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 pre-configured Amazon Machine Images that include popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and GPU drivers, allowing you to build, train, and deploy custom ML models on EC2 instances. Amazon SageMaker (E) is a fully managed service that provides end-to-end capabilities for building, training, and deploying custom ML models at scale, with built-in algorithms, automatic model tuning, and one-click deployment.
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
Polly is for text-to-speech, not for custom ML.
- ✗
Amazon Lex
Why it's wrong here
Lex is for chatbots, not general ML.
- ✓
AWS Deep Learning AMIs
Why this is correct
Deep Learning AMIs provide a customizable environment for building and training models.
- ✗
Amazon Rekognition
Why it's wrong here
Rekognition is a pre-built AI service, not for custom model building.
- ✓
Amazon SageMaker
Why this is correct
SageMaker provides end-to-end ML capabilities.
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