サンプル問題 1： 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.
正解：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.
サンプル問題 2： A company deploys multiple Amazon BedrockCbased generative AI (GenAI) applications across multiple business units for customer service, content generation, and document analysis. Some applications show unpredictable token consumption patterns. The company requires a comprehensive observability solution that provides real-time visibility into token usage patterns across multiple models. The observability solution must support custom dashboards for multiple stakeholder groups and provide alerting capabilities for token consumption across all the foundation models that the company’s applications use. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
A: Use Amazon CloudWatch metrics as data sources to create custom Amazon QuickSight dashboards that show token usage trends and usage patterns across FMs.
B: Use CloudWatch Logs Insights to analyze Amazon Bedrock invocation logs for token consumption patterns and usage attribution by application. Create custom queries to identify high-usage scenarios. Add log widgets to dashboards to enable continuous monitoring.
C: Create custom Amazon CloudWatch dashboards that combine native Amazon Bedrock token and invocation CloudWatch metrics. Set up CloudWatch alarms to monitor token usage thresholds.
D: Create dashboards that show token usage trends and patterns across the company’s FMs by using an Amazon Bedrock zero-ETL integration with Amazon Managed Grafana.
E: Implement Amazon EventBridge rules to capture Amazon Bedrock model invocation events. Route token usage data to Amazon OpenSearch Serverless by using Amazon Data Firehose. Use OpenSearch dashboards to analyze usage patterns.
正解：C, D
Explanation: The combination of Options C and D delivers comprehensive, real-time observability for Amazon Bedrock workloads with the least operational overhead by relying on native integrations and managed services. Amazon Bedrock publishes built-in CloudWatch metrics for model invocations and token usage. Option C leverages these native metrics directly, allowing teams to build centralized CloudWatch dashboards without additional data pipelines or custom processing. CloudWatch alarms provide threshold-based alerting for token consumption, enabling proactive cost and usage control across all foundation models. This approach aligns with AWS guidance to use native service metrics whenever possible to reduce operational complexity. Option D complements CloudWatch by enabling advanced, stakeholder-specific visualizations through Amazon Managed Grafana. The zero-ETL integration allows Bedrock and CloudWatch metrics to be visualized directly in Grafana without building ingestion pipelines or managing storage layers. Grafana dashboards are particularly well suited for serving different audiences, such as engineering, finance, and product teams, each with customized views of token usage and trends. Option A introduces unnecessary complexity by adding a business intelligence layer that is better suited for historical analytics than real-time operational monitoring. Option B is useful for deep log analysis but requires query maintenance and does not provide efficient real-time dashboards at scale. Option E involves multiple services and custom data flows, significantly increasing operational overhead compared to native metric-based observability. By combining CloudWatch dashboards and alarms with Managed Grafana’s zero-ETL visualization capabilities, the company achieves real-time visibility, flexible dashboards, and automated alerting across all Amazon Bedrock foundation models with minimal operational effort.
サンプル問題 3： An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits. Which solution will resolve this problem?
A: Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.
B: Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
C: Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.
D: Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the Lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.
正解：C
Explanation: Option C directly addresses the root cause of truncated and inconsistent responses by using AWS-recommended semantic chunking and dynamic retrieval rather than static or sequential chunk processing. Amazon Bedrock documentation emphasizes that foundation models have fixed context windows and that sending oversized or poorly structured input can lead to truncation, loss of context, and degraded output quality. Semantic chunking breaks documents based on meaning instead of fixed token counts. By using a breakpoint percentile threshold and sentence buffers, the content remains coherent and semantically complete. This approach reduces the likelihood that important concepts are split across chunks, which is a common cause of inconsistent summarization results. The RetrieveAndGenerate API is designed specifically to handle large documents that exceed a model’s context window. Instead of forcing all content into a single inference call, the API generates embeddings for chunks and dynamically selects only the most relevant chunks based on similarity to the user query. This ensures that the FM receives only high-value context while staying within its context window limits. Option A is ineffective because chaining chunks sequentially does not align with how FMs process context and risks exceeding context limits or introducing irrelevant information. Option B improves structure but still relies on larger parent chunks, which can lead to inefficiencies when processing very large documents. Option D processes segments independently, which often causes loss of global context and inconsistent summaries. Therefore, Option C is the most robust, AWS-aligned solution for resolving truncation and consistency issues when processing large technical documents with Amazon Bedrock.
Amazon AIP-C01試験準備をしていますか？それとも自分は全部できそうです。復習する必要がないと思いますか？それとも自分は何もできないような気がします。どうすればいいでしょうか？実は、この問題を解決するのは難しくないです。勉強者がやるべきのは分解分析をマスターすることです。ここではAIP-C01問題集を使って学習中の課題を確認し、知識を整理してAWS Certified Generative AI Developer - Professional試験の準備を進めます。
