AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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.
Exhibit
A SageMaker notebook cell output:
"Model size: 7B parameters\nInference time on ml.g5.2xlarge: 250ms per token\nBatch size: 1\nMemory utilization: 90%"
Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Exhibit
A SageMaker notebook cell output:
"Model size: 7B parameters\nInference time on ml.g5.2xlarge: 250ms per token\nBatch size: 1\nMemory utilization: 90%"
A
Use a CPU instance
Why wrong: CPU instances are generally slower for deep learning inference than GPUs.
B
Reduce model size through quantization
Quantization reduces the precision of model weights, decreasing compute per token and thus latency.
C
Switch to a larger instance type
Why wrong: A larger instance may reduce latency but quantization is more effective as it reduces model size.
D
Increase batch size to 10
Why wrong: Increasing batch size improves throughput but per-token latency may stay the same or increase.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce model size through quantization
Reducing model size through quantization directly decreases the computational and memory requirements per inference step, which lowers the time to generate each token. This is especially effective on GPU instances where smaller models fit better in GPU memory and reduce memory bandwidth bottlenecks, leading to lower per-token latency.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use a CPU instance
Why it's wrong here
CPU instances are generally slower for deep learning inference than GPUs.
✓
Reduce model size through quantization
Why this is correct
Quantization reduces the precision of model weights, decreasing compute per token and thus latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch to a larger instance type
Why it's wrong here
A larger instance may reduce latency but quantization is more effective as it reduces model size.
✗
Increase batch size to 10
Why it's wrong here
Increasing batch size improves throughput but per-token latency may stay the same or increase.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often think that larger instances always reduce latency, when in fact they may increase latency due to higher memory latency and inter-chip communication, while quantization directly addresses the memory bandwidth bottleneck in autoregressive decoding.
Detailed technical explanation
How to think about this question
Quantization reduces model weights from FP32 to lower precision (e.g., INT8 or FP16), which shrinks memory footprint and allows more model parameters to reside in faster GPU cache (L1/L2) rather than slower global memory. This reduction in memory access latency directly translates to faster autoregressive token generation, where each token depends on the previous one and memory bandwidth is often the bottleneck.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce model size through quantization — Reducing model size through quantization directly decreases the computational and memory requirements per inference step, which lowers the time to generate each token. This is especially effective on GPU instances where smaller models fit better in GPU memory and reduce memory bandwidth bottlenecks, leading to lower per-token latency.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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