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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Real Exam Qty: | 85 |
| Exam Price: | $300 USD |
| Available Languages: | English |
| Related Certifications: | AWS Certified AI Practitioner |
| Certificate Validity Period: | 3 years |
| Passing Score: | 750 (on a scale of 100-1000) |
| Exam Format: | Ordering, Multiple Response, Matching, Multiple Choice |
| 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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52. Frage
A cooking spice company is launching a marketing campaign that will invite customers to upload videos of themselves cooking with the company ' s spices to a platform that the company operates. The company is designing a solution for the video-sharing platform.
The solution must extract video transcripts and generate embeddings to help users search for videos based on spices, other ingredients, and cooking methods. The solution must integrate with the company ' s existing open-source chat system so users can interact with videos in real time. The solution must analyze user chat text for sentiment to determine the most positively received videos. Each month, the recipe videos that received the most positive feedback and the highest number of views will be displayed on the platform.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select THREE.)
Antwort: A,B,F
Begründung:
Options A, D, and E provide the required capabilities while minimizing custom infrastructure. Amazon Transcribe converts speech in uploaded media into text, Amazon Comprehend provides managed sentiment detection, and Bedrock Knowledge Bases can support embedding-based semantic retrieval over transcript content. DynamoDB is appropriate for lightweight, scalable counters such as views and positive-feedback totals and avoids operating a relational vector database solely for metrics. The Strands Agents SDK provides prebuilt HTTP-request tooling for invoking the existing chat API and supports tool-based and multi-agent orchestration patterns, reducing bespoke integration code. Option B adds Step Functions and Lambda when Strands can coordinate the required application interactions directly. Option C requires custom orchestration and error-management code. Option F introduces Aurora PostgreSQL and pgvector even though vector retrieval is already handled through the Bedrock knowledge-base layer. AWS Documentation
53. Frage
A software company is using Amazon Q Business to build an AI assistant that allows employees to access company information and personal information by using natural language prompts. The company stores this information in an Amazon S3 bucket.
Each department in the company has a dedicated prefix in the S3 bucket. Each object name includes the S3 prefix of the department that it belongs to. Each department can belong to only a single group in AWS IAM Identity Center. Each employee belongs to a single department.
The company configures Amazon Q Business to access data stored in an S3 bucket as a data source. The company needs to ensure that the AI assistant respects access controls based on the user's IAM Identity Center group membership.
Which solution will meet this requirement with the LEAST operational overhead?
Antwort: D
Begründung:
Option B is the correct solution because Amazon Q Business natively supports access control lists (ACLs) for S3 data sources using a single, centralized JSON file that maps S3 prefixes to IAM Identity Center groups.
This approach directly aligns with the company's data organization model, where each department's data is stored under a distinct S3 prefix and each employee belongs to exactly one department group.
Using a single acl.json file at the bucket root minimizes operational overhead by centralizing access control logic in one location. Administrators can update department mappings without touching individual folders or changing IAM permissions, which simplifies governance and reduces the risk of configuration drift. Amazon Q Business automatically evaluates the user's IAM Identity Center group membership at query time and filters accessible documents accordingly.
Option A increases operational complexity by requiring a separate ACL file in every department folder, which becomes difficult to maintain as departments or prefixes change. Option C attempts to enforce access using IAM permissions sets, but Amazon Q Business access control for S3 data sources is not designed to be managed through IAM condition logic and would significantly increase complexity. Option D introduces a custom metadata structure that is not the supported mechanism for Amazon Q Business access enforcement.
Therefore, Option B provides the cleanest, most scalable, and AWS-recommended solution for enforcing department-based access control with the least operational effort.
54. Frage
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option A is the correct answer because Amazon Bedrock Flows is purpose-built to orchestrate generative AI workflows that combine data access, deterministic transformations, and model invocation with minimal operational overhead. The requirements explicitly state that the workflow must retrieve content from Amazon S3, extract a specific portion of a predefined template, send that portion to an Amazon Bedrock model, and store the model's response back into the same S3 bucket. Amazon Bedrock Flows natively supports all of these steps.
