The correct answer is to increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration. This directly addresses the root cause of the Vertex AI pipelines resource exhaustion, as the logs confirmed the Dataflow job required more resources than the default worker configuration provided. By provisioning a custom machine type with additional vCPUs and memory, the feature engineering step can handle the workload without hitting transient resource limits, which pipeline retries alone cannot fix. On the Google Professional Data Engineer exam, this scenario tests your ability to differentiate between scaling compute resources versus adjusting retry logic or parallelism—a common trap where candidates mistakenly increase retries or change Dataflow worker count instead of upgrading the machine type. Remember the memory tip: when a pipeline fails with “transient error” at a resource-intensive step, think “up, not out”—scale the machine type vertically, not the worker count horizontally.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from BigQuery, feature engineering using Dataflow, and model training using a custom container on Vertex AI Training. Recently, the pipeline has been failing intermittently at the Dataflow step with a 'The job encountered a transient error. Please retry.' message. You have enabled pipeline retries with 3 attempts. However, the pipeline still fails after 3 retries. You check the logs and find that the Dataflow job requires more resources than the default worker configuration provides. Which change should you make to reduce the failure rate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration
Option D is correct because the pipeline fails due to insufficient resources (memory and CPU) in the default Dataflow worker configuration. By increasing the worker machine type (e.g., using a custom machine type with more vCPUs and memory), the Dataflow job can handle the feature engineering workload without hitting resource limits, reducing transient failures. This directly addresses the root cause identified in the logs, unlike retries or parallelism changes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the number of Dataflow workers to improve parallelism
Why it's wrong here
More workers may increase resource contention.
✗
Increase the number of retries in the pipeline to 5
Why it's wrong here
Retries don't fix the underlying resource issue.
✗
Replace Dataflow with Dataproc to run the feature engineering step
Why it's wrong here
This is a major change and may not resolve the resource issue.
✓
Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration
Why this is correct
More resources prevent the transient resource exhaustion errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing parallelism (more workers) or retries will fix resource exhaustion errors, when the actual fix is to increase per-worker resources by selecting a larger machine type.
Detailed technical explanation
How to think about this question
Dataflow uses autoscaling to adjust the number of workers, but each worker's machine type (e.g., n1-standard-4) defines fixed CPU and memory limits. When a pipeline step requires more memory than available per worker, the job may fail with transient errors due to out-of-memory (OOM) kills or disk spills. In Vertex AI Pipelines, you can configure the Dataflow step's worker machine type via the `machine_type` parameter in the pipeline component definition, allowing you to select high-memory machine series (e.g., n1-highmem-8) to match the workload's resource profile.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration — Option D is correct because the pipeline fails due to insufficient resources (memory and CPU) in the default Dataflow worker configuration. By increasing the worker machine type (e.g., using a custom machine type with more vCPUs and memory), the Dataflow job can handle the feature engineering workload without hitting resource limits, reducing transient failures. This directly addresses the root cause identified in the logs, unlike retries or parallelism changes.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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