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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Passing Score: | 750 (scaled score 100–1000) |
| Exam Price: | 300 USD |
| Exam Duration: | 180 minutes |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Related Certifications: | AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner |
| Exam Format: | Multiple response, Matching, Multiple choice, Ordering |
| Available Languages: | English, Simplified Chinese, Korean, Japanese |
| Certificate Validity Period: | 3 years |
| Recommended Training: | AWS Skill Builder - Official Training AWS Certified Generative AI Developer - Professional Exam Guide |
| Exam Registration: | AWS Certification Registration Pearson VUE Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html |
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질문 # 96
A wildlife conservation agency operates zoos globally. The agency uses various sensors, trackers, and audiovisual recorders to monitor animal behavior. The agency wants to launch a generative AI (GenAI) assistant that can ingest multimodal data to study animal behavior.
The GenAI assistant must support natural language queries, avoid speculative behavioral interpretations, and maintain audit logs for ethical research audits.
Which solution will meet these requirements?
정답：C
설명：
Option B best meets the multimodal, ethical, and auditability requirements using managed AWS services designed for research-grade GenAI systems. Multimodal data such as audio, video, sensor telemetry, and tracking data must be curated and summarized before being consumed by a foundation model. Amazon SageMaker Processing and Amazon Transcribe provide scalable, managed preprocessing for audiovisual and textual data.
By ingesting summarized, validated observations into Amazon Bedrock Knowledge Bases, the GenAI assistant can answer natural language queries using grounded, evidence-based context instead of raw sensor signals. This significantly reduces the risk of speculative or anthropomorphic interpretations.
Amazon Bedrock guardrails are critical for preventing speculative behavioral claims, enforcing scientific and ethical constraints at inference time. Guardrails provide a validated, auditable safety layer that custom Lambda-based filters cannot reliably replicate.
AWS AppConfig enables controlled prompt management and change governance, ensuring that research prompts remain consistent and reviewable. AWS CloudTrail captures all access, query, and configuration changes, supporting ethical research audits and regulatory reviews.
Option A lacks grounding and speculative safeguards. Option C focuses on text analytics and does not properly handle multimodal reasoning or safety enforcement. Option D relies heavily on custom logic and introduces unnecessary operational risk.
Therefore, Option B provides the most robust, ethical, and auditable GenAI architecture for wildlife behavior research.
질문 # 97
A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods.
The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics.
Which solution will meet these requirements?
정답：A
설명：
Option B best meets the combined resiliency and observability requirements because it applies AWS- recommended retry behavior for transient throttling and enables true distributed tracing across service boundaries. During peak traffic, intermittent failures are commonly caused by throttling and other transient conditions. The AWS SDK standard retry mode provides exponential backoff with jitter, which reduces synchronized retry storms, prevents cascading failures, and improves overall system stability. Jitter is important because it spreads retry attempts over time, reducing load amplification during throttling events.
For observability, AWS X-Ray provides distributed tracing that follows a request across components such as API Gateway or load balancers, application services, and downstream calls to Amazon Bedrock. X-Ray can identify where latency is being introduced and which downstream call is contributing most to end-to-end response time. This is required to "identify latency contributors" and isolate performance issues under load.
The requirement also states that the company must correlate performance issues with specific FM characteristics. X-Ray annotations are designed for this purpose: the application can annotate traces with the model ID, inference parameters, region, or inference profile used. This enables filtering and analysis (for example, comparing latency or error patterns by model, parameter set, or endpoint configuration) without building a separate telemetry system.
Option A's fixed-delay retries increase synchronized retry behavior and do not provide distributed tracing.
Option C does not prevent cascading failures and cannot provide cross-service tracing. Option D is incorrect because CloudTrail is an audit logging service and does not provide distributed tracing for request latency analysis.
Therefore, Option B provides the correct combination of resilient retries and deep, model-correlated distributed observability for Amazon Bedrock workloads.
질문 # 98
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs.
The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
정답：A
설명：
Option C cleanly separates rapidly changing routing configuration from application code. AWS AppConfig Agent is the AWS-recommended mechanism for retrieving configuration data and maintains a local cache while asynchronously polling AppConfig for updates. In Lambda environments, the extension periodically refreshes configuration in the background and lets functions retrieve cached values locally with very low latency. The Lambda function can therefore evaluate proprietary rules involving user tier, transaction value, Region, compliance constraints, and current model costs without redeployment. Model identifiers and routing thresholds can change independently through AppConfig while API Gateway exposes one stable application endpoint. Option A requires environment-variable modifications and Lambda configuration updates. Option B makes API Gateway transformation templates responsible for complex business decisions. Option D incorrectly couples model-routing decisions with authorization logic and requires separate model-specific Lambda functions, reducing flexibility. AWS Documentation
질문 # 99
A financial services company is developing a real-time generative AI (GenAI) assistant to support human call center agents. The GenAI assistant must transcribe live customer speech, analyze context, and provide incremental suggestions to call center agents while a customer is still speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency under 1 second from speech to initial response display.
