Free Amazon Web Services AIP-C01 Practice Exam with Questions & Answers

Comprehensive and Detailed 250 to 350 words of Explanation From AWS Generative AI concepts and services documents:

Option D is the best solution because it delivers a fully managed, scalable pipeline with minimal infrastructure management while meeting the 50 GB and 4-hour constraint. AWS Step Functions provides a serverless orchestration layer that can coordinate parallel processing steps, retries, and error handling without managing clusters or tuning long-running compute.

Using Amazon Comprehend for PII detection fulfills the requirement to remove customer PII in a managed and consistent way. Step Functions can coordinate Comprehend calls at scale and route sanitized outputs into the embedding step. Generating embeddings with Amazon Bedrock keeps the entire workflow within AWS managed services, eliminates the need to maintain custom embedding models, and supports consistent vector representations for downstream retrieval.

Direct integration with Amazon OpenSearch Serverless provides a low-operations vector store that can handle large-scale indexing and similarity search without cluster sizing, node maintenance, or shard management. This aligns strongly with the requirement for least operational overhead and supports growth beyond the initial 50 GB corpus. Step Functions can batch and parallelize ingestion into OpenSearch Serverless to meet the 4-hour completion goal in a cost-effective manner by controlling concurrency, chunk sizes, and failure handling.

Option A can be difficult and costly at this scale because Lambda concurrency and per-invocation overhead can become complex to tune for 50 GB within 4 hours. Option B introduces SageMaker Processing and embedding model management, increasing operational complexity. Option C requires EMR cluster management and tuning, which is the opposite of minimal overhead.

Therefore, Option D is the most operationally efficient, scalable, and managed approach to build the required PII-sanitized embedding pipeline for a RAG corpus.

Option B best meets the combined resiliency and observability requirements because it applies AWS-recommended retry behavior for transient throttling and enables true distributed tracing across service boundaries. During peak traffic, intermittent failures are commonly caused by throttling and other transient conditions. The AWS SDK standard retry mode provides exponential backoff with jitter, which reduces synchronized retry storms, prevents cascading failures, and improves overall system stability. Jitter is important because it spreads retry attempts over time, reducing load amplification during throttling events.

For observability, AWS X-Ray provides distributed tracing that follows a request across components such as API Gateway or load balancers, application services, and downstream calls to Amazon Bedrock. X-Ray can identify where latency is being introduced and which downstream call is contributing most to end-to-end response time. This is required to “identify latency contributors” and isolate performance issues under load.

The requirement also states that the company must correlate performance issues with specific FM characteristics. X-Ray annotations are designed for this purpose: the application can annotate traces with the model ID, inference parameters, region, or inference profile used. This enables filtering and analysis (for example, comparing latency or error patterns by model, parameter set, or endpoint configuration) without building a separate telemetry system.

Option A’s fixed-delay retries increase synchronized retry behavior and do not provide distributed tracing. Option C does not prevent cascading failures and cannot provide cross-service tracing. Option D is incorrect because CloudTrail is an audit logging service and does not provide distributed tracing for request latency analysis.

Therefore, Option B provides the correct combination of resilient retries and deep, model-correlated distributed observability for Amazon Bedrock workloads.

AWS Step Functions provides native support for human-in-the-loop workflows, making it the best fit for regulatory oversight requirements. The waitForTaskToken integration pattern is explicitly designed to pause a workflow until an external actor—such as a human reviewer—completes a task.

In this architecture, AI-generated recommendations are sent to a human technician for review. The workflow pauses execution using a task token. Once the technician approves or rejects the recommendation, an AWS Lambda function calls SendTaskSuccess or SendTaskFailure, allowing the workflow to continue deterministically.

This approach ensures full auditability, as Step Functions records every state transition, timestamp, and execution path. Storing review outcomes in Amazon DynamoDB provides durable, queryable audit records required for regulatory compliance.

Option A requires custom orchestration and lacks native workflow state management. Option C incorrectly uses AWS Glue, which is not designed for approval workflows. Option D uses caching instead of durable audit storage and introduces unnecessary complexity.

Therefore, Option B is the AWS-recommended, lowest-risk, and most auditable solution for mandatory human review of AI outputs.

Option A is the most comprehensive and architecturally aligned solution for meeting end-to-end data lineage, real-time PII filtering, and automated compliance reporting requirements in a medical GenAI system built on Amazon Bedrock. Each requirement maps directly to a managed AWS service that is purpose-built for governance, security, and compliance.

AWS Glue Data Catalog is designed to register datasets across multiple sources and maintain metadata that supports lineage tracking. By cataloging all inputs that flow into the Bedrock-based system, the organization can trace how data moves from ingestion through processing and storage, which is essential for regulatory audits in healthcare environments.

