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PMLE Practice Question: Monitoring a machine learning pipeline that runs…
You are monitoring a machine learning pipeline that runs on Vertex AI Pipelines. The pipeline occasionally fails with a 'ResourceExhausted' error when attempting to read data from BigQuery. Which action should you take to resolve this issue?
⚠ Common exam trap
Google Cloud often tests the misconception that memory or batch size adjustments in the pipeline environment can fix backend service quota errors, when in fact the error is specific to BigQuery's resource management (slots/queries) and requires query optimization or reservation changes.
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
✓
Reduce the complexity of the BigQuery query or increase the reservation size
The 'ResourceExhausted' error when reading from BigQuery indicates that the query is consuming more resources than the BigQuery reservation allows. Option C is correct because reducing query complexity (e.g., using fewer JOINs, aggregations, or partitions) or increasing the reservation size directly addresses the root cause by either lowering resource demand or allocating more capacity. Other options like switching to Cloud Storage or adjusting pipeline memory do not fix the BigQuery-specific quota or slot exhaustion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from BigQuery to Cloud Storage for data source
Why it's wrong here
ResourceExhausted from BigQuery reflects API quota or concurrent-slot limits, not storage location; moving to Cloud Storage abandons SQL and schema handling without addressing the quota. Cloud Storage is the right source for large unstructured files or when BigQuery quotas genuinely cannot be raised.
- ✗
Increase the memory allocated to the pipeline step
Why it's wrong here
ResourceExhausted originates from BigQuery's API quota and slot limits, not the pipeline step's RAM, so extra memory changes nothing. Raising step memory is correct when the failure is an out-of-memory kill inside the container, which surfaces as a different error than BigQuery quota exhaustion.
- ✓
Reduce the complexity of the BigQuery query or increase the reservation size
Why this is correct
ResourceExhausted signals BigQuery quota or slot limits being hit, not a pipeline bug. Simplifying the query reduces scanned bytes and slot demand, while enlarging the reservation raises available capacity, both directly relieving the exhausted resource.
- ✗
Reduce the batch size of the data being read
Why it's wrong here
Reducing batch size lowers per-request rows but leaves the underlying BigQuery API quota and concurrent-slot ceiling untouched, so the error recurs. Smaller batches are the right remedy when individual queries exceed BigQuery's maximum response size or when streaming inserts hit per-request limits.
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