- A
Deploy the model on a larger machine type to handle duplicate requests faster.
Why wrong: Larger machines don't eliminate redundant computation; caching is more efficient.
- B
Enable Vertex AI endpoint caching by setting the `enable_cache` flag.
Why wrong: Vertex AI does not have a built-in cache flag; caching must be implemented externally.
- C
Implement a cache layer using Cloud Memorystore for Redis, hashing prediction requests.
Correct: Cloud Memorystore provides low-latency caching for identical requests.
- D
Use Cloud CDN in front of the endpoint.
Why wrong: Cloud CDN caches static content, not dynamic prediction responses.
Question 543 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.
An application serving predictions from a Vertex AI endpoint receives many identical requests within a short time window. The team notices redundant computation and wants to cache responses to reduce latency and cost. What is the recommended solution?
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 cache layer using Cloud Memorystore for Redis, hashing prediction requests.
Option C is correct because Vertex AI does not provide built-in request caching; instead, the recommended pattern is to implement an external cache like Cloud Memorystore for Redis. By hashing the prediction request payload and using it as a cache key, identical requests within the short time window can be served from Redis, eliminating redundant model inference and reducing both latency and cost.
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.
- ✗
Deploy the model on a larger machine type to handle duplicate requests faster.
Why it's wrong here
Larger machines don't eliminate redundant computation; caching is more efficient.
- ✗
Enable Vertex AI endpoint caching by setting the `enable_cache` flag.
Why it's wrong here
Vertex AI does not have a built-in cache flag; caching must be implemented externally.
- ✓
Implement a cache layer using Cloud Memorystore for Redis, hashing prediction requests.
Why this is correct
Correct: Cloud Memorystore provides low-latency caching for identical requests.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud CDN in front of the endpoint.
Why it's wrong here
Cloud CDN caches static content, not dynamic prediction responses.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Vertex AI has a native caching feature (like `enable_cache`) because other Google Cloud services (e.g., Cloud CDN, Cloud Load Balancing) offer caching, but Vertex AI endpoints require an external cache layer like Memorystore for Redis.
Detailed technical explanation
How to think about this question
Under the hood, a Redis cache for predictions works by using a hash of the serialized request (e.g., JSON payload) as the key and storing the model's response with a configurable TTL. This approach is especially effective for idempotent models (e.g., deterministic ML models) where the same input always yields the same output. A real-world scenario is a real-time fraud detection system receiving bursts of identical transaction checks; caching avoids repeated inference on the same data, reducing Vertex AI endpoint costs by up to 90% during peak load.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 cache layer using Cloud Memorystore for Redis, hashing prediction requests. — Option C is correct because Vertex AI does not provide built-in request caching; instead, the recommended pattern is to implement an external cache like Cloud Memorystore for Redis. By hashing the prediction request payload and using it as a cache key, identical requests within the short time window can be served from Redis, eliminating redundant model inference and reducing both latency and cost.
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.
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: 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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