PMLE Collaborating to manage data and models Practice Question
An ML team uses Vertex AI Pipelines to automate model retraining. The pipeline includes a step that queries BigQuery to create a training dataset. The team notices that the pipeline fails intermittently with a '403 Exceeded rate limits' error. What is the most likely cause and solution?
⚠ Common exam trap
Test-takers frequently confuse rate-limit errors with performance or timeout issues, and they choose options that optimize query cost or size (B, D) or adjust timeouts (C), instead of recognizing that a 403 error specifically points to a quota or rate-limit violation that requires resource allocation like a reservation.
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
✓
The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity
The 403 'Exceeded rate limits' error in BigQuery indicates that the project is hitting the concurrent query rate limit or the rate of bytes read per second. Using a BigQuery reservation guarantees dedicated slot capacity, which prevents rate-limit errors by ensuring the pipeline has consistent compute resources regardless of other workloads in the project. This is the most direct solution because rate limits are enforced at the project level based on available slots, and a reservation provides a fixed number of slots that bypass those limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity
Why this is correct
Reservations provide dedicated slots, avoiding API rate limits.
- ✗
The training dataset is too large; partition the table and query only the latest partition
Why it's wrong here
The error is about rate limits, not data size.
- ✗
The pipeline step timeout is too short; increase the timeout to 30 minutes
Why it's wrong here
Timeout increase does not resolve rate limit errors.
- ✗
The SQL query is inefficient; rewrite it using materialized views
Why it's wrong here
Inefficient queries cause timeout, not rate limits.
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