- A
Pass large datasets between components using GCS URIs instead of in-memory values.
GCS URIs allow for scalable, cacheable data passing.
- B
Avoid hard-coding file paths; use pipeline parameters to pass URIs.
Using parameters for URIs makes components reusable and portable.
- C
Read data into memory in the first component and pass the in-memory object to subsequent components.
Why wrong: In-memory data cannot be passed between pipeline steps; data must be serialized (e.g., via GCS).
- D
Use global variables in the pipeline code to store intermediate results.
Why wrong: Global variables can interfere with serialization and caching; it's better to pass data via component outputs.
- E
Design components to be idempotent so that the same input always produces the same output.
Idempotency is crucial for reliable pipeline reruns and caching.
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 company is using Vertex AI Pipelines for ML workflows. They want to implement best practices for idempotent components and data passing. Which THREE practices should they adopt?
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
Pass large datasets between components using GCS URIs instead of in-memory values.
Option A is correct because Vertex AI Pipelines components run in isolated containers; passing large datasets in-memory would exceed memory limits and cause failures. Using GCS URIs allows components to read/write data directly from Cloud Storage, which is the recommended pattern for handling large artifacts in Kubeflow Pipelines (the underlying orchestrator). This approach also enables caching and parallel execution since components only depend on the URI, not on the state of previous containers.
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.
- ✓
Pass large datasets between components using GCS URIs instead of in-memory values.
Why this is correct
GCS URIs allow for scalable, cacheable data passing.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Avoid hard-coding file paths; use pipeline parameters to pass URIs.
Why this is correct
Using parameters for URIs makes components reusable and portable.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Read data into memory in the first component and pass the in-memory object to subsequent components.
Why it's wrong here
In-memory data cannot be passed between pipeline steps; data must be serialized (e.g., via GCS).
- ✗
Use global variables in the pipeline code to store intermediate results.
Why it's wrong here
Global variables can interfere with serialization and caching; it's better to pass data via component outputs.
- ✓
Design components to be idempotent so that the same input always produces the same output.
Why this is correct
Idempotency is crucial for reliable pipeline reruns and 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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that in-memory data passing is acceptable in containerized pipelines, but the correct pattern is to use persistent storage (GCS) and artifact URIs to ensure idempotency and scalability.
Trap categories for this question
Command / output trap
Global variables can interfere with serialization and caching; it's better to pass data via component outputs.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines is built on Kubeflow Pipelines (KFP) v2, which uses a 'compiler' to convert Python functions into pipeline definitions. Under the hood, each component is a container image that reads inputs from GCS URIs and writes outputs to GCS URIs, enabling artifact lineage tracking and automatic caching. A subtle behavior: if a component is marked as idempotent (option E), the pipeline can skip re-execution if the same inputs produce the same output hash, saving cost and time—this is critical for retraining pipelines where data hasn't changed.
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.
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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: Pass large datasets between components using GCS URIs instead of in-memory values. — Option A is correct because Vertex AI Pipelines components run in isolated containers; passing large datasets in-memory would exceed memory limits and cause failures. Using GCS URIs allows components to read/write data directly from Cloud Storage, which is the recommended pattern for handling large artifacts in Kubeflow Pipelines (the underlying orchestrator). This approach also enables caching and parallel execution since components only depend on the URI, not on the state of previous containers.
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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