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>> Latest AIP-C01 Exam Questions <<
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NEW QUESTION # 69
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A is the correct solution because it provides proactive, model-aware token management with fine- grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units.
By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures.
Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units-an essential requirement for regulatory reporting and internal chargeback.
Option B is incorrect because Amazon Bedrock Guardrails do not currently provide token quota enforcement or proactive token alerts. Option C is reactive and only analyzes failures after they occur. Option D throttles requests but cannot enforce token-based limits or provide per-model cost attribution.
Therefore, Option A best satisfies proactive alerting, scalability, compliance reporting, and cost allocation requirements with acceptable operational effort.
NEW QUESTION # 70
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, 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?
Answer: C
Explanation:
Option D is the intended Bedrock prompt-engineering solution. AWS guidance recommends using output indicators to explicitly describe constraints and characteristics that the model should follow when constructing its response. The prompt should clearly state that the promotional message is mandatory, where it should appear, and how it should be integrated with the surrounding recommendation. This gives the model a stronger output constraint than an indirect instruction embedded elsewhere in a dynamically assembled prompt. Option A uses Guardrails, which are designed to evaluate and constrain unsafe or disallowed content rather than require insertion of specific marketing text. Option B adds unnecessary model calls and still does not enforce the desired response structure. Option C introduces additional AgentCore and reranking processing for a requirement that should be addressed directly through explicit output instructions in the prompt. AWS Documentation
NEW QUESTION # 71
A GenAI developer is using Amazon Bedrock to build an application. The application needs to interpret complex user requests in natural language, maintain semantic context across multiple interactions with the same user, and dynamically invoke more than 50 enterprise APIs based on reasoning rather than predefined workflows.
The application must autonomously coordinate backend tools and provide explainable results for compliance reviews. The GenAI developer wants to use Amazon Bedrock AgentCore to implement dynamic orchestration.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is correct because the requirement is agentic, dynamic orchestration rather than a predefined workflow. AgentCore Memory can preserve conversational context, while AgentCore Gateway provides a unified MCP interface through which an agent can discover and invoke many tools and backend APIs. AWS documents support multiple Gateway targets and consolidated tool discovery, so adding or selecting enterprise capabilities does not require hard-coding every workflow path. AgentCore observability also exposes detailed traces covering processing steps, tool invocations, decision points, errors, and outputs, which supports compliance review and explainability. Option A is fundamentally static orchestration. Option C substitutes retrieval for API execution and still requires custom Lambda orchestration. Option D uses guardrails for a purpose they are not designed to perform; guardrails constrain content but are not a dynamic enterprise-tool orchestration engine. AWS Documentation
NEW QUESTION # 72
A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
Option B is the most appropriate solution because it uses AWS-native, purpose-built data engineering and governance services to address data quality validation, metadata creation, monitoring, and transformation with minimal custom development. AWS Glue is designed specifically for large-scale data preparation and integrates seamlessly with Amazon S3, making it ideal for preprocessing unstructured datasets for downstream GenAI applications.
AWS Glue crawlers automatically infer schemas and populate the AWS Glue Data Catalog, creating auditable, queryable metadata for all datasets. This satisfies the requirement for traceability and governance, which is especially critical in financial services environments. Glue ETL jobs allow teams to implement customizable transformation logic, including text normalization and chunking strategies optimized for foundation model context windows.
AWS Glue Data Quality provides built-in rulesets for validating completeness, accuracy, and consistency. It also publishes quality metrics that can be monitored over time, meeting the requirement for ongoing data quality monitoring without building custom validation frameworks.
Because AWS Glue is fully managed, it eliminates the need to manage infrastructure, scaling, or orchestration. This significantly reduces development and operational effort compared to custom Lambda pipelines or EC2-based processing. The processed and validated data can then be safely ingested into Amazon Bedrock workflows or knowledge bases.
Option A and C require custom logic for validation, monitoring, and chunking, increasing development complexity. Option D introduces unnecessary infrastructure management and services not optimized for data preprocessing.
Therefore, Option B best meets the requirements while minimizing development effort and aligning with AWS Generative AI data preparation best practices.
NEW QUESTION # 73
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: D
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 # 74
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