PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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 data science team is operationalizing a batch prediction job using Vertex AI Batch Prediction. The model uses a custom container that requires a specific GPU for inference. The job processes a large dataset stored in Cloud Storage. The team wants to minimize cost while ensuring the job completes within a 2-hour window. Which configuration should they choose?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a custom training job with a GPU worker pool and run the inference as a custom job.
Option A is correct because Vertex AI Batch Prediction does not support custom containers with GPU accelerators; it only supports CPUs for batch prediction jobs. To run GPU-accelerated inference on a large dataset, the team must use a custom training job (which supports GPU worker pools) and run inference as a custom job. This approach allows them to leverage GPU hardware for the 2-hour window while minimizing cost by using preemptible VMs or choosing the smallest GPU instance that meets throughput requirements.
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 custom training job with a GPU worker pool and run the inference as a custom job.
Why this is correct
This approach allows GPU usage and is cost-effective for batch processing within a time window.
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 a custom machine type with a GPU accelerator in the batch prediction request.
Why it's wrong here
Vertex AI Batch Prediction does not support GPU accelerators.
✗
Use a high-memory machine type (e.g., n1-highmem-32) without GPU to reduce cost.
Why it's wrong here
The model requires a GPU, so CPU-only machines will not work.
✗
Configure a Vertex AI endpoint with GPU and submit batch requests to the endpoint.
Why it's wrong here
Batch prediction cannot use online prediction endpoints; it uses managed resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that Vertex AI Batch Prediction supports GPU accelerators because it is a managed service, but in reality, GPU support is only available for online prediction endpoints and custom training jobs, not for batch prediction.
Detailed technical explanation
How to think about this question
Vertex AI Batch Prediction internally uses a managed, serverless infrastructure that only supports CPU-based machine families (e.g., n1-standard, n1-highmem) and does not expose GPU options. In contrast, a custom training job can be configured with a GPU worker pool (e.g., using accelerator type NVIDIA_TESLA_T4) and can be run as a one-off job, allowing the team to use preemptible VMs for up to 80% cost reduction. The team must also ensure the custom container is built with the appropriate CUDA and cuDNN libraries to utilize the GPU during inference.
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.
Operationalizing machine learning models — This question tests Operationalizing machine learning 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 training job with a GPU worker pool and run the inference as a custom job. — Option A is correct because Vertex AI Batch Prediction does not support custom containers with GPU accelerators; it only supports CPUs for batch prediction jobs. To run GPU-accelerated inference on a large dataset, the team must use a custom training job (which supports GPU worker pools) and run inference as a custom job. This approach allows them to leverage GPU hardware for the 2-hour window while minimizing cost by using preemptible VMs or choosing the smallest GPU instance that meets throughput requirements.
What should I do if I get this PDE 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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Question Discussion
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