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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 (on a scale of 100-1000) |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Exam Format: | Ordering, Matching, Multiple Choice, Multiple Response |
| Exam Price: | $300 USD |
| Related Certifications: | AWS Certified AI Practitioner |
| Real Exam Qty: | 85 |
| Exam Duration: | 180 minutes |
| 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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NEW QUESTION # 39
A GenAI developer is evaluating Amazon Bedrock foundation models (FMs) to enhance a Europe-based company's internal business application. The company has a multi-account landing zone in AWS Control Tower. The company uses Service Control Policies (SCPs) to allow its accounts to use only the eu-north-1 and eu-west-1 Regions. All customer data must remain in private networks within the approved AWS Regions.
The GenAI developer selects an FM based on analysis and testing and hosts the model in the eu-central-1 Region and the eu-west-3 Region. The GenAI developer must enable access to the FM for the company's employees. The GenAI developer must ensure that requests to the FM are private and remain within the same Regions as the FM.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C is the correct solution because it uses Amazon Bedrock cross-Region inference profiles, which are explicitly designed to support regional data residency, private connectivity, and resilience with minimal operational overhead.
By using a Europe-scoped inference profile, the application ensures that all inference requests are routed only within European Regions where the FM is deployed, such as eu-central-1 and eu-west-3. This satisfies data residency requirements while still providing resilience and load distribution across Regions.
Configuring an Amazon Bedrock VPC endpoint ensures that all traffic remains on the AWS private network.
No public endpoints are used, which aligns with the company's private networking requirements.
Extending existing SCPs to allow inference profile usage ensures that employees can access the FM only in approved Regions, maintaining governance across the Control Tower environment.
Options A and B introduce unnecessary custom routing layers and EC2 management. Option D moves away from Amazon Bedrock entirely and increases operational complexity.
Therefore, Option C is the only solution that satisfies private access, regional confinement, governance controls, and low operational overhead.
NEW QUESTION # 40
A company wants to implement an Amazon Bedrock application that helps lawyers search thousands of legal documents. The solution must give lawyers the ability to query based on the type of legal case, jurisdiction, and case outcome. Queries must return information about document authors and versions. The solution must integrate with the company ' s existing tools and support future growth in volume and query complexity.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B is correct because Amazon Bedrock Knowledge Bases supports structured document metadata that can be stored separately from the text and used as filters during retrieval. Case type, jurisdiction, and case outcome are ideal filterable metadata attributes, while author and version fields preserve provenance for returned documents. AWS documentation confirms that S3 knowledge-base data sources can use sidecar metadata files and that metadata remains available for filtering even when it is excluded from the embedding.
This gives the application precise semantic retrieval plus structured constraints without custom extraction at query time. Option A lacks structured filtering. Option C adds unnecessary Comprehend processing. Option D embeds metadata inside document text, which does not provide reliable field-level filtering and adds unnecessary agent-side extraction complexity. AWS Documentation
NEW QUESTION # 41
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.
NEW QUESTION # 42
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?
Answer: 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.
NEW QUESTION # 43
A company is using Amazon Bedrock to build an AI assistant to help internal teams analyze unstructured customer feedback data. The company stores the customer feedback in an Amazon S3 bucket. The S3 bucket contains more than 25 TB of historical data from mobile app reviews, chat conversations, and call center transcripts. The company expects the data source to grow by 3 GB every day. The data entries often contain multiple unrelated topics within the same input.
The company needs a solution that reliably delivers accurate answers to questions based on the data source.
The solution must not export any personally identifiable information (PII) to the Amazon Bedrock model during processing or response generation.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option A is the only listed choice that satisfies the hard requirement that PII must not reach the Bedrock model. AWS explicitly states that Knowledge Base guardrails apply to the user input and generated model response, but not to references retrieved from the Knowledge Base at runtime. Therefore, Options B, C, and D can still place PII-bearing retrieved chunks into model context even though a guardrail is configured. Amazon Comprehend can detect PII, enabling the Lambda preprocessing step in A to remove sensitive data before embeddings are created and before retrieved context is ever sent to the foundation model. Although semantic chunking would normally be preferable for records containing multiple unrelated topics, privacy is a mandatory constraint. Option A has more custom processing, but it is the only option that enforces PII removal upstream of model invocation.
NEW QUESTION # 44
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