- A
Mount an NFS volume to all containers and share data via the filesystem.
Why wrong: KFP does not support NFS mounts natively; also adds complexity and latency.
- B
Use Cloud Storage URIs (gs://) to point to the data location.
Using GCS URIs allows components to read/write data efficiently and supports caching.
- C
Serialize the dataset to JSON and include it as a pipeline parameter.
Why wrong: Large JSON parameters can cause memory issues and are not meant for large data.
- D
Use the importer component to load the data into the pipeline as an in-memory artifact.
Why wrong: Importer imports metadata but data is still stored in GCS; in-memory passing is not recommended for large datasets.
Question 873 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.
A machine learning engineer is building a pipeline with Vertex AI Pipelines and wants to pass a large dataset between components without copying it to the container's memory. What is the best practice for passing data between pipeline components?
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
✓
Use Cloud Storage URIs (gs://) to point to the data location.
Option B is correct because Vertex AI Pipelines natively supports passing Cloud Storage URIs (gs://) as artifact references between components, allowing components to read the dataset directly from GCS without copying it into container memory. This avoids memory limits and enables efficient handling of large datasets by leveraging GCS's scalable object storage.
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.
- ✗
Mount an NFS volume to all containers and share data via the filesystem.
Why it's wrong here
KFP does not support NFS mounts natively; also adds complexity and latency.
- ✓
Use Cloud Storage URIs (gs://) to point to the data location.
Why this is correct
Using GCS URIs allows components to read/write data efficiently and supports caching.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Serialize the dataset to JSON and include it as a pipeline parameter.
Why it's wrong here
Large JSON parameters can cause memory issues and are not meant for large data.
- ✗
Use the importer component to load the data into the pipeline as an in-memory artifact.
Why it's wrong here
Importer imports metadata but data is still stored in GCS; in-memory passing is not recommended for large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that large data must be passed as in-memory artifacts or serialized parameters, when the correct cloud-native pattern is to pass a storage URI and let components read the data lazily from object storage.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Pipelines uses the ML Metadata (MLMD) framework to track artifacts as typed metadata with URIs. When a component outputs a `Dataset` artifact with a `gs://` URI, downstream components receive that URI as an input, and the actual data is read on demand using the Google Cloud Storage client library (e.g., `google-cloud-storage`). This pattern is essential for datasets exceeding container memory (e.g., 100+ GB), as it avoids OOM errors and allows parallel processing via distributed reading.
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: Use Cloud Storage URIs (gs://) to point to the data location. — Option B is correct because Vertex AI Pipelines natively supports passing Cloud Storage URIs (gs://) as artifact references between components, allowing components to read the dataset directly from GCS without copying it into container memory. This avoids memory limits and enables efficient handling of large datasets by leveraging GCS's scalable object storage.
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.
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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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