Amazon AIP-C01 Prüfung Übungen und Antworten

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Amazon AIP-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Generative AI Developer - Professional
Exam Number:AIP-C01
Related Certifications:AWS Certified AI Practitioner
Available Languages:English
Exam Format:Matching, Multiple Response, Ordering, Multiple Choice
Passing Score:750 (on a scale of 100-1000)
Exam Price:$300 USD
Real Exam Qty:85
Exam Duration:180 minutes
Certificate Validity Period:3 years
Sample Questions:Amazon AIP-C01 Sample Questions
Exam Way:Online proctored exam or test center (Pearson VUE)
Pre Condition:Recommended 3+ years of experience in software development and 1+ year of experience building applications with generative AI on AWS. AWS Cloud Practitioner certification or equivalent cloud knowledge recommended.
Official Syllabus URL:https://aws.amazon.com/certification/certified-generative-ai-developer-professional/

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Amazon AIP-C01 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • AI Safety, Security, and Governance: This domain addresses input
  • output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.
Thema 2
  • Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Thema 3
  • Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Thema 4
  • Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Thema 5
  • Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.

Amazon AWS Certified Generative AI Developer - Professional AIP-C01 Prüfungsfragen mit Lösungen (Q100-Q105):

100. Frage
A healthcare company wants to develop a proof-of-concept application that uses Amazon Bedrock to automatically summarize medical documents. The company has 3 weeks to validate the application ' s accuracy. The application must comply with the company's data privacy policies. The application must include metrics to evaluate summarization accuracy and processing time. Which solution will meet these requirements?

Antwort: A

Begründung:
For a 3-week proof-of-concept in a regulated field like healthcare, Retrieval Augmented Generation (RAG) is more efficient and safer than fine-tuning. RAG allows the use of anonymized patient records without risking the leak of sensitive data into the model ' s permanent memory. To evaluate accuracy quantitatively and rapidly, the " LLM-as-a-judge " pattern is recommended. Using a strong judge model to score the outputs of multiple candidate FMs provides objective metrics (e.g., factual alignment, completeness) that manual qualitative feedback (Option C) cannot scale to provide within the timeline. Fine-tuning (Option B) typically takes longer than 3 weeks to properly data-prep and validate for clinical accuracy.


101. Frage
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?

Antwort: C

Begründung:
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.


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: C

Begründung:
Amazon SageMaker Serverless Inference is specifically designed for applications that experience intermittent or bursty traffic. It automatically scales compute capacity based on the number of requests and scales down to zero when there is no traffic, satisfying the requirement to optimize resource usage during low demand. To meet the 500 ms latency requirement during peak periods and avoid " cold start " delays, provisioned concurrency keeps a specified number of execution environments warm and ready to respond immediately. This provides a balance between the cost-effectiveness of serverless and the performance predictability of provisioned instances. Multi-model endpoints (Option A) can introduce " noisy neighbor " issues and latency spikes, while asynchronous inference (Option D) is intended for long-running workloads and cannot meet sub-500 ms requirements.


103. Frage
A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company's employees. The application must display AI- generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.
Which solution will meet these requirements?

Antwort: A

Begründung:
This requirement explicitly calls for character-by-character streaming, long-running responses, low latency, and massive concurrency, which aligns directly with Amazon Bedrock streaming inference patterns.
Amazon Bedrock provides the InvokeModelWithResponseStream API specifically for streaming partial model outputs as tokens are generated. This enables near-instant feedback to users instead of waiting for the full response to complete, which is essential when responses last up to 45 seconds.
Amazon API Gateway WebSocket APIs are purpose-built for bidirectional, low-latency, server-initiated communication, allowing the backend to push characters or tokens to clients in real time. This eliminates inefficient polling and supports thousands of concurrent open connections.
AWS Lambda integrates natively with WebSocket APIs and scales automatically with connection volume, enabling a fully managed, serverless architecture. This approach maintains security, centralized authentication, throttling, and observability while avoiding direct client access to Bedrock APIs.
Option B introduces polling latency and unnecessary API overhead and does not provide true streaming.
Option C violates AWS security best practices by exposing Bedrock directly to clients and does not scale securely. Option D only serves completed responses and cannot meet the real-time streaming requirement.
Therefore, Option A is the only solution that fully satisfies streaming behavior, concurrency, latency, and managed-service constraints.


104. Frage
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

Antwort: C


105. Frage
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