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>> AIP-C01 Prüfungsunterlagen <<
Nun gibt es viele IT-Profis in der ganzen Welt und die Konkurrenz der IT-Branche ist sehr hart. So viele IT-Profis entscheiden sich dafür, an der IT-Zertifizierungsprüfung teilzunehmen, um ihre Position in der IT-Branche zu verstärken. Die AIP-C01 Prüfung ist eine sehr wichtige Amazon-Zertifizierungsprüfung. Aber wenn Sie eine Amazon-Zertifizierung erhalten wollen, müssen Sie die Prüfung bestehen.
102. Frage
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses.
The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes.
Which solution will meet these requirements?
Antwort: D
Begründung:
Option B is correct because the workload has predictable short peak periods, strict low-latency requirements, and lower demand between spikes. Amazon SageMaker Serverless Inference is designed for intermittent or unpredictable traffic because SageMaker manages the infrastructure and the customer pays based on usage rather than continuously running idle instances. AWS documentation describes serverless inference as suitable when traffic is intermittent or unpredictable and when users do not want to manage instances or scaling policies.
Provisioned concurrency is the key part of this answer. For latency-sensitive serverless inference workloads, provisioned concurrency keeps the required number of serverless instances initialized and ready to respond.
AWS documentation states that SageMaker Serverless Inference integrates with Application Auto Scaling so provisioned concurrency can be scaled up or down based on a target metric or schedule. This fits a daily 2- hour peak window because the company can configure enough provisioned concurrency for 500 concurrent requests during peak periods and scale down when demand decreases.
Deploying each model to a separate serverless endpoint is also important. The categorization, extraction, and response-generation models likely have different memory, latency, and concurrency needs. Separate endpoints allow independent memory sizing, maximum concurrency configuration, monitoring, and scaling for each task. AWS API documentation also shows that serverless endpoint configuration includes maximum concurrency and provisioned concurrency settings, with provisioned concurrency required to be less than or equal to maximum concurrency.
Option A is less suitable because multi-model endpoints are usually best when many models can share infrastructure, but model loading and shared capacity can add latency risk. Option C assumes Amazon Bedrock provisioned throughput for models, but the company is developing specialized NLP models and needs task-specific deployment controls. Request batching can also add latency. Option D uses asynchronous inference, which is inappropriate for a less-than-500-ms synchronous response requirement. Therefore, option B best satisfies latency, concurrency, accuracy isolation, and resource optimization.
103. Frage
A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768-dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries.
The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information.
Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds.
The company expects storage needs for the application to grow from 90 GB to 360 GB within a year.
The company needs a solution to improve retrieval relevance and system performance at scale.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option C directly addresses both retrieval relevance and performance scalability. Fixed token chunking breaks semantic continuity in legal texts, causing incomplete context retrieval and degraded response quality. By switching to semantic chunking-based on legal arguments, clauses, or sections-the application preserves contextual integrity, improving retrieval accuracy and reducing hallucinations.
Regenerating embeddings aligned with the new chunk structure also improves vector search efficiency, reducing unnecessary comparisons and helping control latency as the dataset scales.
Option A increases cost and latency without fixing the core issue. Option B removes dynamic reasoning, which defeats the purpose of a legal RAG system. Option D discards vector semantics entirely and is unsuitable for nuanced legal research. Therefore, Option C is the correct and scalable solution.
104. Frage
An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations.
Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data.
The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval.
Which combination of solutions will meet these requirements? (Select TWO.)
Antwort: B,D
Begründung:
Options A and E are correct because the failure pattern is caused by exceeding the model's usable context window and by sending too much raw customer data directly to the FM. Amazon Bedrock Knowledge Bases follows the RAG pattern by splitting source content into manageable chunks, converting chunks into embeddings, storing them in a vector index, and using semantic similarity to retrieve relevant content for a user query. This supports option E because the application can index customer purchases, profiles, and review- related information, then retrieve only the most relevant records for the current recommendation request instead of stuffing all raw data into one prompt.
Option A is also correct because chunking and staged synthesis are standard ways to handle inputs that exceed an FM's context capacity. Processing smaller segments avoids context-length-exceeded errors, and a final synthesis call can combine intermediate findings into a complete recommendation. This is especially useful when the company says it wants the system to consider all customer data, not merely truncate older or less important data. Amazon Bedrock Knowledge Bases also lets teams configure how content is chunked for storage and retrieval, which supports a structured approach rather than an oversized one-shot prompt.
Option B is not sufficient because truncating "less important" data conflicts with the requirement to consider all customer data. Option C violates the implied design direction because the company wants to use Knowledge Bases, and simply choosing a larger context model does not solve long-term growth of customer histories. Option D is technically incorrect: model parameters such as max_tokens control output generation limits, not the model's maximum context window, and different Claude models have model-specific maximum values. You cannot use additionalModelRequestFields to bypass the FM's context limit.
105. Frage
A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to 40% failure rates. The tools perform inconsistently for different teams at the company.
A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead.
Which solution will meet these requirements?
Antwort: C
Begründung:
Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation.
AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.
Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.
Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling APIs with Lambda adds custom logic and operational overhead. Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the "built-in Amazon Bedrock capabilities" and
"minimal overhead" requirements. Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing. Therefore, option A is the most AWS-aligned solution.
106. Frage
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)
Antwort: A,E,F
107. Frage
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