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Question 825 of 1,000

Serving and Scaling Models →hardMultiple SelectObjective-mapped

PMLE Serving and Scaling Models Practice Question

This PMLE practice question tests your understanding of serving and scaling models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

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 min_replicas = 0 to allow scale-to-zero and save costs.
Why wrong: Scale-to-zero increases cold-start latency, violating the SLO.
B
Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.
GPUs can reduce inference latency for deep learning models.
C
Set min_replicas = 3 to keep a baseline of warm instances.
Warm replicas reduce cold starts during traffic spikes.
D
Enable Vertex AI Model Optimization for automatic quantization.
Why wrong: Quantization may reduce accuracy and is not primarily for latency SLO under variable load; hardware choice and instance count are more direct.
E
Use batch prediction instead of online prediction.
Why wrong: Batch prediction is not real-time and introduces latency.

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 GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.

Option B is correct because GPU-enabled machine types (e.g., N1 with T4) significantly accelerate TensorFlow model inference, which is critical for meeting the p99 < 100ms latency SLO. GPUs parallelize matrix operations common in deep learning models, reducing per-request latency even under variable traffic spikes.

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 min_replicas = 0 to allow scale-to-zero and save costs.
Why it's wrong here
Scale-to-zero increases cold-start latency, violating the SLO.
✓
Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.
Why this is correct
GPUs can reduce inference latency for deep learning models.
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.
✓
Set min_replicas = 3 to keep a baseline of warm instances.
Why this is correct
Warm replicas reduce cold starts during traffic spikes.
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.
✗
Enable Vertex AI Model Optimization for automatic quantization.
Why it's wrong here
Quantization may reduce accuracy and is not primarily for latency SLO under variable load; hardware choice and instance count are more direct.
✗
Use batch prediction instead of online prediction.
Why it's wrong here
Batch prediction is not real-time and introduces latency.

Common exam traps

Common exam trap: answer the scenario, not the keyword

A common misconception is that scale-to-zero (min_replicas = 0) is always cost-effective, but in latency-sensitive real-time inference, it introduces unacceptable cold-start delays, making baseline warm instances (min_replicas > 0) essential.

Detailed technical explanation

How to think about this question

Under the hood, GPU inference leverages CUDA cores to execute TensorFlow operations in parallel, reducing per-request latency by up to 10x compared to CPU-only inference for deep learning models. Setting min_replicas = 3 ensures that Vertex AI Prediction maintains a pool of warm instances, avoiding the cold-start penalty of loading the model into memory and initializing the GPU runtime, which can take several seconds. In real-world scenarios, a fintech company might combine GPU inference with autoscaling (e.g., using min_replicas = 3 and max_replicas = 10) to handle spikes while keeping p99 latency under 100ms.

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: Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference. — Option B is correct because GPU-enabled machine types (e.g., N1 with T4) significantly accelerate TensorFlow model inference, which is critical for meeting the p99 < 100ms latency SLO. GPUs parallelize matrix operations common in deep learning models, reducing per-request latency even under variable traffic spikes.

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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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.