Question 172 of 500

Business Strategies for Generative AI Solutions →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the pipeline spec does not declare 'eval_dataset' as a pipeline input parameter. This is the root cause because in Vertex AI pipeline definitions, any parameter used by a component must first be defined in the root-level inputDefinitions section of the pipeline spec; simply referencing it as a componentInput inside a task is insufficient. The runtimeConfig may supply a value, but without the formal declaration in inputDefinitions, the pipeline fails with a missing parameter error. On the Google Cloud Generative AI Leader exam, this question tests your ability to read a JSON pipeline representation and distinguish between component-level inputs and pipeline-level inputs—a common trap is confusing where a parameter is used versus where it is declared. Remember the memory tip: "Declare it at the root before you use it as a shoot."

Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions

This Generative AI Leader practice question tests your understanding of business strategies for generative ai 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.

Exhibit

Refer to the exhibit.

```
{
  "displayName": "my-pipeline",
  "pipelineSpec": {
    "root": {
      "inputDefinitions": {},
      "task": {
        "componentRef": {
          "name": "comp-model-eval"
        },
        "inputs": {
          "project": {
            "runtimeValue": {
              "constantValue": "my-project"
            }
          },
          "location": {
            "runtimeValue": {
              "constantValue": "us-central1"
            }
          },
          "model_name": {
            "componentInput": "model_name"
          },
          "eval_dataset": {
            "componentInput": "eval_dataset"
          }
        }
      }
    }
  },
  "runtimeConfig": {
    "parameterValues": {
      "model_name": "text-bison@001",
      "eval_dataset": "projects/my-project/datasets/eval"
    }
  }
}
```

A machine learning engineer is defining a Vertex AI pipeline for model evaluation using the JSON representation shown. The pipeline fails with an error that the 'eval_dataset' parameter is missing. What is the issue?

Question 1hardmultiple choice

Full question →

Exhibit

Refer to the exhibit.

```
{
  "displayName": "my-pipeline",
  "pipelineSpec": {
    "root": {
      "inputDefinitions": {},
      "task": {
        "componentRef": {
          "name": "comp-model-eval"
        },
        "inputs": {
          "project": {
            "runtimeValue": {
              "constantValue": "my-project"
            }
          },
          "location": {
            "runtimeValue": {
              "constantValue": "us-central1"
            }
          },
          "model_name": {
            "componentInput": "model_name"
          },
          "eval_dataset": {
            "componentInput": "eval_dataset"
          }
        }
      }
    }
  },
  "runtimeConfig": {
    "parameterValues": {
      "model_name": "text-bison@001",
      "eval_dataset": "projects/my-project/datasets/eval"
    }
  }
}
```

A
The component 'comp-model-eval' does not accept 'eval_dataset' as input
Why wrong: The error is about the parameter not being defined in the pipeline spec, not the component.
B
The 'project' parameter should be a pipeline input, not a constant
Why wrong: Using a constant for project is valid.
C
The runtimeConfig parameter values must be strings, not references
Why wrong: The runtimeConfig values are strings; the issue is the missing declaration.
D
The pipeline spec does not declare 'eval_dataset' as a pipeline input parameter
The root inputDefinitions is empty, so 'eval_dataset' is not recognized as a pipeline parameter.

Full breakdown with real-world context →

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 pipeline spec does not declare 'eval_dataset' as a pipeline input parameter

The pipeline spec defines 'eval_dataset' as a componentInput, but it is not defined in the root's inputDefinitions (option B). The runtimeConfig has the value, but the pipeline spec does not declare the parameter. The component (A) may be fine. The constant (C) is for project. The runtimeConfig (D) is correct but the spec is missing the input definition.

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 component 'comp-model-eval' does not accept 'eval_dataset' as input
Why it's wrong here
The error is about the parameter not being defined in the pipeline spec, not the component.
✗
The 'project' parameter should be a pipeline input, not a constant
Why it's wrong here
Using a constant for project is valid.
✗
The runtimeConfig parameter values must be strings, not references
Why it's wrong here
The runtimeConfig values are strings; the issue is the missing declaration.
✓
The pipeline spec does not declare 'eval_dataset' as a pipeline input parameter
Why this is correct
The root inputDefinitions is empty, so 'eval_dataset' is not recognized as a pipeline parameter.
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 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 Generative AI Leader 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.

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FAQ

Questions learners often ask

What does this Generative AI Leader question test?

Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The pipeline spec does not declare 'eval_dataset' as a pipeline input parameter — The pipeline spec defines 'eval_dataset' as a componentInput, but it is not defined in the root's inputDefinitions (option B). The runtimeConfig has the value, but the pipeline spec does not declare the parameter. The component (A) may be fine. The constant (C) is for project. The runtimeConfig (D) is correct but the spec is missing the input definition.

What should I do if I get this Generative AI Leader question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 22, 2026

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This Generative AI Leader 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 Generative AI Leader exam.