MLA-C01 Practice Question: A data scientist runs this pipeline but the Train…

Exhibit

Refer to the exhibit.
```
Pipeline definition snippet:
{
  "Steps": [
    {
      "Name": "Preprocess",
      "Type": "Processing",
      "Arguments": {
        "ProcessingResources": {
          "ClusterConfig": {
            "InstanceCount": 1,
            "InstanceType": "ml.m5.large",
            "VolumeSizeInGB": 10
          }
        }
      }
    },
    {
      "Name": "Train",
      "Type": "Training",
      "DependsOn": ["Preprocess"],
      "Arguments": {
        "AlgorithmSpecification": {
          "TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-training:latest",
          "TrainingInputMode": "File"
        },
        "ResourceConfig": {
          "InstanceCount": 2,
          "InstanceType": "ml.p3.2xlarge",
          "VolumeSizeInGB": 30
        }
      }
    }
  ]
}
```

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

⚠ Common exam trap

AWS often tests the distinction between resource limits (quotas) and other failure modes; the trap here is that candidates may confuse 'ResourceLimitExceeded' with a generic 'insufficient capacity' error, but the error specifically refers to account-level service quotas, not AWS resource availability.

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 account has a limit of 0 for ml.p3.2xlarge instances.

The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.

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 account has a limit of 0 for ml.p3.2xlarge instances.
Why this is correct
A zero limit or insufficient quota results in ResourceLimitExceeded.
✗
The volume size is too small for training.
Why it's wrong here
Small volume would cause a disk full error, not ResourceLimitExceeded.
✗
The Preprocess step did not complete successfully.
Why it's wrong here
Preprocess failure would cause a different error, not ResourceLimitExceeded.
✗
The training image is not accessible.
Why it's wrong here
Inaccessible image causes a different error (e.g., image not found).

Go deeper

MLA-C01 Practice Question: A data scientist runs this pipeline but the Train…

Exhibit

Why each option matters

Option-by-option breakdown

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