MLA-C01 Data Preparation for Machine Learning Practice Question

Exhibit

{
  "ProcessingResources": {
    "ClusterConfig": {
      "InstanceCount": 1,
      "InstanceType": "ml.m5.large",
      "VolumeSizeInGB": 30
    }
  },
  "AppSpecification": {
    "ImageUri": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:latest",
    "ContainerEntrypoint": ["python", "process.py"]
  },
  "RoleArn": "arn:aws:iam::123456789012:role/SageMakerProcessingRole",
  "ProcessingInputs": [
    {
      "InputName": "input-1",
      "S3Input": {
        "S3Uri": "s3://my-bucket/input/data.csv",
        "LocalPath": "/opt/ml/processing/input",
        "S3DataType": "S3Prefix",
        "S3InputMode": "File",
        "S3DataDistributionType": "FullyReplicated",
        "S3CompressionType": "None"
      }
    }
  ],
  "ProcessingOutputConfig": {
    "Outputs": [
      {
        "OutputName": "output-1",
        "S3Output": {
          "S3Uri": "s3://my-bucket/output/",
          "LocalPath": "/opt/ml/processing/output",
          "S3UploadMode": "EndOfJob"
        }
      }
    ]
  }
}

Refer to the exhibit. A SageMaker Processing job configured as above fails with a timeout error. The input data is 100 GB of CSV files. The processing script performs standard data cleaning operations. What is the most likely cause?

⚠ Common exam trap

The trap here is that candidates may overlook the memory-to-data ratio and assume a timeout error always indicates a network or permission issue, rather than recognizing that an undersized instance with insufficient RAM for the dataset volume causes the job to stall and eventually time out.

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 processing job does not have enough memory for the data volume

The SageMaker Processing job is configured with a single `ml.m5.large` instance, which has 8 GiB of memory. The input data is 100 GB of CSV files, and the processing script performs standard data cleaning operations that typically load the entire dataset into memory (e.g., using pandas). With only 8 GiB of RAM, the instance cannot hold 100 GB of data, causing the job to run out of memory and eventually fail with a timeout error as the OS kills the process or the job hangs.

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 processing job does not have enough memory for the data volume
Why this is correct
ml.m5.large has 8 GB memory; 100 GB data likely causes memory exhaustion and slow disk swapping.
✗
The container entrypoint is missing the full path to the script
Why it's wrong here
This would cause a startup error, not a timeout.
✗
The S3Input S3CompressionType is set to "None" but the file is compressed
Why it's wrong here
This would cause parsing errors, not timeout.
✗
The IAM role does not have permission to write to the output bucket
Why it's wrong here
Permission errors would appear before processing starts.

Go deeper

MLA-C01 Data Preparation for Machine Learning Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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