easyMultiple Choice
AIF-C01 Practice Question: Use Amazon Bedrock to generate product…
A company wants to use Amazon Bedrock to generate product descriptions for an e-commerce catalog. They need to process 100,000 product records efficiently and cost-effectively. Which inference option should they choose?
⚠ Common exam trap
A common pitfall is assuming that on-demand inference is suitable for all use cases. For large-scale, non-real-time batch jobs like generating 100,000 product descriptions, Batch inference in Amazon Bedrock is the most cost-effective and efficient choice. Candidates often overlook the asynchronous processing and cost savings of batch inference.
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
✓
Batch inference
Batch inference is the correct choice because it is designed for asynchronous, high-volume processing of large datasets like 100,000 product records. It processes requests in bulk, significantly reducing per-record cost compared to real-time options, and is ideal for non-latency-sensitive workloads such as generating product descriptions for an entire catalog.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
On-demand inference
Why it's wrong here
On-demand inference bills per token with no commitment, so 100,000 records incur full per-token rates and synchronous throttling; it suits low-volume, intermittent, interactive traffic. Batch inference submits the whole dataset as one asynchronous job at reduced cost, which this bulk catalogue workload needs.
- ✓
Batch inference
Why this is correct
Batch inference processes large volumes of records asynchronously through a single job, which suits the 100,000-record catalogue without maintaining real-time throughput. Amazon Bedrock batch jobs typically complete within 24 hours at roughly 50% lower cost than on-demand inference, directly satisfying the stem's efficiency and cost-effectiveness constraints.
- ✗
Model caching
Why it's wrong here
Model caching is not a Bedrock inference option; prompt caching reuses repeated prompt prefixes to cut latency and token cost, and it does not process a large one-off batch. Batch inference handles the 100,000 records asynchronously at lower cost, which is what the scenario requires.
- ✗
Provisioned throughput
Why it's wrong here
Provisioned throughput reserves dedicated model units for a committed term, billing hourly whether or not requests arrive; it suits steady, predictable, high-volume traffic, not a one-off batch of 100,000 records. Batch inference processes large asynchronous jobs at roughly half the cost, matching this workload.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.