Question 141 of 835
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.