AIF-C01 Fundamentals of AI and ML Practice Question
Which AWS services can be used to build, train, and deploy custom machine learning models? (Choose two.)
⚠ Common exam trap
Many candidates confuse pre-built AI services (Polly, Lex, Rekognition) with platforms that allow custom model development, leading them to select services that only consume pre-trained models rather than build and train custom ones.
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
✓
AWS Deep Learning AMIs
AWS Deep Learning AMIs (C) are correct because they provide pre-configured Amazon EC2 images with popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and GPU drivers, giving you the full environment needed to build and train custom models on your own infrastructure. Amazon SageMaker (E) is correct because it is a fully managed platform whose built-in capabilities (notebooks, training jobs, automatic model tuning, and hosting endpoints) let you build, train, and deploy custom ML models end to end. The other options are managed AI services with pre-trained models rather than tools for creating custom models: Amazon Polly (A) only converts text to lifelike speech, Amazon Lex (B) only builds conversational chatbots using NLU, and Amazon Rekognition (D) only provides pre-trained image and video analysis.
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 Polly
Why it's wrong here
Amazon Polly is a text-to-speech service, converting written text into lifelike speech; it consumes pre-built models rather than letting you build, train, or deploy custom ones. It would be the right choice for adding voice output to an application, not for the ML lifecycle.
- ✗
Amazon Lex
Why it's wrong here
Amazon Lex builds conversational chatbots using pre-trained natural language understanding; it does not expose model training or deployment. It fits creating voice and text interfaces for applications, not the build-train-deploy pipeline that Amazon SageMaker provides.
- ✓
AWS Deep Learning AMIs
Why this is correct
AWS Deep Learning AMIs provide pre-configured Amazon EC2 images bundling frameworks such as TensorFlow and PyTorch with GPU drivers, satisfying the build-and-train requirement. They supply the compute environment for custom model development, though deployment needs a separate service. This addresses the training half of the stem directly.
- ✗
Amazon Rekognition
Why it's wrong here
Amazon Rekognition offers pre-trained computer vision APIs for image and video analysis; it cannot build or train custom models. It suits detecting objects, faces, or inappropriate content out of the box, whereas SageMaker covers the full build, train, and deploy lifecycle.
- ✓
Amazon SageMaker
Why this is correct
Amazon SageMaker provides a fully managed environment covering the entire workflow: building with notebooks and built-in algorithms, training jobs, and deployment to endpoints. It satisfies the stem's build, train and deploy requirement end to end.
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