CLF-C02 Cloud Technology and Services Practice Question
Which AWS service allows you to build, train, and deploy machine learning models at scale?
⚠ Common exam trap
Test-takers frequently confuse purpose-built AI services (like Rekognition or Comprehend) with the full ML platform (SageMaker), assuming any service with 'AI' in its name can handle custom model training and deployment.
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
✓
Amazon SageMaker
Amazon SageMaker is the correct answer because it is a fully managed service that provides every component needed for the machine learning lifecycle, including building, training, and deploying models at scale. It offers integrated Jupyter notebooks for development, built-in algorithms, automatic model tuning, and one-click deployment to a production endpoint with auto-scaling. This makes it the single AWS service designed specifically for end-to-end ML workflows, unlike the other options which serve narrower AI/ML functions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Rekognition
Why it's wrong here
Amazon Rekognition is a fully managed, pre-trained computer vision service that performs tasks such as object detection, face analysis, and content moderation by calling a simple API. The underlying models are fixed and managed by AWS, so you cannot bring custom training data to build and deploy a unique model. This makes it a specialized AI service, not a general-purpose ML development platform.
- ✗
Amazon Comprehend
Why it's wrong here
Amazon Comprehend is a natural language processing (NLP) service that provides pre-trained models to extract sentiment, key phrases, entities, and language from unstructured text. It focuses solely on text analysis and does not support the broader ML lifecycle, such as custom model training, hyperparameter tuning, or scalable deployment of your own algorithms. Thus, it is a single-purpose AI service, not a general ML development platform.
- ✓
Amazon SageMaker
Why this is correct
Amazon SageMaker is AWS's flagship machine learning platform, offering an integrated suite that covers the entire ML lifecycle: labeling with Ground Truth, building via Jupyter notebooks, training on managed clusters, automatic tuning with Autopilot, and deployment to scalable endpoints. It supports popular frameworks like TensorFlow, PyTorch, and XGBoost, and includes MLOps features such as pipelines, model registry, and monitoring. This makes it the correct choice as a general, end-to-end ML development platform on AWS.
- ✗
AWS DeepLens
Why it's wrong here
AWS DeepLens is a physical deep learning-enabled video camera that executes ML models locally at the edge, commonly used for prototyping computer vision applications. It is not an environment for building or training models at scale; instead, it runs models that were created elsewhere with limited on-device compute for inference. Therefore, it fails as a general-purpose ML development platform.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CLF-C02 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 CLF-C02 exam.