- A
Use hot key detection and split the hot key into multiple sub-keys (e.g., append a random number).
Splitting a hot key distributes its values across multiple workers, reducing bottleneck.
- B
Enable the Dataflow service's automatic reshuffling feature.
Why wrong: Automatic reshuffling is not a feature; Dataflow may optimize shuffle, but not specifically for skew.
- C
Use CoGroupByKey to reduce the number of keys.
Why wrong: CoGroupByKey is for joining multiple PCollections, not for reducing skew of a single PCollection.
- D
Increase the number of worker machines.
Why wrong: More workers can parallelize but doesn't address the fundamental skew if one key still goes to one worker.
- E
Use Combine.perKey with a combiner to aggregate values locally before shuffling.
Local combining reduces the amount of data being shuffled, which helps even if keys are skewed.
Quick Answer
The correct actions are salting the hot key and using Combine.perKey with a combiner for local aggregation. Salting works by splitting a single skewed key into multiple sub-keys—often by appending a random number—so that its values are distributed across many workers during the shuffle, preventing any one worker from becoming a bottleneck. The combiner technique reduces the volume of data sent over the network by merging values locally on each worker before the group-by-key operation, which is especially effective when the combiner is associative and commutative. On the Google Professional Data Engineer exam, this question tests your understanding of Dataflow’s shuffle mechanics and common anti-patterns; a frequent trap is assuming that simply increasing worker count solves skew, but without salting or combining, the hot key still overwhelms a single shard. Remember the mnemonic “Salt and Combine” to recall that you need both distribution and local reduction to tame a hot key.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. 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.
You are optimizing a Dataflow pipeline that performs a group-by-key transformation on a large, skewed dataset. The pipeline is experiencing high latency due to data skew (some keys have many more values). Which TWO actions can help mitigate the skew? (Choose two.)
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 hot key detection and split the hot key into multiple sub-keys (e.g., append a random number).
Option A is correct because splitting a hot key into multiple sub-keys (e.g., by appending a random number) distributes the values across multiple shards during the shuffle phase, reducing the load on any single worker. This technique, often called "salting," is a standard pattern in Dataflow and Apache Beam to handle data skew by breaking the bottleneck caused by a single key with disproportionately many values.
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.
- ✓
Use hot key detection and split the hot key into multiple sub-keys (e.g., append a random number).
Why this is correct
Splitting a hot key distributes its values across multiple workers, reducing bottleneck.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable the Dataflow service's automatic reshuffling feature.
Why it's wrong here
Automatic reshuffling is not a feature; Dataflow may optimize shuffle, but not specifically for skew.
- ✗
Use CoGroupByKey to reduce the number of keys.
Why it's wrong here
CoGroupByKey is for joining multiple PCollections, not for reducing skew of a single PCollection.
- ✗
Increase the number of worker machines.
Why it's wrong here
More workers can parallelize but doesn't address the fundamental skew if one key still goes to one worker.
- ✓
Use Combine.perKey with a combiner to aggregate values locally before shuffling.
Why this is correct
Local combining reduces the amount of data being shuffled, which helps even if keys are skewed.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that simply adding more workers (Option D) or enabling automatic reshuffling (Option B) can fix data skew, when in fact these actions do not address the root cause of a single key being processed by one shard.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's group-by-key operation uses a hash of the key to assign it to a specific shard; a hot key causes that shard to receive an imbalanced amount of data, leading to straggler tasks. Salting works by appending a random suffix to the hot key, effectively creating multiple sub-keys that hash to different shards, and then a subsequent per-key aggregation (e.g., Combine.perKey) merges the partial results. In real-world scenarios, you must carefully choose the number of sub-keys to balance load without creating too many small keys that increase overhead.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use hot key detection and split the hot key into multiple sub-keys (e.g., append a random number). — Option A is correct because splitting a hot key into multiple sub-keys (e.g., by appending a random number) distributes the values across multiple shards during the shuffle phase, reducing the load on any single worker. This technique, often called "salting," is a standard pattern in Dataflow and Apache Beam to handle data skew by breaking the bottleneck caused by a single key with disproportionately many values.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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