PMLE Scaling Prototypes into ML Models Practice Question
An ML engineer is using Vertex AI Training to fine-tune a large image classification model on a dataset stored in Cloud Storage. The training job uses a custom container and runs on a single NVIDIA V100 GPU. The engineer notices that GPU utilization is consistently low (around 20%) and training is slow. The data is stored as many small JPEG files. What should the engineer do to improve GPU utilization and training speed?
⚠ Common exam trap
The trap here is attributing low GPU utilization to insufficient GPU power or needing hyperparameter tuning, rather than diagnosing the input pipeline bottleneck.
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
✓
Convert the dataset to TFRecord format and use the tf.data API with parallel interleaving and prefetching.
Low GPU utilization during training often indicates that the GPU is starved for data. When data is stored as many small files, the I/O overhead dominates. Converting to TFRecord, a binary format optimized for TensorFlow, and using tf.data with parallel reads and prefetching can dramatically speed up data loading. This ensures the GPU receives data as fast as it can process it, improving utilization and reducing training time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Vertex AI Vizier to automatically tune hyperparameters and improve training speed.
Why it's wrong here
Vertex AI Vizier is a hyperparameter tuning service that optimizes model performance metrics, not training throughput. It cannot fix a data input bottleneck. Using Vizier would add overhead and complexity without addressing the low GPU utilization caused by slow data reads. Hyperparameter tuning is orthogonal to input pipeline efficiency.
- ✗
Use a larger GPU instance with more memory, such as an NVIDIA A100.
Why it's wrong here
A more powerful GPU will not help if the GPU is idle waiting for data. The bottleneck is the data input pipeline, not GPU compute capacity. Upgrading the GPU would increase cost without resolving the low utilization. The engineer should first optimize data loading before considering hardware changes.
- ✓
Convert the dataset to TFRecord format and use the tf.data API with parallel interleaving and prefetching.
Why this is correct
TFRecord is a binary format that stores data efficiently and allows sequential reads, which is much faster than reading many small JPEG files individually. Using tf.data with interleave, map with num_parallel_calls, and prefetch overlaps data loading and preprocessing with GPU computation. This directly addresses the input pipeline bottleneck, leading to higher GPU utilization and faster training.
- ✗
Increase the batch size to the maximum that fits in GPU memory.
Why it's wrong here
Increasing the batch size can improve GPU utilization to some extent, but if the bottleneck is data loading, a larger batch may not help and could even worsen the problem by increasing memory pressure. The root cause is likely the slow reading of many small files, not insufficient batch size. Without addressing data input, GPU utilization may remain low.
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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.