- A
Use Vertex AI online prediction with a load balancer in front to distribute requests.
Why wrong: Online prediction is for real-time, not batch, and would be cost-inefficient.
- B
Use Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput.
Batch prediction jobs handle resource scaling automatically; choosing appropriate machine type and batch size ensures performance.
- C
Create a Dataflow pipeline to read from BigQuery and write predictions to GCS, using the trained model as a side input.
Why wrong: Dataflow is not required; Vertex AI batch prediction natively supports BigQuery sources.
- D
Deploy the model to an endpoint with min_replicas=0 and max_replicas=10, then send batch requests to the endpoint.
Why wrong: Batch prediction does not use endpoints; it runs as a separate job.
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 company runs batch predictions on Vertex AI every hour using a custom container. They want to reduce costs by minimizing idle time while ensuring the batch job completes within 10 minutes. Which endpoint configuration should they use?
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 Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput.
Option B is correct because Vertex AI batch prediction jobs are designed for high-throughput, asynchronous processing of large datasets without maintaining persistent infrastructure. By tuning `machine_type` and `batch_size`, you can minimize idle time and ensure the job completes within the 10-minute window, as the job only runs while actively processing and scales resources as needed.
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 Vertex AI online prediction with a load balancer in front to distribute requests.
Why it's wrong here
Online prediction is for real-time, not batch, and would be cost-inefficient.
- ✓
Use Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput.
Why this is correct
Batch prediction jobs handle resource scaling automatically; choosing appropriate machine type and batch size ensures performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a Dataflow pipeline to read from BigQuery and write predictions to GCS, using the trained model as a side input.
Why it's wrong here
Dataflow is not required; Vertex AI batch prediction natively supports BigQuery sources.
- ✗
Deploy the model to an endpoint with min_replicas=0 and max_replicas=10, then send batch requests to the endpoint.
Why it's wrong here
Batch prediction does not use endpoints; it runs as a separate job.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse online prediction with autoscaling (min_replicas=0) as a cost-saving measure for batch workloads, but online prediction endpoints still incur a minimum charge for the underlying infrastructure and are not optimized for asynchronous batch jobs.
Detailed technical explanation
How to think about this question
Vertex AI batch prediction jobs automatically spin up compute resources only for the duration of the job, then tear them down, which directly minimizes idle time. The `batch_size` parameter controls how many records are sent per model request, allowing you to balance throughput and latency; for example, a larger batch size reduces the number of API calls but increases memory usage, and tuning this alongside `machine_type` (e.g., n1-standard-4) can optimize cost and speed for a 10-minute SLA. Under the hood, batch prediction uses distributed processing across multiple workers, and the job's lifecycle is managed by Vertex AI's job scheduler, which allocates resources from a shared pool.
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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Serving and Scaling Models — study guide chapter
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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 Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput. — Option B is correct because Vertex AI batch prediction jobs are designed for high-throughput, asynchronous processing of large datasets without maintaining persistent infrastructure. By tuning `machine_type` and `batch_size`, you can minimize idle time and ensure the job completes within the 10-minute window, as the job only runs while actively processing and scales resources as needed.
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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