AWS Certified Generative AI Developer - Professional AIP-C01 Exam Questions
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A company is using Amazon Bedrock and Anthropic Claude 3 Haiku to develop an AI assistant. The AI
assistant normally processes 10,000 requests each hour but experiences surges of up to 30,000
requests each hour during peak usage periods. The AI assistant must respond within 2 seconds while
operating across multiple AWS Regions.
The company observes that during peak usage periods, the AI assistant experiences throughput
bottlenecks that cause increased latency and occasional request timeouts. The company must resolve
the performance issues.
Which solution will meet this requirement?
A.
Purchase provisioned throughput and sufficient model units (MUs) in a single Region. Configure the
application to retry failed requests with exponential backoff.
B.
Implement token batching to reduce API overhead. Use cross-Region inference profiles to automatically distribute traffic across available Regions.
C.
Set up auto scaling AWS Lambda functions in each Region. Implement client-side round-robin request distribution. Purchase one model unit (MU) of provisioned throughput as a backup.
D.
Implement batch inference for all requests by using Amazon S3 buckets across multiple Regions. Use Amazon SQS to set up an asynchronous retrieval process.
Correct Answer: B
Explanation:
Option B is the correct solution because it directly addresses both throughput bottlenecks and latency
requirements using native Amazon Bedrock performance optimization features that are designed for
real-time, high-volume generative AI workloads.
Amazon Bedrock supports cross-Region inference profiles, which allow applications to transparently
route inference requests across multiple AWS Regions. During peak usage periods, traffic is
automatically distributed to Regions with available capacity, reducing throttling, request queuing, and
timeout risks. This approach aligns with AWS guidance for building highly available, low-latency GenAI
applications that must scale elastically across geographic boundaries.
Token batching further improves efficiency by combining multiple inference requests into a single model
invocation where applicable. AWS Generative AI documentation highlights batching as a key optimization
technique to reduce per-request overhead, improve throughput, and better utilize model capacity. This is
especially effective for lightweight, low-latency models such as Claude 3 Haiku, which are designed for
fast responses and high request volumes.
Option A does not meet the requirement because purchasing provisioned throughput in a single Region
creates a regional bottleneck and does not address multi-Region availability or traffic spikes beyond
reserved capacity. Retries increase load and latency rather than resolving the root cause Option C improves application-layer scaling but does not solve model-side throughput limits. Client-side
round-robin routing lacks awareness of real-time model capacity and can still send traffic to saturated
Regions.
Option D is unsuitable because batch inference with asynchronous retrieval is designed for offline or
non-interactive workloads. It cannot meet a strict 2-second response time requirement for an interactive
AI assistant.
Therefore, Option B provides the most effective and AWS-aligned solution to achieve low latency, global
scalability, and high throughput during peak usage periods.