- A
Use the component's temporary directory to share data between containers.
Why wrong: Containers do not share filesystems; data must be externalized.
- B
Pass the data as a serialized Python object in memory.
Why wrong: In-memory passing is limited by container memory and not suitable for large datasets.
- C
Write the data to Cloud Storage and pass the GCS URI as an artifact.
This is the recommended best practice for large data in KFP pipelines.
- D
Store the data in a BigQuery table and pass the table reference.
Why wrong: BigQuery is for analytics, not as a general intermediate data store for pipelines.
Question 324 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.
You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Write the data to Cloud Storage and pass the GCS URI as an artifact.
In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.
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.
- ✗
Use the component's temporary directory to share data between containers.
Why it's wrong here
Containers do not share filesystems; data must be externalized.
- ✗
Pass the data as a serialized Python object in memory.
Why it's wrong here
In-memory passing is limited by container memory and not suitable for large datasets.
- ✓
Write the data to Cloud Storage and pass the GCS URI as an artifact.
Why this is correct
This is the recommended best practice for large data in KFP pipelines.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Store the data in a BigQuery table and pass the table reference.
Why it's wrong here
BigQuery is for analytics, not as a general intermediate data store for pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that temporary directories are shared between containers in a pod, but in KFP each component runs in its own container with isolated storage, making Cloud Storage the correct choice for durable, cross-component data sharing.
Detailed technical explanation
How to think about this question
Under the hood, KFP SDK v2 uses the `dsl.Artifact` or `dsl.Input[Dataset]` annotation to automatically upload the data to a GCS bucket managed by the pipeline, and the URI is passed as a string parameter. The artifact is versioned and can be reused across pipeline runs, enabling lineage tracking and caching. In real-world scenarios, this pattern is critical when processing terabytes of image or text data, where in-memory or local disk approaches would cause out-of-memory errors or container restarts.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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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: Write the data to Cloud Storage and pass the GCS URI as an artifact. — In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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