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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 |
| Exam Format: | Multiple choice, Matching, Ordering, Multiple response |
| Passing Score: | 750 (scaled score 100โ1000) |
| Available Languages: | English, Simplified Chinese, Japanese, Korean |
| Exam Duration: | 180 minutes |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| 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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NEW QUESTION # 36
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described-answers citing irrelevant sections when a query spans multiple paper sections-often occurs when chunks are either too small (losing cross-paragraph context) or too "flat" (retrieving isolated snippets without their surrounding rationale).
In a scientific paper, related information is frequently distributed across methodology, results, and discussion.
Flat, fixed-size chunking (Option A) can split these logically connected ideas into separate chunks, causing retrieval to surface fragments that match a term but not the full intent. Semantic chunking (Option C) improves boundary placement, but it does not inherently provide a multi-resolution structure that helps preserve section-level continuity at massive scale.
Hierarchical chunking solves this by creating parent chunks (larger context windows) that capture broader section context and child chunks (smaller units) that retain retrieval precision. When the retriever identifies relevant child chunks, it can also bring in the associated parent context so the foundation model sees the surrounding methodological or discussion framing. The defined overlaps further reduce the risk that key transitions or references are split across chunks.
This approach is well suited for a corpus of 25 million papers because it improves relevance without requiring a custom reranking model or a manual preprocessing pipeline. It remains operationally efficient because it is configured at the knowledge base level rather than implemented through custom code per document.
Option D introduces high operational complexity and inconsistent document handling at scale. Therefore, Option B best meets the requirement to preserve semantic context across related paragraphs and improve citation relevance across scientific paper sections.
NEW QUESTION # 37
A company is building a multicloud generative AI (GenAI)-powered secret resolution application that uses Amazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including key stores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrieve secrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. The application supports secret chaining and detects secret drift. The application handles short-lived and expiring secrets. The application also supports prompt flows for templated instructions. The application uses AWS Step Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection.
The company finds multiple issues during application testing. The application does not refresh expired secrets in time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance issues.
Which solution will meet this requirement?
Answer: C
Explanation:
Option A is the correct solution because it directly addresses all identified failure modes while preserving the existing Step Functions-based orchestration architecture with minimal redesign.
Using Step Functions Map states enables parallel execution of secret resolution workflows, which improves refresh latency for short-lived and expiring secrets. This ensures that secrets are refreshed in time before downstream agents require them. Passing updated secret metadata through Lambda outputs guarantees that subsequent steps always consume the latest resolved values, preventing agents from using stale data even after drift alerts are generated.
Versioning prompt flows in AWS AppConfig is critical to resolving cascading failures caused by outdated templates. AppConfig natively supports version control, validation, staged rollout, and rollback of configuration artifacts. By gating prompt flows through AppConfig, the company can immediately roll back faulty templates and prevent agents from reusing outdated instructions.
This solution maintains clear separation of concerns: Step Functions handle orchestration and parallelism, Lambda handles secret retrieval and metadata propagation, and AppConfig governs prompt lifecycle management. No additional event pipelines or custom retry coordination layers are required.
Option B oversimplifies the architecture and does not address secret lifecycle or drift. Option C introduces event-driven ordering complexity without solving prompt versioning. Option D introduces unnecessary tooling and dynamic prompt generation risk.
Therefore, Option A best resolves performance, correctness, and stability issues while minimizing operational overhead.
NEW QUESTION # 38
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgreSQL database.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
Option D requires the least development effort because it uses a managed retrieval workflow that bundles the most time-consuming parts of semantic search: embedding generation, vector indexing, and natural language retrieval. With an Amazon Bedrock knowledge base, the application does not need to implement and operate separate services to (1) generate embeddings for hundreds of millions of records, (2) store and manage vectors, (3) build query-time embedding conversion logic, and (4) implement k-NN search orchestration.
Instead, the knowledge base is configured to automatically create embeddings during ingestion, and the application queries it using the Amazon Bedrock Retrieve API, which accepts natural language input and performs the vector search as a managed capability.
The performance requirement (95% of queries within 500 ms) is best served by a purpose-built vector search backend rather than running similarity search directly inside a transactional PostgreSQL system at this scale.
A knowledge base is designed for retrieval patterns and can be backed by scalable vector stores, which helps meet latency goals under heavy concurrency. The hourly freshness requirement maps naturally to ingestion updates: the pipeline can re-ingest updated restaurant details on a schedule so the knowledge base remains current without building custom re-embedding workflows in application code.
Cost-effective scaling during peak periods is also easier with a managed retrieval layer because scaling the retrieval workload is separated from the operational database. This avoids overprovisioning PostgreSQL for peak semantic-search traffic and reduces the engineering effort to tune performance, sharding, indexing, and retry logic.
Options B and C can work, but they require the team to build and maintain embedding pipelines, query embedding generation, vector index management, and operational scaling strategies. Option A does not provide semantic search because it relies on keyword-based matching rather than embeddings.
NEW QUESTION # 39
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?
Answer: C
Explanation:
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.
NEW QUESTION # 40
A company uses AWS Lambda functions to build an AI agent solution. A GenAI developer must set up a Model Context Protocol (MCP) server that accesses user information. The GenAI developer must also configure the AI agent to use the new MCP server. The GenAI developer must ensure that only authorized users can access the MCP server.
Which solution will meet these requirements?
Answer: C
Explanation:
Option C is the correct solution because it provides a secure, scalable, and standards-compliant way to expose an MCP server to an AI agent while enforcing strong user authorization. The Model Context Protocol supports HTTP-based transports for remote MCP servers, making Streamable HTTP the appropriate choice when the server is hosted as a managed service rather than a local process.
Hosting the MCP server in AWS Lambda enables automatic scaling and cost-efficient execution. By placing Amazon API Gateway in front of the Lambda function, the company creates a secure, managed HTTP endpoint that the AI agent can invoke reliably. This architecture cleanly separates transport, authentication, and business logic, which aligns with AWS serverless best practices.
Using Amazon Cognito to enforce OAuth 2.1 ensures that only authenticated and authorized users can access the MCP server. This satisfies security and compliance requirements when the MCP server handles sensitive user information. Cognito integrates natively with API Gateway, removing the need for custom authentication logic and reducing operational overhead.
Option A lacks user-level authorization controls. Option B and Option D rely on STDIO transport, which is intended for local or tightly coupled processes and is not suitable for distributed, serverless architectures.
Option D also introduces security risks by handling credentials through environment variables.
Therefore, Option C best meets the requirements for secure access control, scalability, and correct MCP integration in an AWS-based AI agent architecture.
NEW QUESTION # 41
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