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PDE Practice Question: A team uses Vertex AI Pipelines to automate…
A team uses Vertex AI Pipelines to automate retraining of a model every month. The pipeline includes data preprocessing, training, and deployment steps. After a recent update, the pipeline fails intermittently with a timeout error during the deployment step. What is the most likely cause?
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 trained model size has increased due to more data, causing the deployment step to time out
A model with increased size due to training on more data can cause the deployment step to time out if the deployment infrastructure has a timeout limit. Option A (insufficient permissions) would cause persistent errors, not intermittent. Option C (parallel step execution) typically causes resource contention, not specifically timeout on deployment. Option D (BigQuery quotas) would affect preprocessing, not the deployment step.
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 service account used by the pipeline lacks permissions to deploy the model
Why it's wrong here
Permission issues would cause persistent failures, not intermittent.
- ✓
The trained model size has increased due to more data, causing the deployment step to time out
Why this is correct
Larger models take longer to upload and deploy, potentially exceeding timeout limits.
- ✗
The pipeline is configured to run steps in parallel, leading to resource contention
Why it's wrong here
Parallel execution would not cause timeout specifically in deployment.
- ✗
BigQuery query quotas are being exceeded during data preprocessing
Why it's wrong here
Quota errors would cause failures earlier in the pipeline, not during deployment.
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