PMLE Scaling Prototypes into ML Models Practice Question
A team is training a TensorFlow model on Vertex AI using a custom container. The training script writes checkpoints to a local directory inside the container. The job runs for 14 hours, and when it completes, the team cannot find the checkpoints in Cloud Storage. They need the checkpoints to be persisted so they can resume training and deploy the best model. What should they do?
⚠ Common exam trap
The trap here is assuming that Vertex AI automatically uploads local training artifacts to Cloud Storage, when only files written to a gs:// path are persisted.
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
✓
Configure the training job to write checkpoints to a Cloud Storage URI by passing a gs:// path to the checkpoint directory in the training script, and ensure the Vertex AI service account has storage.objectAdmin on the bucket.
Checkpoints must be written to a durable location, and in Vertex AI custom training the durable location is Cloud Storage. Passing a gs:// URI to the checkpoint directory makes TensorFlow persist each checkpoint through its GCS filesystem, and granting storage.objectAdmin to the training service account authorizes those writes. This allows both resuming training and retrieving the best model for deployment.
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 TensorBoard integration and configure the training script to log checkpoints as TensorBoard artifacts, then retrieve them from the TensorBoard instance.
Why it's wrong here
Vertex AI TensorBoard stores time-series metrics and optional profiling data, not model checkpoint files. Logging a checkpoint as a TensorBoard artifact would at best store a reference or a small summary, not the full weights needed to resume training or deploy. TensorBoard integration is for observability, so it does not satisfy the requirement to persist and reuse checkpoints.
- ✗
Set the training job's base output directory to a local path and rely on Vertex AI to automatically upload everything under that path to the job's Cloud Storage output directory at the end of training.
Why it's wrong here
Vertex AI does not automatically upload arbitrary local files from the training container to Cloud Storage at job completion. Only artifacts the script explicitly writes to a gs:// location are persisted. Relying on an implicit upload of a local path is a misunderstanding of the custom training contract, and the checkpoints would still be lost when the VM is torn down.
- ✗
Increase the boot disk size of the training VM and re-run the job, then copy the checkpoints from the boot disk to Cloud Storage after training finishes.
Why it's wrong here
The boot disk of a Vertex AI custom training VM is ephemeral and is deleted when the job completes or the VM is reclaimed. Increasing its size only postpones the loss; there is no post-training window in which the disk can be accessed. This option misunderstands the lifecycle of the training VM and does not provide a durable storage location for the checkpoints.
- ✓
Configure the training job to write checkpoints to a Cloud Storage URI by passing a gs:// path to the checkpoint directory in the training script, and ensure the Vertex AI service account has storage.objectAdmin on the bucket.
Why this is correct
Writing checkpoints directly to a gs:// URI makes TensorFlow use its GCS filesystem implementation, so checkpoint files are persisted in Cloud Storage as they are written. The Vertex AI custom training service account must have permission to write to the bucket, which storage.objectAdmin grants. This is the standard pattern for durable checkpoints in Vertex AI training and directly solves the missing-checkpoint problem.
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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.