A financial services company has a microservice that processes trade confirmations. The service receives a stream of Trade objects (with fields: id (long), symbol (String), quantity (int), price (double)) and needs to compute the total value (quantity * price) for each symbol, but only for trades with quantity > 0 and price > 0. The result should be a Map mapping symbol to total value. The current implementation uses a for loop with manual aggregation, but it is error-prone and difficult to parallelize. The team decides to refactor using the Stream API. The DataSource provides a Stream trades(). The code must be efficient and handle large datasets. Which approach best meets these requirements?

trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), Double::sum))

trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), (v1, v2) -> v1 + v2))

trades().filter(t -> t.quantity() > 0 && t.price() > 0).reduce(new HashMap<>(), (map, t) -> { map.merge(t.symbol(), t.quantity() * t.price(), Double::sum); return map; }, (m1, m2) -> { m1.putAll(m2); return m1; })

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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 financial services company has a microservice that processes trade confirmations. The service receives a stream of Trade objects (with fields: id (long), symbol (String), quantity (int), price (double)) and needs to compute the total value (quantity * price) for each symbol, but only for trades with quantity > 0 and price > 0. The result should be a Map<String, Double> mapping symbol to total value. The current implementation uses a for loop with manual aggregation, but it is error-prone and difficult to parallelize. The team decides to refactor using the Stream API. The DataSource provides a Stream<Trade> trades(). The code must be efficient and handle large datasets. Which approach best meets these requirements?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

Question 1hardmultiple choice

Full question →

A
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.groupingBy(Trade::symbol, Collectors.summingDouble(t -> t.quantity() * t.price())))
Correct and idiomatic.
B
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), Double::sum))
Why wrong: Works but toMap is not intended for grouping; groupingBy is more semantic.
C
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), (v1, v2) -> v1 + v2))
Why wrong: Works but groupingBy is better for grouping operations.
D
trades().filter(t -> t.quantity() > 0 && t.price() > 0).reduce(new HashMap<>(), (map, t) -> { map.merge(t.symbol(), t.quantity() * t.price(), Double::sum); return map; }, (m1, m2) -> { m1.putAll(m2); return m1; })
Why wrong: Reduce with mutable accumulation is not recommended; combiner is incorrect (overwrites instead of merging).

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

✓

trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.groupingBy(Trade::symbol, Collectors.summingDouble(t -> t.quantity() * t.price())))

Option A is correct because it uses `Collectors.groupingBy` with a downstream `Collectors.summingDouble` collector, which is the idiomatic and efficient way to group trades by symbol and sum their computed values (quantity * price) after filtering out invalid trades. This approach is concise, leverages the Stream API's built-in parallelization support, and avoids manual accumulation or mutable state issues.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

✓
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.groupingBy(Trade::symbol, Collectors.summingDouble(t -> t.quantity() * t.price())))
Why this is correct
Correct and idiomatic.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), Double::sum))
Why it's wrong here
Works but toMap is not intended for grouping; groupingBy is more semantic.
✗
trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.toMap(Trade::symbol, t -> t.quantity() * t.price(), (v1, v2) -> v1 + v2))
Why it's wrong here
Works but groupingBy is better for grouping operations.
✗
trades().filter(t -> t.quantity() > 0 && t.price() > 0).reduce(new HashMap<>(), (map, t) -> { map.merge(t.symbol(), t.quantity() * t.price(), Double::sum); return map; }, (m1, m2) -> { m1.putAll(m2); return m1; })
Why it's wrong here
Reduce with mutable accumulation is not recommended; combiner is incorrect (overwrites instead of merging).

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may choose a `toMap` or `reduce` variant thinking they are more flexible, but they overlook the subtle correctness issues with mutable reduction or the need for a proper merge function in parallel streams, while `groupingBy` with a downstream collector is the intended pattern for this scenario.

Detailed technical explanation

How to think about this question

Under the hood, `Collectors.groupingBy` with `summingDouble` uses a `Map` accumulator that merges values via the `Collector`'s combiner, which correctly sums partial results from parallel threads. The `reduce` approach in option D is flawed because `HashMap` is mutable and the combiner `m1.putAll(m2)` overwrites duplicate keys instead of summing their values, leading to data loss in parallel execution. In real-world high-throughput trade processing, using the correct collector ensures thread-safety and optimal performance without manual synchronization.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A practitioner preparing for the 1Z0-829 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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: trades().filter(t -> t.quantity() > 0 && t.price() > 0).collect(Collectors.groupingBy(Trade::symbol, Collectors.summingDouble(t -> t.quantity() * t.price()))) — Option A is correct because it uses `Collectors.groupingBy` with a downstream `Collectors.summingDouble` collector, which is the idiomatic and efficient way to group trades by symbol and sum their computed values (quantity * price) after filtering out invalid trades. This approach is concise, leverages the Stream API's built-in parallelization support, and avoids manual accumulation or mutable state issues.

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

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 11, 2026

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