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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.
Topic 4	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.
Topic 5	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.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ' s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?

A. Use Amazon Transcribe to convert speech to text and to perform real-time analytics. Use Amazon Comprehend to perform sentiment analysis. Use Amazon SQS to queue processing tasks. Run the Amazon Bedrock InvokeModel operation to generate responses.
B. Use the Amazon Transcribe streaming API with standard settings to convert speech to text. Use Amazon Bedrock batch processing to perform inference. Store call recordings and metadata in Amazon S3. Use S3 Lifecycle policies to manage the storage.
C. Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table.
D. Use Amazon Transcribe batch processing to perform post-call analysis. Configure AWS Lambda functions to generate responses by using the Amazon Bedrock InvokeModel operation. Use Amazon CloudWatch to log supervisor feedback.

Answer: C

Explanation:
To achieve the ultra-low latency requirement of less than 500 ms, the system must utilize streaming capabilities at every stage. Using Amazon Transcribe streaming with small (100-ms) audio chunks ensures that transcription begins immediately as the customer speaks. On the model side, Amazon Bedrock's InvokeModelWithResponseStream allows the application to receive tokens as they are generated, rather than waiting for the entire completion, which is critical for real-time interactions. Amazon DynamoDB is the ideal choice for storing supervisor ratings during a live call because it provides the single-digit millisecond latency required for high-frequency writes without impacting the application ' s performance. Options involving batch processing or SQS queuing are unsuitable for sub-500 ms interactive requirements.

NEW QUESTION # 75
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) 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?

A. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
B. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
C. Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FMs through a single Amazon API Gateway REST API endpoint.
D. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.

Answer: C

Explanation:
Option C cleanly separates rapidly changing routing configuration from application code. AWS AppConfig Agent is the AWS-recommended mechanism for retrieving configuration data and maintains a local cache while asynchronously polling AppConfig for updates. In Lambda environments, the extension periodically refreshes configuration in the background and lets functions retrieve cached values locally with very low latency. The Lambda function can therefore evaluate proprietary rules involving user tier, transaction value, Region, compliance constraints, and current model costs without redeployment. Model identifiers and routing thresholds can change independently through AppConfig while API Gateway exposes one stable application endpoint. Option A requires environment-variable modifications and Lambda configuration updates. Option B makes API Gateway transformation templates responsible for complex business decisions. Option D incorrectly couples model-routing decisions with authorization logic and requires separate model-specific Lambda functions, reducing flexibility. AWS Documentation

NEW QUESTION # 76
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?

A. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
B. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
C. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.
D. Use Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.

Answer: D

Explanation:
Option B is the correct solution because legal research workloads require both semantic understanding and exact lexical precision, especially for statutes, citations, and domain-specific terminology. A hybrid search architecture directly addresses this need by combining vector similarity search with traditional keyword-based retrieval.
Vector search alone is often insufficient for legal research because exact phrases, citation formats, and jurisdiction-specific terms must be matched precisely. Keyword search ensures high recall and precision for citations and legal terms, while vector search captures deeper semantic relationships between legal concepts, precedents, and arguments. Amazon OpenSearch Service natively supports hybrid search, enabling efficient scoring and ranking without external orchestration.
Applying an Amazon Bedrock reranker model further improves relevance by reordering retrieved documents based on deeper contextual understanding. Reranking is especially valuable in legal research because multiple documents may appear relevant, but only a subset truly addresses the user's legal question. The reranker optimizes final results before they are passed to the Anthropic Claude FM, improving answer accuracy and reducing hallucinations.
Option A relies on default vector search, which does not reliably handle citations and exact terminology.
Option C focuses on query suggestions and post-processing rather than retrieval quality. Option D introduces unnecessary operational complexity by merging results across multiple systems.
Therefore, Option B best meets the requirements for precision, performance, and semantic understanding in a legal research AI assistant.

NEW QUESTION # 77
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?

A. Create a metadata file named metadata.json at the top level of the S3 bucket. Add an AccessControlList object to the file that specifies the S3 path of each department's prefix. Specify the IAM Identity Center group that should have access to each department's prefix. Reference the file location in the data source metadata settings.
B. Create a single JSON file named acl.json at the top level of the S3 bucket. Add access control entries that map each department's S3 prefix to its corresponding IAM Identity Center group. Indicate the location of the JSON file in the Access Control section of the data source settings.
C. For each IAM Identity Center group, create a separate permissions set that denies access to all prefixes in the S3 bucket. Add a StringNotEquals condition key to the permissions set for each group that specifies the department each group is associated with. Attach the permissions sets to the Identity Center groups.
D. Create a JSON file named acl.json in each department folder. In each file, create access control entries that specify the IAM Identity Center group that should have access to that department's data. Indicate the location of the JSON file in the Access Control section of the data source settings.

Answer: B

Explanation:
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.

NEW QUESTION # 78
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?

A. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM.
B. Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt.
C. Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context.
D. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH.

Answer: A

Explanation:
When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or " wrappers " are highly effective. By explicitly defining where the promotional message should appear (e.g., " The response must end with the following message:
[PROMO TEXT] " ) or providing an example output structure, the developer reinforces the constraint within the model ' s generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.

NEW QUESTION # 79
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