PMLE Scaling Prototypes into ML Models Practice Question
An ML engineer is preparing to train a large model on Vertex AI using a custom training job. The training data is stored in a Cloud Storage bucket as a set of TFRecord files. The engineer wants to optimize the training job to reduce cost and improve performance. Which two actions should the engineer take? (Choose two.)
⚠ Common exam trap
The trap here is thinking that using a default service account or enabling Model Monitoring on training jobs are valid optimizations, when they are not.
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 TPU or GPU accelerator and ensure the input pipeline is optimized to keep the accelerator busy.
To optimize training cost and performance, the engineer should use accelerators and ensure the input pipeline is efficient, and store data in the same region as the training job. These actions reduce training time and avoid unnecessary network overhead. Other options like using broad service accounts or increasing steps do not contribute to efficiency and may introduce security or cost issues.
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 TPU or GPU accelerator and ensure the input pipeline is optimized to keep the accelerator busy.
Why this is correct
Accelerators like GPUs or TPUs can significantly speed up training for large models. However, they must be fed data efficiently; otherwise, utilization drops. Optimizing the input pipeline with tf.data, prefetching, and parallel reads ensures the accelerator is not idle. This combination reduces training time and cost, as you pay for the accelerator only while it is used effectively.
- ✗
Enable Vertex AI Model Monitoring on the training job to detect anomalies during training.
Why it's wrong here
Vertex AI Model Monitoring is for deployed models, not training jobs. It cannot be enabled on a training job. This option is irrelevant to optimizing training performance and cost. Model Monitoring is used for detecting drift in production predictions.
- ✓
Store the training data in a Cloud Storage bucket in the same region as the training job.
Why this is correct
Placing the data in the same region as the training job minimizes network latency and data egress costs. Cross-region data transfer can add significant latency and cost, especially for large datasets. Co-locating data and compute is a best practice for performance and cost optimization on Vertex AI.
- ✗
Use the default Compute Engine service account with broad permissions to simplify access to Cloud Storage.
Why it's wrong here
Using a default service account with broad permissions is a security risk and not a performance or cost optimization. It is better to create a least-privilege service account with only the necessary permissions. This option does not address training efficiency and could lead to security vulnerabilities.
- ✗
Increase the number of training steps to improve model accuracy, regardless of cost.
Why it's wrong here
Increasing training steps may improve accuracy but also increases cost and time. The goal is to optimize cost and performance, not to blindly increase steps. This action could lead to overfitting and higher costs without guaranteeing better results. It does not address efficiency.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.