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Solving business challenges with ML →mediumMultiple ChoiceObjective-mapped

Vertex AI Explainability Metadata Error — Missing DisplayName | Google Professional Machine Learning Engineer Explained

This PMLE practice question tests your understanding of solving business challenges with ml. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: explanation metadata. 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.

Exhibit

Refer to the exhibit.

```yaml
deploymentResourcePool: projects/my-project/locations/us-central1/deploymentResourcePools/my-pool
disableContainerLogging: false
enableAccessLogging: true
explanationSpec:
  parameters:
    examples:
      exampleGcsSource:
        dataFormat: jsonl
        gcsSource:
          uris:
          - gs://my-bucket/examples/*.jsonl
        neighborCount: 10
      neighborCount: 10
      presampling: true
  metadata:
    inputs:
      input:
        inputTensor: input
        modality: numeric
        name: input
    outputs:
      output:
        outputTensor: output
        modality: numeric
        name: output
machineSpec:
  machineType: n1-standard-2
  acceleratorCount: 1
  acceleratorType: NVIDIA_TESLA_T4
minReplicaCount: 1
maxReplicaCount: 3
model: projects/my-project/locations/us-central1/models/123
trafficSplit:
  '0': 100
```

Refer to the exhibit. A machine learning engineer deployed a model on Vertex AI using this configuration. When testing the endpoint, the engineer receives a 400 error with the message: 'Invalid argument: Explanation metadata missing required field: `outputs`.' 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.

Exhibit

Refer to the exhibit.

```yaml
deploymentResourcePool: projects/my-project/locations/us-central1/deploymentResourcePools/my-pool
disableContainerLogging: false
enableAccessLogging: true
explanationSpec:
  parameters:
    examples:
      exampleGcsSource:
        dataFormat: jsonl
        gcsSource:
          uris:
          - gs://my-bucket/examples/*.jsonl
        neighborCount: 10
      neighborCount: 10
      presampling: true
  metadata:
    inputs:
      input:
        inputTensor: input
        modality: numeric
        name: input
    outputs:
      output:
        outputTensor: output
        modality: numeric
        name: output
machineSpec:
  machineType: n1-standard-2
  acceleratorCount: 1
  acceleratorType: NVIDIA_TESLA_T4
minReplicaCount: 1
maxReplicaCount: 3
model: projects/my-project/locations/us-central1/models/123
trafficSplit:
  '0': 100
```

Quick Answer

The answer is the missing `displayName` attribute within the explanation metadata `outputs` field. This is correct because Vertex AI Explainability requires the `outputs` dictionary to define which model output tensor to explain, and each entry in that dictionary must include a `displayName` key; without it, the API cannot map the explanation to a specific output, triggering the 400 error. On the Google Professional Machine Learning Engineer exam, this tests your understanding of Vertex AI’s Explainable AI configuration schema, often appearing as a trick where candidates focus on the `inputs` field or assume a missing `metadata` block. A common trap is forgetting that `displayName` is mandatory inside `outputs`, not optional. Remember the mnemonic: “Every output needs a name to play the explanation game.”

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

✓

The explanation metadata outputs field is missing the required 'displayName' attribute.

The error clearly states 'missing required field: outputs'. The explanation metadata must include an `outputs` field. Option A incorrectly attributes the error to a missing 'displayName' attribute; the actual issue is the absence of the entire `outputs` field.

Key principle: Explanation metadata

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 explanation metadata outputs field is missing the required 'displayName' attribute.

    Why this is correct

    Incorrect. The error is about missing outputs field, not just missing displayName.

    Clue confirmation

    The clue word "most likely" in the question point toward this answer.

    Related concept

    Explanation metadata

  • ✗

    The explanation metadata needs a 'baseline' configuration for the input.

    Why it's wrong here

    Incorrect. Missing baseline is a different error.

  • ✗

    The explanation metadata inputs field should be wrapped inside a 'visualization' block.

    Why it's wrong here

    Incorrect. Visualization block is not relevant.

  • ✗

    The explainability method chosen is not supported for the model type.

    Why it's wrong here

    Incorrect. Unsupported method would give a different error.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Candidates often focus on the subfields like displayName, but the error indicates the top-level outputs field is missing.

Detailed technical explanation

How to think about this question

In Vertex AI, the explanation metadata is defined in the `explanationSpec` of the endpoint deployment. The `outputs` field is a map where each key corresponds to an output tensor name from the model's signature, and each value must include a `displayName` for identification. This is critical for models with multiple outputs (e.g., multi-class classification or object detection) to specify which output to explain. A common real-world scenario is deploying a TensorFlow model with multiple output heads; omitting the `outputs` field or its `displayName` causes the exact 400 error seen here.

KKey Concepts to Remember

  • Explanation metadata

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

Explanation metadata

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. Explanation metadata 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.

Review explanation metadata, then practise related PMLE questions on the same topic to reinforce the concept.

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FAQ

Questions learners often ask

What does this PMLE question test?

Solving business challenges with ML — This question tests Solving business challenges with ML — Explanation metadata.

What is the correct answer to this question?

The correct answer is: The explanation metadata outputs field is missing the required 'displayName' attribute. — The error clearly states 'missing required field: outputs'. The explanation metadata must include an `outputs` field. Option A incorrectly attributes the error to a missing 'displayName' attribute; the actual issue is the absence of the entire `outputs` field.

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

Review explanation metadata, then practise related PMLE questions on the same topic to reinforce the concept.

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?

Explanation metadata

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Last reviewed: Jun 30, 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.