The answer is that the model_id parameter is referencing the wrong output. This is the most likely cause because the Vertex AI pipeline evaluate component relies on a specific model artifact produced by a prior training step; if the model_id points to a misnamed output, a different step’s result, or a nonexistent property, the pipeline cannot resolve the model and throws the “Model not found” error. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how pipeline steps pass artifacts through PropertyFiles or JsonGet, and a common trap is confusing the training step’s output name with the evaluate step’s expected input name. Remember that the error is about model resolution, not about data quality or thresholds, so always verify the output reference chain. A useful memory tip: “Model not found? Check the ID’s ground—wrong output is the root around.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Network Topology
The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model_id parameter is referencing the wrong output
The error 'Model not found' during the evaluate component indicates that the model_id parameter is referencing an output that does not exist or is incorrectly named. In SageMaker Pipelines, the evaluate step typically takes the model artifact from a previous training step via a PropertyFile or JsonGet, and if the model_id points to a wrong output (e.g., a different step's output or a misspelled reference), the pipeline cannot locate the model. This is the most likely cause because the error is specific to model resolution, not dataset or threshold issues.
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.
✗
The dataset_id is misspelled
Why it's wrong here
Wrong: Would cause training failure, not model not found.
✓
The model_id parameter is referencing the wrong output
Why this is correct
Correct: Output name mismatch causes Model not found.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The training container did not produce a model artifact
Why it's wrong here
Wrong: The training likely succeeded but output name is incorrect.
✗
The threshold value is invalid
Why it's wrong here
Wrong: Threshold is for evaluation, not model existence.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between resource resolution errors (like 'Model not found') and data/validation errors, tricking candidates into confusing dataset or threshold issues with pipeline step output references.
Trap categories for this question
Command / output trap
Wrong: The training likely succeeded but output name is incorrect.
Detailed technical explanation
How to think about this question
In SageMaker Pipelines, the evaluate step uses a ProcessingStep or TransformStep that references a model artifact via a PipelineVariable or PropertyFile. The model_id is often derived from the output of a TrainingStep using `TrainingStep.properties.model_artifacts` or a custom `JsonGet` on the training output. If the model_id is incorrectly set to a different step's output (e.g., a preprocessing step) or a non-existent key, the pipeline fails at runtime because the evaluate step cannot resolve the S3 path to the model. This is a common misconfiguration when chaining steps manually without using the built-in `Model` object or `PipelineModel`.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model_id parameter is referencing the wrong output — The error 'Model not found' during the evaluate component indicates that the model_id parameter is referencing an output that does not exist or is incorrectly named. In SageMaker Pipelines, the evaluate step typically takes the model artifact from a previous training step via a PropertyFile or JsonGet, and if the model_id points to a wrong output (e.g., a different step's output or a misspelled reference), the pipeline cannot locate the model. This is the most likely cause because the error is specific to model resolution, not dataset or threshold issues.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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