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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Exam Price: | 300 USD |
| Available Languages: | English, Korean, Simplified Chinese, Japanese |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Exam Format: | Multiple choice, Ordering, Matching, Multiple response |
| Exam Duration: | 180 minutes |
| Certificate Validity Period: | 3 years |
| Passing Score: | 750 (scaled score 100–1000) |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| 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 |
Die Kandidaten können die Schulungsunterlagen zur Amazon AIP-C01 Zertifizierungsprüfung von ITZert in einer Simulationsumgebung lernen. Sie können die Prüfungssorte und die Testzeit kontrollieren. In ITZert können Sie sich ohne Druck und Stress gut auf die Amazon AIP-C01 Prüfung vorbereiten. Zugleich können Sie auch einige häufige Fehler vermeiden. So werden Sie mehr Selbstbewusstsein in der Amazon AIP-C01 Prüfung haben. In der realen Prüfung können Sie Ihre Erfahrungen wiederholen, um Erfolg in der Prüfung zu erzielen.
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30. Frage
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.
31. Frage
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) 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?
Antwort: B
Begründung:
Option C is the correct solution because AWS AppConfig is designed for real-time, validated, centrally managed configuration changes with safe rollout, immediate propagation, and rollback support-exactly matching the company's requirements.
By storing routing rules, cost thresholds, regulatory constraints, and A/B testing logic in AWS AppConfig, the company can switch between Amazon Bedrock foundation models without redeploying Lambda code.
AppConfig supports feature flags, dynamic configuration updates, JSON schema validation, and staged rollouts, which are essential for safely managing complex and frequently changing routing logic.
Using the AWS AppConfig Agent, Lambda functions can retrieve cached configurations efficiently, ensuring low latency even under thousands of concurrent requests. This approach allows the Lambda function to apply proprietary business logic-such as user tier, transaction value, Region compliance, and real-time cost metrics-before selecting the appropriate FM.
Option A is operationally fragile because environment variable changes require function restarts and do not support validation or controlled rollouts. Option B is too limited for complex, dynamic logic and is difficult to maintain at scale. Option D misuses Lambda authorizers, which are intended for authentication and authorization, not high-frequency dynamic routing decisions.
Therefore, Option C provides the most scalable, flexible, and low-overhead architecture for dynamic, regulation-aware FM routing in a global GenAI system.
32. Frage
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?
Antwort: D
Begründung:
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.
33. Frage
A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company wants to manage these controls centrally.
Which combination of solutions will meet these requirements? (Select TWO.)
Antwort: A,C
Begründung:
The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.
To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.
To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.
Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.
By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.
34. Frage
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option A is the only choice that simultaneously addresses all three requirements: (1) higher output consistency for identical inputs, (2) sub-1-second performance, and (3) validated safety controls that block unsafe or hallucinated recommendations.
Provisioned throughput in Amazon Bedrock reserves capacity for the chosen model, which helps stabilize latency and reduces the chance of throttling or variable response times across Regions. This is important for a mobile app with strict latency goals and users distributed across multiple Regions. While provisioned throughput primarily improves performance predictability, it also reduces variability caused by contention during peak demand.
Amazon Bedrock guardrails provide validated safety controls to filter or block unsafe content. Semantic denial rules are appropriate for preventing dangerous brewing guidance (for example, excessively high temperatures) and for reducing hallucinated instructions that violate safety policies. Guardrails can be enforced consistently regardless of prompt-chain complexity, providing a uniform safety layer around the model outputs.
Amazon Bedrock Prompt Management supports controlled prompt versioning and approval workflows. By standardizing prompts, controlling changes, and ensuring the same prompt version is used for identical inputs, the company improves output stability and reduces drift caused by unmanaged prompt edits. Combined with strict configuration control (including fixed inference parameters such as temperature where appropriate), this improves repeatability and increases the likelihood of achieving the 99.5% consistency target.
Option B improves observability and experimentation but does not provide strong safety enforcement or latency stabilization. Option C improves performance through caching and tracing but does not provide validated safety controls and does not directly address cross-Region output consistency. Option D may improve retrieval but does not enforce safety controls or ensure repeatable outputs.
Therefore, Option A best meets the stability, performance, and safety requirements using AWS-native controls.
35. Frage
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