The architecture must use only managed AWS services and must support bidirectional streaming to ensure that call center agents receive updates in real time.
Which solution will meet these requirements?
정답：D
설명：
Option B is the only solution that satisfies all strict real-time, streaming, and latency requirements. Amazon Transcribe streaming with partial results allows transcription fragments to be delivered before the speaker finishes a sentence. This significantly reduces perceived latency and enables downstream processing to begin immediately, which is essential for maintaining sub-1-second end-to-end response times.
Using Amazon Bedrock's InvokeModelWithResponseStream API enables token-level or chunk-level streaming responses from the foundation model. This allows the GenAI assistant to begin delivering suggestions to call center agents incrementally instead of waiting for a full model response. This streaming inference capability is critical for interactive, real-time agent assistance use cases.
Amazon API Gateway WebSocket APIs provide fully managed, bidirectional communication between backend services and agent dashboards. This ensures that updates flow continuously to agents as new transcription fragments and model outputs become available, preserving real-time responsiveness without requiring custom socket infrastructure.
Option A introduces additional synchronous processing layers and storage writes that increase latency. Option C uses batch transcription and post-call processing, which cannot meet real-time requirements. Option D uses embeddings and asynchronous messaging, which are not suitable for live incremental suggestions and bidirectional streaming.
Therefore, Option B best aligns with AWS real-time GenAI architecture patterns by combining streaming transcription, streaming model inference, and managed bidirectional communication while maintaining low latency and operational simplicity.
질문 # 100
Company configures a landing zone in AWS Control Tower. The company handles sensitive data that must remain within the European Union. The company must use only the eu-central-1 Region. The company uses Service Control Policies (SCPs) to enforce data residency policies. GenAI developers at the company are assigned IAM roles that have full permissions for Amazon Bedrock.
The company must ensure that GenAI developers can use the Amazon Nova Pro model through Amazon Bedrock only by using cross-Region inference (CRI) and only in eu-central-1. The company enables model access for the GenAI developer IAM roles in Amazon Bedrock. However, when a GenAI developer attempts to invoke the model through the Amazon Bedrock Chat/Text playground, the GenAI developer receives the following error:
User arn:aws:sts:123456789012:assumed-role/AssumedDevRole/DevUserName
Action: bedrock:InvokeModelWithResponseStream
On resource(s): arn:aws:bedrock:eu-west-3::foundation-model/amazon.nova-pro-v1:0 Context: a service control policy explicitly denies the action The company needs a solution to resolve the error. The solution must retain the company's existing governance controls and must provide precise access control. The solution must comply with the company's existing data residency policies.
Which combination of solutions will meet these requirements? (Select TWO.)
정답：C,E
설명：
This error occurs because SCPs override IAM permissions, and the SCP currently blocks Bedrock inference calls that resolve to eu-west-3, even though the company intends to use cross-Region inference (CRI) from eu- central-1.
Amazon Nova Pro is not hosted in eu-central-1, so when invoked, Amazon Bedrock transparently routes the request to a supporting Region (such as eu-west-3) through CRI inference profiles. However, SCPs that restrict Regions or specific Bedrock resources will block this routing unless explicitly allowed.
Option B is required because the SCP must explicitly allow the eu.amazon.nova-pro-v1:0 inference profile, which is the Bedrock abstraction that enables CRI while preserving data residency guarantees. Without this, Bedrock cannot legally route the request.
Option E is also required to allow EU-scoped inference profiles rather than individual Regions. This preserves precise governance while allowing Bedrock-managed CRI routing within the EU boundary, ensuring no data leaves Europe.
Option A violates least-privilege and does not override SCPs. Option C breaks data residency by enabling direct eu-west-3 access. Option D does not resolve the SCP denial.
Therefore, Options B and E are the only combination that resolves the error while preserving governance and EU-only data residency.
질문 # 101
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AIP-C01인증시험 인기 덤프자료: https://www.passtip.net/AIP-C01-pass-exam.html
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