hardMultiple Select
AIF-C01 Practice Question: Optimizing costs for a Bedrock application that…
A company is optimizing costs for a Bedrock application that performs sentiment analysis on customer reviews. The workload is steady with occasional spikes. Which THREE strategies can help reduce costs without sacrificing accuracy? (Choose THREE)
⚠ Common exam trap
A common misconception is that fine-tuning a larger model always yields better accuracy and cost savings, but in reality, fine-tuning increases cost and a smaller, right-sized model can achieve comparable accuracy for the specific task. Additionally, enabling model caching and using batch inference are effective cost-saving strategies without sacrificing accuracy.
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
✓
Enable model caching to avoid reprocessing identical reviews
Option A is correct because enabling model caching (e.g., Bedrock prompt caching) stores results for identical review inputs, so repeated or duplicate reviews are served from cache instead of invoking the model again, directly cutting inference token costs while returning the same accurate output. Option B is correct because Bedrock batch inference processes large volumes of reviews asynchronously at a lower per-token price than on-demand invocation, and running these jobs during off-peak hours aligns with the steady workload with occasional spikes, reducing cost without changing model accuracy. Option D is correct because right-sizing to a smaller foundation model that still performs well on sentiment analysis lowers the per-token price and latency, and since accuracy is validated on the task, cost drops without sacrificing quality. Option C is not correct because fine-tuning a large model increases training and inference costs and is aimed at accuracy gains, not cost reduction, which the scenario does not need. Option E is not correct because provisioning throughput capacity (Provisioned Throughput) is a fixed hourly commitment suited to predictable high utilization; for a steady workload with only occasional spikes it can cost more than on-demand or batch usage rather than reducing costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable model caching to avoid reprocessing identical reviews
Why this is correct
Model caching stores responses for previously seen inputs, so identical reviews reuse the stored result instead of invoking the model again. This satisfies the cost-reduction goal without changing model outputs, preserving accuracy for steady workloads with repeated content.
- ✓
Use batch inference to process reviews in bulk during off-peak hours
Why this is correct
Batch inference submits many reviews in a single asynchronous job, which Bedrock prices at a discount to on-demand invocation. This satisfies the cost-reduction goal for steady workloads, since bulk processing during off-peak hours lowers per-review spend without altering model accuracy.
- ✗
Fine-tune a large model on the sentiment dataset for better accuracy
Why it's wrong here
Fine-tuning adds cost; using a large model increases per-invocation cost.
- ✓
Select a smaller, right-sized foundation model that performs well on sentiment analysis
Why this is correct
Sentiment analysis is a narrow classification task that smaller foundation models handle accurately. Right-sizing to a smaller model lowers per-token inference cost, directly addressing the steady workload's cost constraint while preserving accuracy, since the larger model's extra capacity adds no benefit here.
- ✗
Provision enough throughput capacity to handle peak loads
Why it's wrong here
Provisioned throughput guarantees capacity but is more expensive than on-demand.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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