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Solving business challenges with ML →mediumMultiple ChoiceObjective-mapped

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.”

PMLE Solving business challenges with ML Practice Question

This PMLE practice question tests your understanding of solving business challenges with ml. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.

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.

Question 1mediummultiple choice
Read the full NAT/PAT explanation →

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
```

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 message indicates that the explanation metadata provided in the Vertex AI endpoint configuration is missing the required `outputs` field. In Vertex AI's Explainable AI, the `outputs` field must contain at least one entry with a `displayName` attribute to define which output tensor to explain. Without this, the API rejects the request with a 400 error.

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

    Why this is correct

    Vertex AI requires each output in explanation metadata to have a 'displayName' field.

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

    Why it's wrong here

    Baselines are optional and not required for the error message shown.

  • ✗

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

    Why it's wrong here

    Inputs are specified directly; 'visualization' is not required for tabular data.

  • ✗

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

    Why it's wrong here

    The error is about missing metadata fields, not about method compatibility.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the distinction between required fields in the explanation metadata (inputs vs. outputs) and their sub-attributes (like displayName), leading candidates to confuse a missing baseline or unsupported method with the actual missing outputs field.

Trap categories for this question

  • Command / output trap

    Baselines are optional and not required for the error message shown.

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

  • 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.

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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 — Read the scenario before looking for a memorised answer..

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 message indicates that the explanation metadata provided in the Vertex AI endpoint configuration is missing the required `outputs` field. In Vertex AI's Explainable AI, the `outputs` field must contain at least one entry with a `displayName` attribute to define which output tensor to explain. Without this, the API rejects the request with a 400 error.

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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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.