By configuring S3 action nodes at the beginning and end of the flow, the workflow can retrieve the original communications and persist the reviewed output without custom code. The expression step allows deterministic parsing of a specific portion of the template, which is essential when only part of the message should be reviewed. This avoids relying on generative logic for parsing, which would be less predictable and harder to audit. The agent step is then used specifically for the review task, where the foundation model evaluates or modifies the extracted content.
Option B uses AWS Step Functions, which can achieve similar outcomes but requires more explicit orchestration logic and does not provide GenAI-native constructs such as expressions and agent steps in a single managed experience. Options C and D rely on Amazon Bedrock agents and AWS Lambda functions to handle parsing and data movement, which increases complexity, operational overhead, and maintenance burden.
Because Amazon Bedrock Flows directly integrates S3 actions, parsing expressions, and model review steps in a single managed workflow, Option A best meets the requirements with the least development and operational effort.
55. Frage
A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5-100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.
Which solution will meet these requirements?
Antwort: D
Begründung:
Option B best satisfies the requirements because it directly applies Retrieval Augmented Generation principles using managed Amazon Bedrock Knowledge Bases, which are designed to handle large, complex documents while preserving contextual relationships. Financial reports often interleave tables with explanatory narrative, and accurate analysis depends on keeping those elements logically connected. By segmenting documents based on their structural layout-for example, sections, subsections, tables, and surrounding commentary-the knowledge base can retrieve semantically relevant chunks that maintain this relationship during inference.
Amazon Bedrock Knowledge Bases support contextual chunking strategies that go beyond simple fixed-size segmentation. This is critical for financial documents, where a metric in a table may be explained in adjacent paragraphs or footnotes. Context-aware chunking ensures that retrieved content includes both the numeric data and its interpretation, enabling the foundation model to generate accurate, grounded responses. Including citations further improves analyst trust and auditability by allowing users to trace answers back to specific source sections, which is a common requirement in financial environments.
Scalability is another key requirement. Knowledge Bases manage embedding generation, indexing, and retrieval orchestration as a managed service, which allows the solution to scale across large document collections without requiring custom infrastructure or model hosting. This approach also supports efficient updates as new quarterly reports are added, ensuring the retrieval layer remains current.
Option A does not scale well because processing entire 5-100 page documents in a single prompt increases token usage, latency, and cost while risking context truncation. Option C relies on fixed-size chunking triggered at query time, which often breaks semantic relationships in structured financial content. Option D introduces unnecessary architectural complexity by splitting structured and unstructured data into separate applications, increasing operational overhead without providing better contextual retrieval than a unified RAG approach.
56. Frage
A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: B
Begründung:
Option A best satisfies the requirement to capture multi-hop, highly interconnected relationships with minimal operational overhead. Traditional vector similarity search excels at finding semantically similar text but is not optimized for reasoning over explicit entity-to-entity relationships, especially when analysts need indirect, multi-hop connections (for example, fund # holding # issuer # sector # regulation). Graph-based retrieval is designed specifically for these kinds of relationship traversals.
GraphRAG combines retrieval-augmented generation with graph-aware context selection. By representing entities and their relationships in a graph store, the system can traverse multiple hops to assemble a holistic set of relevant facts. This improves completeness and reduces the chance that the model misses indirect relationships that are essential for accurate investment guidance.
Amazon Neptune Analytics provides a managed graph analytics environment capable of efficiently traversing and analyzing complex relationship networks. When integrated with Amazon Bedrock Knowledge Bases, it reduces custom engineering by providing managed ingestion, retrieval, and orchestration patterns suitable for GenAI applications. This lowers operational overhead compared to building and maintaining custom multi- stage retrieval logic.
Meeting the sub-3-second requirement is also more feasible with a graph-optimized engine because multi-hop traversals can be executed efficiently compared to chaining multiple vector searches and joining results in an application layer. The managed nature of Knowledge Bases and Neptune Analytics reduces maintenance, scaling, and operational burden while enabling strong performance.
Option B and C require extensive custom logic and orchestration, increasing complexity and latency. Option D is not designed for graph-style multi-hop exploration and would require significant custom indexing and retrieval logic.
Therefore, Option A is the most AWS-aligned and operationally efficient approach for multi-hop relationship- aware RAG with strong performance.
57. Frage
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