- A
Use a regional load balancer with session affinity.
Why wrong: Session affinity does not cache responses; it routes requests from the same client to the same backend instance.
- B
Implement a caching layer using Cloud Memorystore with request hashing.
Cloud Memorystore (Redis) can cache prediction results keyed by a hash of the input request, reducing latency for duplicate requests.
- C
Use Cloud CDN to cache prediction responses.
Why wrong: Cloud CDN is for static content caching, not API response caching based on request body.
- D
Enable prediction caching on Vertex AI Endpoints.
Why wrong: Vertex AI Endpoints does not have a built-in prediction caching feature.
Question 72 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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 deploys a model on Vertex AI Endpoints for real-time inference. They need to minimize latency for prediction requests that are identical to previous requests. Which approach should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Implement a caching layer using Cloud Memorystore with request hashing.
Option B is correct because caching identical prediction requests using Cloud Memorystore with request hashing reduces latency by serving cached responses directly from an in-memory cache, avoiding redundant model inference. This approach is ideal for real-time inference where many requests are identical, as it bypasses the model endpoint entirely for cached requests, minimizing response time.
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 a regional load balancer with session affinity.
Why it's wrong here
Session affinity does not cache responses; it routes requests from the same client to the same backend instance.
- ✓
Implement a caching layer using Cloud Memorystore with request hashing.
Why this is correct
Cloud Memorystore (Redis) can cache prediction results keyed by a hash of the input request, reducing latency for duplicate requests.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud CDN to cache prediction responses.
Why it's wrong here
Cloud CDN is for static content caching, not API response caching based on request body.
- ✗
Enable prediction caching on Vertex AI Endpoints.
Why it's wrong here
Vertex AI Endpoints does not have a built-in prediction caching feature.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Vertex AI's built-in features with external caching mechanisms, assuming 'prediction caching' is a native endpoint option when it is not, leading them to select option D.
Detailed technical explanation
How to think about this question
Cloud Memorystore provides a managed Redis or Memcached service that can store key-value pairs where the key is a hash of the request payload and the value is the prediction response. This approach leverages in-memory data stores for sub-millisecond access times, significantly reducing latency compared to model inference, which may take tens to hundreds of milliseconds. In practice, you must also handle cache invalidation (e.g., TTL-based expiry) to ensure stale predictions are not served when model versions change.
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.
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 PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a caching layer using Cloud Memorystore with request hashing. — Option B is correct because caching identical prediction requests using Cloud Memorystore with request hashing reduces latency by serving cached responses directly from an in-memory cache, avoiding redundant model inference. This approach is ideal for real-time inference where many requests are identical, as it bypasses the model endpoint entirely for cached requests, minimizing response time.
What should I do if I get this PMLE 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This PMLE 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 PMLE exam.
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