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Question 222 of 506

Automating and orchestrating ML pipelines →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to configure the 'train-exec' executor to use a machine type with higher memory. This is correct because in Vertex AI pipelines, memory allocation is tied directly to the executor specification defined by the executorLabel; you cannot adjust a task’s memory independently—you must modify the executor’s machine type, such as switching to an n1-highmem series, to resolve resource exhaustion. On the Google Professional Machine Learning Engineer exam, this tests your understanding of pipeline component definitions versus task-level configurations, and a common trap is trying to add an accelerator or increase memory via the task’s runtime arguments, which is unsupported. Remember the key: to fix Vertex AI pipeline memory errors, you always fix the executor, not the task—think “executor equals memory spec.”

PMLE Automating and orchestrating ML pipelines Practice Question

This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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

{
  "pipelineJob": {
    "pipelineSpec": {
      "pipelineInfo": {"name": "training-pipeline"},
      "root": {
        "dag": {
          "tasks": {
            "preprocess": {
              "taskInfo": {"name": "preprocess"},
              "componentRef": {"name": "data-processing"},
              "inputs": {"data": {"artifacts": [{"name": "raw_data", "type": "Dataset"}]}}
            },
            "train": {
              "taskInfo": {"name": "train"},
              "componentRef": {"name": "trainer"},
              "inputs": {"dataset": {"taskOutputArtifact": {"taskName": "preprocess", "outputKey": "processed_data"}}},
              "dependentTasks": ["preprocess"],
              "executorLabel": "train-exec"
            }
          }
        }
      }
    },
    "runtimeConfig": {
      "gcsOutputDirectory": "gs://my-bucket/pipeline-output",
      "parameterValues": {
        "learning_rate": 0.01,
        "epochs": 10
      },
      "inputArtifacts": {
        "raw_data": {
          "gcsSourceArtifact": {
            "artifacts": [{"uri": "gs://my-bucket/data/raw.csv"}]
          }
        }
      }
    }
  }
}

Refer to the exhibit. A ML engineer runs this Vertex AI pipeline. After execution, the "train" task fails with a resource exhaustion error. The task consumes more memory than allocated. Which step should the engineer take to fix this issue without increasing the overall quota cost?

Question 1hardmultiple choice

Full question →

Exhibit

{
  "pipelineJob": {
    "pipelineSpec": {
      "pipelineInfo": {"name": "training-pipeline"},
      "root": {
        "dag": {
          "tasks": {
            "preprocess": {
              "taskInfo": {"name": "preprocess"},
              "componentRef": {"name": "data-processing"},
              "inputs": {"data": {"artifacts": [{"name": "raw_data", "type": "Dataset"}]}}
            },
            "train": {
              "taskInfo": {"name": "train"},
              "componentRef": {"name": "trainer"},
              "inputs": {"dataset": {"taskOutputArtifact": {"taskName": "preprocess", "outputKey": "processed_data"}}},
              "dependentTasks": ["preprocess"],
              "executorLabel": "train-exec"
            }
          }
        }
      }
    },
    "runtimeConfig": {
      "gcsOutputDirectory": "gs://my-bucket/pipeline-output",
      "parameterValues": {
        "learning_rate": 0.01,
        "epochs": 10
      },
      "inputArtifacts": {
        "raw_data": {
          "gcsSourceArtifact": {
            "artifacts": [{"uri": "gs://my-bucket/data/raw.csv"}]
          }
        }
      }
    }
  }
}

A
Add a 'memory' field to the train task specification.
Why wrong: The Vertex AI pipeline schema does not include a 'memory' field on tasks; resource allocation is controlled through executors.
B
Configure the 'train-exec' executor to use a machine type with higher memory.
The executor defines the machine type, and modifying it to use a higher-memory machine (e.g., n1-highmem-8) will provide more memory without changing other quota.
C
Increase the memory of the train task to 32 GiB.
Why wrong: Memory is not set on the task level in Vertex AI pipelines; it is configured via the executor.
D
Set 'acceleratorType' to 'NVIDIA_TESLA_T4' on the train task.
Why wrong: Adding a GPU accelerator does not address memory exhaustion and may increase cost.

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

✓

Configure the 'train-exec' executor to use a machine type with higher memory.

In Vertex AI pipelines, the executorLabel maps to a predefined executor that defines machine type. To increase memory for the train task, the engineer must modify the executor specification (e.g., 'machine_type: n1-highmem-*'). Increasing the task's memory directly is not supported; it's done via the executor. Adding an accelerator does not address memory exhaustion.

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.

✗
Add a 'memory' field to the train task specification.
Why it's wrong here
The Vertex AI pipeline schema does not include a 'memory' field on tasks; resource allocation is controlled through executors.
✓
Configure the 'train-exec' executor to use a machine type with higher memory.
Why this is correct
The executor defines the machine type, and modifying it to use a higher-memory machine (e.g., n1-highmem-8) will provide more memory without changing other quota.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the memory of the train task to 32 GiB.
Why it's wrong here
Memory is not set on the task level in Vertex AI pipelines; it is configured via the executor.
✗
Set 'acceleratorType' to 'NVIDIA_TESLA_T4' on the train task.
Why it's wrong here
Adding a GPU accelerator does not address memory exhaustion and may increase cost.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which PMLE 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 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: Configure the 'train-exec' executor to use a machine type with higher memory. — In Vertex AI pipelines, the executorLabel maps to a predefined executor that defines machine type. To increase memory for the train task, the engineer must modify the executor specification (e.g., 'machine_type: n1-highmem-*'). Increasing the task's memory directly is not supported; it's done via the executor. Adding an accelerator does not address memory exhaustion.

What should I do if I get this PMLE 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 24, 2026

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