Question 8 of 1,000
Automating and Orchestrating ML PipelineshardMultiple SelectObjective-mapped

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 tasked with building a robust ML pipeline that must be idempotent and handle data skew between training and serving. Which three practices should you implement?

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

Store intermediate data in Cloud Storage with unique run IDs.

Idempotent components ensure the same inputs produce the same outputs. Passing data via GCS URIs is a best practice. Skew detection should compare training data distribution with serving data. Using unique run IDs for outputs ensures idempotency. Avoiding in-memory data passing is important for large datasets.

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 intermediate data in Cloud Storage with unique run IDs.

    Why this is correct

    Unique paths prevent collisions and support idempotency.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Pass large datasets between components as serialized in-memory objects.

    Why it's wrong here

    In-memory passing is not recommended; use GCS URIs.

  • Monitor feature distributions in training data vs. serving data to detect skew.

    Why this is correct

    Skew detection is critical for maintaining model performance.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Use the same random seed for every run to ensure reproducibility.

    Why it's wrong here

    Reproducibility is good, but not specifically for skew detection; also, random seeds may not be the primary method.

  • Ensure each component produces deterministic outputs given the same inputs.

    Why this is correct

    Idempotency requires deterministic components.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
  • Use explanations to understand the rule behind the answer.

TExam Day Tips

  • Underline the problem statement mentally.
  • 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

Related practice questions

Related PMLE practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

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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: Store intermediate data in Cloud Storage with unique run IDs. — Idempotent components ensure the same inputs produce the same outputs. Passing data via GCS URIs is a best practice. Skew detection should compare training data distribution with serving data. Using unique run IDs for outputs ensures idempotency. Avoiding in-memory data passing is important for large datasets.

What should I do if I get this PMLE question wrong?

Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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

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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.