PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team uses Vertex AI Pipelines with a custom training component that reads data from a BigQuery table. They need to ensure that a new pipeline run uses a specific snapshot of the training data for reproducibility. Which approach should they take?
⚠ Common exam trap
The trap here is relying on time travel or filtered queries for reproducibility, which are not durable or immutable beyond their retention limits.
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
✓
Create a BigQuery table snapshot of the training data and pass the snapshot name to the training component as an input artifact.
To ensure reproducibility, the training data must be immutable. A BigQuery table snapshot provides a point-in-time copy that does not change even if the source table is updated. Passing the snapshot name to the training component as an input artifact also integrates with Vertex AI Pipelines lineage tracking, making the data reference explicit and stable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a BigQuery table snapshot of the training data and pass the snapshot name to the training component as an input artifact.
Why this is correct
A BigQuery table snapshot is an immutable, point-in-time copy of the table that persists even if the source table changes. By creating a snapshot and passing its name to the training component, you ensure the pipeline run always reads the same data. This provides reproducibility and aligns with Vertex AI Pipelines artifact tracking, as the snapshot can be recorded as an input artifact.
- ✗
Pass the BigQuery table name and a WHERE clause that filters on a timestamp column to the training component, and record the query in the pipeline parameters.
Why it's wrong here
Filtering by a timestamp column does not guarantee an immutable snapshot because the underlying table can still be updated or backfilled, changing the query results. Recording the query helps document the intent but does not freeze the data. For reproducibility, you need a stable, versioned data reference, such as a BigQuery table snapshot or a time-travel query with a fixed point in time.
- ✗
Export the BigQuery table to a Cloud Storage bucket in CSV format and pass the Cloud Storage URI to the training component.
Why it's wrong here
Exporting to Cloud Storage creates a copy, but CSV exports can be modified or overwritten, and they lack the managed immutability of a BigQuery snapshot. Without versioning or retention policies, the file could change. A BigQuery snapshot provides a more controlled, point-in-time reference that is natively supported and easier to track as an artifact.
- ✗
Use BigQuery's time travel feature by adding FOR SYSTEM_TIME AS OF a fixed timestamp to the query in the training component.
Why it's wrong here
BigQuery time travel queries are read-only and depend on the table's time travel window, which defaults to seven days. If the pipeline runs after that window, the data is no longer available. Time travel is useful for short-term recovery but not for long-term reproducibility. A table snapshot persists until deleted and is not subject to the same expiration.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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