hardMultiple Choice
PMLE Practice Question: A logistics company uses Vertex AI AutoML Tables…
A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between a raw data source (BigQuery table) and a Vertex AI Dataset resource, trapping candidates who assume AutoML can directly consume a BigQuery table without the required metadata wrapper.
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 BigQuery output table is not being passed as a Vertex AI Dataset resource.
The error 'Dataset not found' occurs because AutoML Tables requires a Vertex AI Dataset resource (a metadata wrapper) to reference the training data, not just a BigQuery table. The pipeline's BigQuery query produces a table, but if that table is not explicitly converted into or passed as a Vertex AI Dataset resource (via the `aiplatform.Dataset` creation step), AutoML training cannot locate it. Option D correctly identifies this missing step as the root cause.
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 AutoML training step is referencing a different dataset location.
Why it's wrong here
The BigQuery query succeeded and wrote a table, so the AutoML step is reading a dataset reference that was never created or was deleted, not a different location. Referencing another location is tempting because path errors look similar, yet the error names a missing dataset.
- ✗
The training data has been manually deleted from Cloud Storage.
Why it's wrong here
The error names a dataset, not a Cloud Storage object; AutoML Tables reads the BigQuery table the query produced, so deleting GCS files would not trigger 'Dataset not found'. It is tempting because AutoML exports data to GCS, which would be the cause if the error referenced a missing bucket path.
- ✗
The pipeline's IAM permissions are insufficient to access BigQuery.
Why it's wrong here
Insufficient IAM permissions produce a permission-denied error, not 'Dataset not found'; the query step already succeeded, proving BigQuery access works. It is tempting because IAM is a common pipeline failure cause, which would be correct if the error were an access-denied message rather than a missing dataset.
- ✓
The BigQuery output table is not being passed as a Vertex AI Dataset resource.
Why this is correct
AutoML training requires a Vertex AI Dataset resource, not a raw BigQuery table reference. The pipeline's BigQuery step succeeds, but the training step cannot locate a registered dataset, producing 'Dataset not found'. The output table must first be imported into a Vertex AI Dataset before AutoML training can consume it.
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