- A
Use batch prediction instead of online prediction.
Why wrong: Changes the serving pattern, not a best practice for reducing latency for online prediction.
- B
Move the features to Cloud Storage and read them directly.
Why wrong: Would bypass Feature Store benefits and may not improve latency.
- C
Increase the number of nodes in the feature store cluster.
Why wrong: May improve throughput but does not address latency per query due to high cardinality.
- D
Use feature store caching with a larger cache size.
Caching frequently accessed features reduces BigQuery calls and latency.
Question 681 of 1,000
mediumMultiple ChoiceObjective-mapped
Reduce Vertex AI Feature Store Latency with Caching
This PMLE practice question tests your understanding of pmle exam topics. 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 team uses Vertex AI Feature Store to serve features for online predictions. They notice that the online serving latency is high for certain features. The features are stored in a BigQuery source with high cardinality. What is the best practice to reduce latency?
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.
Quick Answer
The answer is to use feature store caching with a larger cache size. This directly reduces Vertex AI Feature Store online serving latency by storing frequently accessed feature values in memory, eliminating repeated, expensive lookups to the BigQuery source, which is particularly slow with high cardinality features. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how caching mitigates latency bottlenecks in online prediction pipelines, a common trap being to mistakenly choose query optimization or data denormalization, which don’t address the repeated fetch overhead. Remember the memory tip: “Cache the cardinal, don’t query the cardinal” — high cardinality means more unique keys, so a larger cache holds more of them, slashing round trips to BigQuery.
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 feature store caching with a larger cache size.
The best practice to reduce online serving latency for high-cardinality features in Vertex AI Feature Store is to enable caching with a larger cache size (Option D). Caching stores frequently accessed feature values in memory, reducing the need to query the BigQuery source for each prediction request. For high-cardinality features, a larger cache can hold more distinct values, minimizing cache misses. Option A (batch prediction) is not suitable for online serving. Option B (moving to Cloud Storage) does not integrate with Feature Store and may still have latency. Option C (increasing nodes) can improve throughput but does not directly address repeated access latency as effectively as caching.
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 batch prediction instead of online prediction.
Why it's wrong here
Changes the serving pattern, not a best practice for reducing latency for online prediction.
- ✗
Move the features to Cloud Storage and read them directly.
Why it's wrong here
Would bypass Feature Store benefits and may not improve latency.
- ✗
Increase the number of nodes in the feature store cluster.
Why it's wrong here
May improve throughput but does not address latency per query due to high cardinality.
- ✓
Use feature store caching with a larger cache size.
Why this is correct
Caching frequently accessed features reduces BigQuery calls and latency.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use feature store caching with a larger cache size. — The best practice to reduce online serving latency for high-cardinality features in Vertex AI Feature Store is to enable caching with a larger cache size (Option D). Caching stores frequently accessed feature values in memory, reducing the need to query the BigQuery source for each prediction request. For high-cardinality features, a larger cache can hold more distinct values, minimizing cache misses. Option A (batch prediction) is not suitable for online serving. Option B (moving to Cloud Storage) does not integrate with Feature Store and may still have latency. Option C (increasing nodes) can improve throughput but does not directly address repeated access latency as effectively as caching.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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.
About these practice questions
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Last reviewed: Jun 24, 2026
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