- A
dsl.ParallelFor
Why wrong: dsl.ParallelFor is for parallel iteration, not conditional execution.
- B
dsl.ExitHandler
Why wrong: ExitHandler is for cleanup tasks after pipeline completion, not for conditional branching.
- C
dsl.Condition
Correct: dsl.Condition (or dsl.If) provides conditional execution of tasks based on conditions.
- D
dsl.Collected
Why wrong: dsl.Collected is used to collect results from parallel for loops, not for conditionals.
Question 699 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 Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?
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
✓
dsl.Condition
The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.
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.
- ✗
dsl.ParallelFor
Why it's wrong here
dsl.ParallelFor is for parallel iteration, not conditional execution.
- ✗
dsl.ExitHandler
Why it's wrong here
ExitHandler is for cleanup tasks after pipeline completion, not for conditional branching.
- ✓
dsl.Condition
Why this is correct
Correct: dsl.Condition (or dsl.If) provides conditional execution of tasks based on conditions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
dsl.Collected
Why it's wrong here
dsl.Collected is used to collect results from parallel for loops, not for conditionals.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse `dsl.Condition` with `dsl.ExitHandler` because both involve decision-making, but `ExitHandler` is only for post-exit cleanup, not for branching based on step outputs.
Detailed technical explanation
How to think about this question
Under the hood, `dsl.Condition` translates to an Argo Workflows `when` clause in the generated YAML, which evaluates a condition expression at runtime. The condition can reference the output of a previous component (e.g., `{{inputs.parameters.eval_metric}} > 0.9`). A subtle behavior is that the condition must be based on a parameter (string or numeric) that is available at pipeline compile time or as a runtime parameter; it cannot directly evaluate a large artifact. In a real-world scenario, if the evaluation metric is a float stored as a string, you must convert it to a number in the condition expression (e.g., using `float()` in the KFP DSL) to avoid comparison errors.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: dsl.Condition — The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.
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.
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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