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PDE Practice Question: A retail company uses Vertex AI Pipelines to…
A retail company uses Vertex AI Pipelines to automate monthly retraining of a recommendation model. The pipeline consists of three steps: (1) extract data from BigQuery, (2) train a TensorFlow model on Vertex AI Training, (3) upload the model to Vertex AI Model Registry and deploy to an endpoint if performance metrics improve. Recently, the pipeline has been failing at step 2 with the error: 'The job was cancelled by the system because it exceeded the maximum training time of 3600 seconds.' You have confirmed that the training code is correct and the data size has not changed significantly. What should you do to fix this pipeline failure? A) Reconfigure the pipeline to use a larger machine type for training. B) Set the training timeout to 7200 seconds in the pipeline configuration. C) Reduce the training dataset size by sampling fewer rows. D) Switch from TensorFlow to a simpler model framework.
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
✓
Set the training timeout to 7200 seconds in the pipeline configuration.
The default timeout for a training job in Vertex AI Pipelines is 3600 seconds; increasing the timeout allows the job to complete. Option A (larger machine) may help but is not a direct fix for timeout. Option C (reducing data) degrades model quality. Option D (changing framework) is drastic and unnecessary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the training dataset size by sampling fewer rows.
Why it's wrong here
Reducing data could harm model performance and is not a robust solution.
- ✓
Set the training timeout to 7200 seconds in the pipeline configuration.
Why this is correct
Increasing the timeout accommodates the training duration within the expected limits.
- ✗
Switch from TensorFlow to a simpler model framework.
Why it's wrong here
Changing framework is a major change and may introduce new issues.
- ✗
Reconfigure the pipeline to use a larger machine type for training.
Why it's wrong here
A larger machine might train faster but the root cause is the timeout limit; even if faster, it's not guaranteed.
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