Quick Answer
The answer is Amazon SageMaker and AWS Deep Learning AMIs. These two services are correct because they both enable you to build, train, and deploy custom machine learning models, but at different levels of abstraction. Amazon SageMaker is a fully managed, end-to-end platform that handles infrastructure, provides built-in algorithms, automatic model tuning, and one-click deployment, making it ideal for scalable, production-ready workflows. In contrast, AWS Deep Learning AMIs are pre-configured Amazon Machine Images with popular frameworks like TensorFlow and PyTorch, giving you full control over the underlying EC2 instances for custom model development. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to distinguish between managed and unmanaged services for custom ML workloads. A common trap is selecting AWS Rekognition or Comprehend, which are pre-built AI services, not for building custom models. Memory tip: think “SageMaker for the full stack, Deep Learning AMI for the hands-on stack.”
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which AWS services can be used to build, train, and deploy custom machine learning models? (Choose two.)
Amazon Polly
Why wrong: Polly is for text-to-speech, not for custom ML.
Amazon Lex
Why wrong: Lex is for chatbots, not general ML.
AWS Deep Learning AMIs
Deep Learning AMIs provide a customizable environment for building and training models.
Amazon Rekognition
Why wrong: Rekognition is a pre-built AI service, not for custom model building.
Amazon SageMaker
SageMaker provides end-to-end ML capabilities.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that 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.
Detailed technical explanation
Under the hood, AWS Deep Learning AMIs come with CUDA, cuDNN, and NCCL pre-installed for GPU acceleration, and support distributed training across multiple instances using Horovod or parameter servers. SageMaker uses a managed infrastructure with automatic scaling, built-in Spot Instance support, and SageMaker Neo for model optimization to run on edge devices. A real-world scenario is a data science team using Deep Learning AMIs for custom model experimentation on a P4d instance with Elastic Fabric Adapter (EFA) for low-latency inter-node communication, then migrating to SageMaker for production training and deployment with automatic scaling.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-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 AIF-C01 exam.
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