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Serving and Scaling Models →mediumMultiple SelectObjective-mapped

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.

You are deploying a large deep learning model on Vertex AI endpoints. The model requires GPU acceleration and you want to minimize cold-start latency. Which TWO actions should you take? (Choose 2 correct answers)

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.

A
Set minReplicaCount to 0 to allow scale-to-zero.
Why wrong: Scale-to-zero increases cold-start latency when a request arrives.
B
Use a custom container that loads the model during startup.
Pre-loading the model reduces latency for the first prediction.
C
Increase maxReplicaCount to a high number.
Why wrong: Increasing max does not reduce cold-start latency; it only allows more replicas to be created.
D
Use batch prediction instead of online prediction.
Why wrong: Batch prediction is not real-time and does not address cold start for online serving.
E
Set minReplicaCount to 1 to always have at least one replica running.
Keeping one replica running reduces cold-start latency for the first request.

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

✓

Use a custom container that loads the model during startup.

Option B is correct because loading the model during container startup (e.g., in the Dockerfile's ENTRYPOINT or CMD) ensures that the model is already in memory when the first prediction request arrives, drastically reducing cold-start latency. This is a standard practice for Vertex AI endpoints where the container must be ready to serve immediately after scaling up.

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.

✗
Set minReplicaCount to 0 to allow scale-to-zero.
Why it's wrong here
Scale-to-zero increases cold-start latency when a request arrives.
✓
Use a custom container that loads the model during startup.
Why this is correct
Pre-loading the model reduces latency for the first prediction.
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.
✗
Increase maxReplicaCount to a high number.
Why it's wrong here
Increasing max does not reduce cold-start latency; it only allows more replicas to be created.
✗
Use batch prediction instead of online prediction.
Why it's wrong here
Batch prediction is not real-time and does not address cold start for online serving.
✓
Set minReplicaCount to 1 to always have at least one replica running.
Why this is correct
Keeping one replica running reduces cold-start latency for the first request.
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google often tests the misconception that scale-to-zero (minReplicaCount=0) reduces latency, when in fact it increases cold-start latency; the correct approach is to keep at least one replica always warm (minReplicaCount=1) and pre-load the model during container startup.

Detailed technical explanation

How to think about this question

Under the hood, Vertex AI endpoints use a container-based serving stack where the model is loaded into GPU memory. Cold-start latency includes container startup, model loading, and GPU initialization (e.g., CUDA context creation). By pre-loading the model in the startup script, you eliminate the model-loading phase from the first request path. In real-world scenarios, models like large transformers (e.g., BERT-large) can take 30–60 seconds to load from disk, so pre-loading is critical for sub-second response SLAs.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

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: Use a custom container that loads the model during startup. — Option B is correct because loading the model during container startup (e.g., in the Dockerfile's ENTRYPOINT or CMD) ensures that the model is already in memory when the first prediction request arrives, drastically reducing cold-start latency. This is a standard practice for Vertex AI endpoints where the container must be ready to serve immediately after scaling up.

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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