- A
Store the data in BigQuery and pass the table reference.
Why wrong: This is not the standard pattern for artifact passing in KFP.
- B
Define output as a string and pass the GCS path manually.
Why wrong: While possible, using artifact types provides better tracking and lineage.
- C
Use the Dataset artifact type available in the KFP SDK for inputs and outputs.
Artifact types like Dataset are designed for passing data via GCS URIs.
- D
Use in-memory Python objects as function return values.
Why wrong: Passing data in-memory is not possible in distributed pipelines and violates best practices.
Question 810 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An ML engineer is building a pipeline on Vertex AI Pipelines and wants to pass a dataset artifact from one component to another without incurring additional cost for intermediate storage. How should they define the input and output types?
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
✓
Use the Dataset artifact type available in the KFP SDK for inputs and outputs.
Option C is correct because using the KFP SDK's Dataset artifact type allows Vertex AI Pipelines to manage the data as a lineage-tracked artifact, automatically handling the underlying GCS storage reference without incurring additional intermediate storage costs. The artifact type enables the pipeline to pass the metadata (URI, type, etc.) between components efficiently, leveraging the native artifact management of the KFP SDK.
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.
- ✗
Store the data in BigQuery and pass the table reference.
Why it's wrong here
This is not the standard pattern for artifact passing in KFP.
- ✗
Define output as a string and pass the GCS path manually.
Why it's wrong here
While possible, using artifact types provides better tracking and lineage.
- ✓
Use the Dataset artifact type available in the KFP SDK for inputs and outputs.
Why this is correct
Artifact types like Dataset are designed for passing data via GCS URIs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use in-memory Python objects as function return values.
Why it's wrong here
Passing data in-memory is not possible in distributed pipelines and violates best practices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'avoiding additional cost' with 'avoiding any storage at all,' leading them to choose in-memory passing (Option D) or manual string paths (Option B), not realizing that the KFP artifact system uses the same underlying GCS storage that is already part of the pipeline's infrastructure, thus incurring no extra cost.
Detailed technical explanation
How to think about this question
Under the hood, the KFP SDK's Dataset artifact type is a subclass of the Artifact class, which encapsulates a URI (typically a GCS path) and metadata. When a component outputs a Dataset artifact, the pipeline system automatically creates a GCS blob for the artifact's data, but the cost is minimal because the artifact is typically small metadata; the actual data can be stored in the same GCS bucket used by the pipeline, avoiding separate intermediate storage charges. In real-world scenarios, using artifact types also enables automatic lineage tracking, which is crucial for model governance and debugging in production pipelines.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the Dataset artifact type available in the KFP SDK for inputs and outputs. — Option C is correct because using the KFP SDK's Dataset artifact type allows Vertex AI Pipelines to manage the data as a lineage-tracked artifact, automatically handling the underlying GCS storage reference without incurring additional intermediate storage costs. The artifact type enables the pipeline to pass the metadata (URI, type, etc.) between components efficiently, leveraging the native artifact management of the KFP SDK.
What should I do if I get this PMLE 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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