType, DoubleSummaryStatistics>. The team observes high latency and CPU spikes during peak loads. They suspect the stream pipeline is inefficient. The pipeline code is:

Map<TransactionType, DoubleSummaryStatistics> stats = transactions.stream() .filter(t -> t.getTimestamp().isAfter(Instant.now().minusSeconds(60))) .collect(Collectors.groupingBy(Transaction::getType, Collectors.summarizingDouble(Transaction::getAmount)));

The transactions list is an ArrayList that is frequently modified by other threads (adding new transactions). The system has multiple CPU cores available. Which of the following changes is the MOST effective way to improve performance while maintaining correctness?

Question 1hardmultiple choice

Full question →

A
Use .stream().parallel() with a custom thread pool and ensure the stream source is not modified during operation.
Why wrong: Ensuring no modification is difficult in a real-time system; this approach does not address the root cause of concurrent access efficiently.
B
Use .parallelStream() on a snapshot (new ArrayList<>(transactions)) and keep the same collector.
Correct. The snapshot eliminates concurrent modification issues, and parallel processing improves throughput.
C
Replace ArrayList with CopyOnWriteArrayList and use .parallelStream() with the same collector.
Why wrong: CopyOnWriteArrayList is inefficient for frequent writes; also, groupingBy is not concurrent and may not perform optimally.
D
Replace the collector with Collectors.groupingByConcurrent() and use .parallelStream() directly on the original list.
Why wrong: Although groupingByConcurrent is thread-safe, the source list may be modified during iteration, leading to undefined behavior.

Full breakdown with real-world context →

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

✓

Use .parallelStream() on a snapshot (new ArrayList<>(transactions)) and keep the same collector.

Creating a snapshot (new ArrayList<>(transactions)) ensures the stream operates on a consistent, immutable view, avoiding ConcurrentModificationException. Using parallelStream then leverages multiple cores. The groupingBy collector works correctly in parallel because it has a combiner. This combination balances performance and safety.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Answer analysis

Option-by-option breakdown

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

✗
Use .stream().parallel() with a custom thread pool and ensure the stream source is not modified during operation.
Why it's wrong here
Ensuring no modification is difficult in a real-time system; this approach does not address the root cause of concurrent access efficiently.
✓
Use .parallelStream() on a snapshot (new ArrayList<>(transactions)) and keep the same collector.
Why this is correct
Correct. The snapshot eliminates concurrent modification issues, and parallel processing improves throughput.
Related concept
Static NAT maps one inside address to one outside address.
✗
Replace ArrayList with CopyOnWriteArrayList and use .parallelStream() with the same collector.
Why it's wrong here
CopyOnWriteArrayList is inefficient for frequent writes; also, groupingBy is not concurrent and may not perform optimally.
✗
Replace the collector with Collectors.groupingByConcurrent() and use .parallelStream() directly on the original list.
Why it's wrong here
Although groupingByConcurrent is thread-safe, the source list may be modified during iteration, leading to undefined behavior.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Real-world example

How this comes up in practice

A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.

What to study next

Got this wrong? Here's your next step.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-829 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this 1Z0-829 question test?

Working with Streams and Lambda Expressions — This question tests Working with Streams and Lambda Expressions — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Use .parallelStream() on a snapshot (new ArrayList<>(transactions)) and keep the same collector. — Creating a snapshot (new ArrayList<>(transactions)) ensures the stream operates on a consistent, immutable view, avoiding ConcurrentModificationException. Using parallelStream then leverages multiple cores. The groupingBy collector works correctly in parallel because it has a combiner. This combination balances performance and safety.

What should I do if I get this 1Z0-829 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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