AWSML Model Development

AI Services & Custom Model — MLA-C01 Practice Question

A representative AWS Machine Learning Engineer Associate (MLA-C01) exam question on AI Services & Custom Model. Work through it below, then read why each option is right or wrong.

Short answer

The correct answer is B. Use Amazon Textract to extract text from the label images, then apply simple regex parsing.

Amazon Textract is a managed AI service purpose-built for extracting text (and key-value pairs) from documents and images. Combined with lightweight regex, it meets the two-week deadline with no ML expertise required.

The Question

A retail chain wants to automatically extract product names, prices, and expiration dates from photos of shelf labels. The team has no ML engineers and needs a solution within two weeks. Which approach is MOST appropriate?

ATrain a custom object detection model on SageMaker using labeled shelf-label images

BUse Amazon Textract to extract text from the label images, then apply simple regex parsingCorrect

CBuild a custom CNN from scratch in a SageMaker notebook and deploy it to a real-time endpoint

DUse Amazon Rekognition Custom Labels to train a new label-detection model

Why B is correct

Why the other options are wrong

Option A: Train a custom object detection model on SageMaker using labeled shelf-label images

Training a custom object detection model requires labeled data, ML expertise, and time the team does not have — it is unnecessary when a managed OCR service already solves the problem.

Option C: Build a custom CNN from scratch in a SageMaker notebook and deploy it to a real-time endpoint

Building a CNN from scratch is the highest-effort, highest-skill option and offers no advantage over a managed text-extraction service for this text-based task.

Option D: Use Amazon Rekognition Custom Labels to train a new label-detection model

Rekognition Custom Labels is for image classification/object detection, not text extraction, and still requires training data and time to build a custom model.

Key idea: AI Services & Custom Model

Why B is correct: Amazon Textract is a managed AI service purpose-built for extracting text (and key-value pairs) from documents and images. Combined with lightweight regex, it meets the two-week deadline with no ML expertise required. Why A is wrong: Training a custom object detection model requires labeled data, ML expertise, and time the team does not have — it is unnecessary when a managed OCR service already solves the problem. Why C is wrong: Building a CNN from scratch is the highest-effort, highest-skill option and offers no advantage over a managed text-extraction service for this text-based task. Why D is wrong: Rekognition Custom Labels is for image classification/object detection, not text extraction, and still requires training data and time to build a custom model. On the MLA-C01 exam, questions in the "ML Model Development" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.

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