- A
Use global variables to share state between components.
Why wrong: Global variables lead to non-determinism and break idempotency.
- B
Pass data through Cloud Storage URIs rather than in-memory.
GCS URIs make components idempotent and enable caching.
- C
Write component outputs to a database with timestamps.
Why wrong: Timestamps cause non-determinism.
- D
Use the same output name for all runs to avoid duplication.
Why wrong: Output names should be unique per run.
Question 383 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.
Which of the following is a best practice when designing idempotent pipeline components in Vertex AI?
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 data through Cloud Storage URIs rather than in-memory.
Passing data through Cloud Storage URIs ensures that component outputs are stored persistently and can be retrieved by downstream components, even if the original component instance is terminated or scaled down. This aligns with the principle of idempotency because the same input will always produce the same output stored at the same URI, and re-running the component will not cause side effects or data loss. In contrast, in-memory data is ephemeral and tied to a specific runtime instance, breaking idempotency across retries or parallel executions.
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 global variables to share state between components.
Why it's wrong here
Global variables lead to non-determinism and break idempotency.
- ✓
Pass data through Cloud Storage URIs rather than in-memory.
Why this is correct
GCS URIs make components idempotent and enable 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.
- ✗
Write component outputs to a database with timestamps.
Why it's wrong here
Timestamps cause non-determinism.
- ✗
Use the same output name for all runs to avoid duplication.
Why it's wrong here
Output names should be unique per run.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google PMLE exam often tests the misconception that idempotency is about avoiding duplication of output names or using timestamps for uniqueness, when in fact idempotency requires that repeated executions produce the same result without side effects, which is achieved by using immutable, deterministic storage like Cloud Storage URIs rather than mutable state or time-dependent writes.
Trap categories for this question
Command / output trap
Output names should be unique per run.
Detailed technical explanation
How to think about this question
In Vertex AI Pipelines, components are containerized and run as Kubernetes pods; passing data via Cloud Storage URIs leverages object storage immutability and versioning, ensuring that if a component is retried (e.g., due to a transient failure), it reads the same input and writes to the same output path, producing identical results. This pattern also supports caching: the pipeline service can skip re-execution if the input and output URIs match a previous run, reducing cost and time. Under the hood, the Kubeflow Pipelines SDK serializes artifacts as URIs, and the pipeline DAG resolves dependencies by checking for the existence of those URIs, enforcing idempotency at the orchestration layer.
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 data through Cloud Storage URIs rather than in-memory. — Passing data through Cloud Storage URIs ensures that component outputs are stored persistently and can be retrieved by downstream components, even if the original component instance is terminated or scaled down. This aligns with the principle of idempotency because the same input will always produce the same output stored at the same URI, and re-running the component will not cause side effects or data loss. In contrast, in-memory data is ephemeral and tied to a specific runtime instance, breaking idempotency across retries or parallel executions.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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