For real-time PII filtering, Amazon Bedrock Guardrails provide native PII detection and filtering during model inference. Guardrails operate inline with model invocation, ensuring sensitive information is blocked or redacted before responses are returned to users. This satisfies the requirement for real-time protection rather than post-processing analysis.

AWS CloudTrail delivers a complete audit trail of all Amazon Bedrock API calls, including InvokeModel requests and configuration changes. Storing these logs in Amazon S3 enables long-term retention and supports compliance audits. CloudTrail ensures traceability of who accessed the system, when, and how it was used.

To strengthen compliance monitoring, Amazon Macie continuously scans stored data for sensitive information and automatically classifies findings. Publishing Macie findings to Amazon CloudWatch Logs and visualizing them through dashboards enables near-real-time visibility into compliance posture and supports automated reporting workflows.

The other options fall short. Option B performs PII filtering at the application edge rather than at inference time and relies on scheduled analysis instead of real-time enforcement. Option C focuses on replication and document processing rather than inline GenAI governance. Option D uses services that are not designed for PII detection in text-based GenAI workflows and lacks native lineage tracking.

Therefore, A best fulfills all stated requirements using AWS-recommended governance and security capabilities.

Option C is the correct solution because AWS AppConfig is purpose-built to manage dynamic application configurations with low latency, strong validation, and minimal operational overhead, which directly matches the company’s requirements.

AWS AppConfig enables the company to centrally manage model selection logic, inference parameters, and customer-tier routing rules without redeploying Lambda functions. By using feature flags, the company can easily perform A/B testing of new models or prompt strategies by gradually rolling out changes to a subset of users or customer tiers. This allows experimentation and controlled releases without code changes.

AppConfig also supports JSON schema validation, which is critical for validating parameters such as temperature, maximum token limits, and other model-specific settings before they are applied. This prevents invalid or unsafe configurations from being deployed and reduces the risk of runtime errors or degraded model behavior in production.

Using the AWS AppConfig Agent allows Lambda functions to retrieve configurations efficiently with built-in caching and polling mechanisms, minimizing latency and avoiding excessive calls to configuration services. This approach scales well for high-throughput, low-latency applications such as GenAI APIs behind Amazon API Gateway.

Option A introduces unnecessary redeployment logic and polling complexity. Option B requires building and maintaining custom configuration access patterns in DynamoDB and does not natively support feature flags or schema validation. Option D adds operational overhead by requiring ElastiCache cluster management and custom validation logic.

Therefore, Option C provides the most scalable, flexible, and low-maintenance solution for dynamic model switching, A/B testing, and safe configuration management in a GenAI application.

Option A is the most appropriate design because it provides scalable multi-agent orchestration, clear domain separation, and strong governance with minimal operational complexity. A supervisor-agent pattern is a standard AWS-recommended approach for multi-agent systems: one agent performs intent classification and routing, while specialized agents handle domain-specific tasks.

Isolating data with separate knowledge bases ensures that each specialized collaborator agent retrieves only the information relevant to its department. This improves response accuracy, reduces hallucinations, and supports privacy controls because clinical content, claims content, and scheduling content can have different access policies. IAM-based filtering ensures that each agent has permission only to the knowledge base it is authorized to use.

Routing patient inquiries through a supervisor agent supports high concurrency and extensibility. New departments or features can be added by introducing new collaborator agents and knowledge bases without redesigning the entire system. Because routing is handled centrally, changes in classification logic do not require updates across many independent supervisors.

Using RAG within each collaborator agent ensures that responses are grounded in department-approved information sources, which is critical in healthcare settings to reduce unsafe or incorrect guidance. This approach also improves performance because each retrieval scope is smaller and more relevant, supporting thousands of parallel interactions.

Option B introduces manual handoffs that do not scale. Option C relies on rule-based routing inside one general agent, which becomes brittle and difficult to govern as complexity grows. Option D mixes all departments into a single knowledge base and merges responses externally, increasing risk of incorrect domain answers and operational overhead.

Therefore, Option A best meets the scalability, correctness, and multi-agent onboarding requirements.

Option B best satisfies the requirements with the least custom development effort by using native Amazon Bedrock capabilities for prompt experimentation, traffic management, fairness monitoring, and alerting. Amazon Bedrock Prompt Management allows teams to define and manage multiple prompt variants without code changes, making it ideal for comparing recommendation strategies across demographic groups.

Amazon Bedrock Flows enables controlled traffic allocation between prompt variants, which supports real-time A/B testing. This allows the company to collect live fairness metrics under production conditions instead of relying on offline analysis. Because Flows are fully managed, they eliminate the need for custom routing or experimentation frameworks.

