This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An ML team is deploying a real-time inference endpoint for a computer vision model using Amazon SageMaker. The model requires GPU acceleration for low latency. Which instance type should the team choose to minimize cost while meeting the GPU requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
ml.g5.xlarge
Why wrong: G5 instances are GPU-based but typically more costly than P3 for inference.
ml.c5.xlarge
Why wrong: C5 instances are CPU-only, no GPU.
ml.p3.2xlarge
P3 provides GPU acceleration and is cost-effective for inference.
ml.p4d.24xlarge
Why wrong: P4d is powerful but much more expensive than P3.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
ml.p3.2xlarge
Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA Tesla V100) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.p3.2xlarge offers a single GPU with sufficient compute for real-time inference without over-provisioning resources, minimizing cost compared to larger GPU instances like ml.p4d.24xlarge.
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.
ml.g5.xlarge
Why it's wrong here
G5 instances are GPU-based but typically more costly than P3 for inference.
ml.c5.xlarge
Why it's wrong here
C5 instances are CPU-only, no GPU.
ml.p3.2xlarge
Why this is correct
P3 provides GPU acceleration and is cost-effective for inference.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
ml.p4d.24xlarge
Why it's wrong here
P4d is powerful but much more expensive than P3.
Common exam traps
The trap here is that candidates may assume any GPU instance is acceptable, but Cisco tests the ability to balance GPU requirement with cost minimization, leading them to pick the cheapest GPU option (ml.g5.xlarge) without recognizing that ml.p3.2xlarge is even more cost-effective for this specific workload.
Detailed technical explanation
Under the hood, SageMaker real-time endpoints allocate dedicated instances that remain active, so choosing an instance with the right GPU-to-cost ratio is critical. The ml.p3.2xlarge uses the NVIDIA Tesla V100 with 16 GB GPU memory, which is sufficient for many computer vision models like ResNet-50 or YOLOv5, while the ml.g5.xlarge uses the newer but more expensive A10G. In practice, teams often over-select GPU instances for inference, not realizing that smaller GPU instances like p3.2xlarge can handle production throughput with proper batching and model optimization.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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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: ml.p3.2xlarge — Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA Tesla V100) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.p3.2xlarge offers a single GPU with sufficient compute for real-time inference without over-provisioning resources, minimizing cost compared to larger GPU instances like ml.p4d.24xlarge.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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