Quick Answer
The answer is to replace .stream() with .parallelStream(). This is correct because parallelStream enables the stream to process the filter operations concurrently across multiple CPU cores, splitting the large dataset into smaller chunks that are processed in parallel threads. For a list with millions of entries, this parallel execution dramatically reduces processing time while preserving the exact same business logic and filter conditions. On the Oracle Java Foundations 1Z0-811 exam, this question tests your understanding of stream performance optimization and the distinction between sequential and parallel stream processing. A common trap is assuming that changing the filter order or using a different collector will improve speed, but the real bottleneck for large datasets is single-threaded execution. Remember the memory tip: "Parallel for performance, sequential for simplicity" — when you see millions of records, think parallelStream to leverage multicore processing.
This 1Z0-811 practice question tests your understanding of java basics and syntax. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 mobile app backend uses Java streams to process user data. The code snippet filters users who are active and older than 18, then collects them into a list:
List<User> result = users.stream() .filter(u -> u.isActive()) .filter(u -> u.getAge() > 18) .collect(Collectors.toList());
Performance metrics show that this stream operation is slow when the user list has millions of entries. The team wants to improve performance without changing the business logic. Which change would most likely improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Replace .stream() with .parallelStream().
Parallel processing can utilize multiple cores for large datasets.
Use a HashSet instead of List for users collection.
Why wrong: Does not affect filtering speed.
Replace the stream with a traditional for loop.
Why wrong: Might be slightly faster but less scalable.
Combine both filters into one using logical AND operator.
Why wrong: Minimal impact; still sequential.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Replace .stream() with .parallelStream().
Option A is correct because replacing `.stream()` with `.parallelStream()` enables parallel processing of the stream, which can leverage multiple CPU cores to process the filter operations concurrently. For large datasets (millions of entries), this can significantly reduce execution time by splitting the workload across threads, while preserving the same business logic and order of operations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Replace .stream() with .parallelStream().
Why this is correct
Parallel processing can utilize multiple cores for large datasets.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use a HashSet instead of List for users collection.
Why it's wrong here
Does not affect filtering speed.
Replace the stream with a traditional for loop.
Why it's wrong here
Might be slightly faster but less scalable.
Combine both filters into one using logical AND operator.
Why it's wrong here
Minimal impact; still sequential.
Common exam traps
The trap here is that candidates may think combining filters (Option D) is the most effective optimization, but Cisco tests the understanding that parallelization is the key performance lever for large data sets, not minor syntactic changes.
Detailed technical explanation
Under the hood, `parallelStream()` uses the common ForkJoinPool to split the source into substreams, process them in parallel, and then combine results. However, for small datasets or operations with high overhead (e.g., I/O-bound tasks), parallelization can actually degrade performance due to thread management costs. In this scenario, with millions of entries and simple CPU-bound filters, parallelization is likely to yield substantial speedup on multi-core systems.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 1Z0-811 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Java Basics and Syntax — This question tests Java Basics and Syntax — Read the scenario before looking for a memorised answer..
The correct answer is: Replace .stream() with .parallelStream(). — Option A is correct because replacing `.stream()` with `.parallelStream()` enables parallel processing of the stream, which can leverage multiple CPU cores to process the filter operations concurrently. For large datasets (millions of entries), this can significantly reduce execution time by splitting the workload across threads, while preserving the same business logic and order of operations.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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