Quick Answer
The answer is Cloud Storage. This is the correct choice because Vertex AI Training workers require shared, concurrent read access to the training dataset without manual replication, and Cloud Storage provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or the GCS connector, eliminating data duplication and ensuring consistency across the cluster. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how to handle distributed training data sharing efficiently, often appearing as a trap where candidates might consider local disks or a database instead—remember that Cloud Storage is the only fully integrated solution for parallel reads at scale. A useful memory tip: think of GCS as the "single source of truth" for all workers, avoiding the bottleneck of copying data to each node.
This PMLE practice question tests your understanding of collaborating to manage data and models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?
Copy the dataset to each worker's local disk
Why wrong: Copying to local disk is inefficient and not scalable.
Use NFS
Why wrong: NFS is not a managed service and can introduce bottlenecks.
Use Cloud Storage
Cloud Storage provides scalable, shared access to training data.
Use Google Drive
Why wrong: Google Drive is not supported for Vertex AI Training.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Cloud Storage
Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Copy the dataset to each worker's local disk
Why it's wrong here
Copying to local disk is inefficient and not scalable.
Use NFS
Why it's wrong here
NFS is not a managed service and can introduce bottlenecks.
Use Cloud Storage
Why this is correct
Cloud Storage provides scalable, shared access to training data.
Related concept
Read the scenario before looking for a memorised answer.
Use Google Drive
Why it's wrong here
Google Drive is not supported for Vertex AI Training.
Common exam traps
The trap here is that candidates confuse 'shared storage' with 'local copies' or 'user-friendly sync tools,' assuming NFS or Drive are viable for distributed ML, when Vertex AI explicitly requires a cloud-native object store like GCS for scalability and fault tolerance.
Detailed technical explanation
Under the hood, Cloud Storage uses a strongly consistent, flat namespace with object versioning, enabling all workers to read the same data without locking. The GCS connector (gcsfuse or the TensorFlow GCS filesystem plugin) handles chunked, parallel reads and retries, which is critical for large datasets like TFRecords or Parquet files. In a real-world scenario, using GCS allows you to scale to hundreds of workers without data bottlenecks, while NFS would saturate a single network path and local copies would fail on preemption.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Collaborating to manage data and models — This question tests Collaborating to manage data and models — Read the scenario before looking for a memorised answer..
The correct answer is: Use Cloud Storage — Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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