Amazon Bedrock guardrails provide built-in monitoring and intervention mechanisms. When configured for fairness-related checks, guardrails can detect policy violations and surface metrics such as InvocationsIntervened, which indicate when outputs are modified or blocked due to rule enforcement. These metrics integrate directly with Amazon CloudWatch, enabling real-time dashboards and threshold-based alarms. Setting an alarm at a 15% discrepancy threshold satisfies the alerting requirement with minimal configuration.

Weekly reporting can be generated from CloudWatch metrics using scheduled exports or dashboards without building custom analytics pipelines. Option A requires significant custom post-processing logic. Option C introduces an additional service with higher operational overhead and is not optimized for real-time monitoring. Option D focuses on offline evaluation jobs and does not provide continuous real-time fairness monitoring.

Therefore, Option B provides the most AWS-native, scalable, and low-effort solution for fairness evaluation and monitoring.

Option B is the correct solution because AWS AppConfig is specifically designed to support dynamic configuration management with safe rollout, validation, and rollback, which are explicit requirements in the scenario.

By storing the active model provider configuration in AWS AppConfig, the company can switch between Amazon Bedrock model providers in real time without redeploying Lambda code. AppConfig supports deployment strategies such as canary releases, linear rollouts, and immediate deployments, allowing safe and controlled changes. If a configuration causes issues, AppConfig supports automatic rollback, reducing operational risk.

AWS AppConfig also supports schema validation, ensuring that configuration values such as model identifiers, provider names, or inference parameters are valid before being applied. This prevents misconfiguration from impacting production workloads.

Option A uses Parameter Store, which lacks native rollout strategies, validation, and automated rollback, making it unsuitable for safe real-time switching. Option C requires manual routing changes and code coupling, increasing operational overhead and deployment risk. Option D introduces unnecessary complexity by hosting configuration files in Amazon S3 when AppConfig already supports native hosted configurations.

Therefore, Option B provides the most robust, scalable, and low-maintenance solution for dynamic model switching in a serverless Amazon Bedrock inference architecture.

Option C is the correct solution because AWS AppConfig is designed for real-time, validated, centrally managed configuration changes with safe rollout, immediate propagation, and rollback support—exactly matching the company’s requirements.

By storing routing rules, cost thresholds, regulatory constraints, and A/B testing logic in AWS AppConfig, the company can switch between Amazon Bedrock foundation models without redeploying Lambda code. AppConfig supports feature flags, dynamic configuration updates, JSON schema validation, and staged rollouts, which are essential for safely managing complex and frequently changing routing logic.

Using the AWS AppConfig Agent, Lambda functions can retrieve cached configurations efficiently, ensuring low latency even under thousands of concurrent requests. This approach allows the Lambda function to apply proprietary business logic—such as user tier, transaction value, Region compliance, and real-time cost metrics—before selecting the appropriate FM.

Option A is operationally fragile because environment variable changes require function restarts and do not support validation or controlled rollouts. Option B is too limited for complex, dynamic logic and is difficult to maintain at scale. Option D misuses Lambda authorizers, which are intended for authentication and authorization, not high-frequency dynamic routing decisions.

Therefore, Option C provides the most scalable, flexible, and low-overhead architecture for dynamic, regulation-aware FM routing in a global GenAI system.

Option A best meets the combined requirements of low latency, stability, and validated safety controls by using purpose-built Amazon Bedrock features designed for production GenAI operations. The company’s latency target of under 1 second and its observation of degradation during spikes strongly indicate capacity and throughput variability. Provisioned throughput for Amazon Bedrock is intended to deliver more predictable performance by reserving inference capacity for a chosen model, reducing throttling risk and stabilizing response times under load. This directly improves operational consistency across Regions where on-demand capacity can vary.

The requirement to “block unsafe or hallucinated recommendations” is most directly addressed by Amazon Bedrock Guardrails. Guardrails provide managed safety enforcement, including sensitive information controls and configurable content policies. Using semantic denial rules enables the application to prevent unsafe guidance such as dangerous brewing temperatures or other harmful procedural instructions, enforcing safety at the model boundary rather than relying on downstream filtering.

The remaining requirement is “99.5% output consistency for identical inputs.” While generative models can be probabilistic, production systems achieve practical consistency by controlling prompt versions, inputs, and policy behavior. Amazon Bedrock Prompt Management supports controlled prompt lifecycle practices, including versioning and approval workflows, which reduce unintended drift across deployments and Regions. By ensuring the same approved prompt templates and parameters are used consistently, the company can materially improve repeatability for the same structured inputs and retrieval context, which is essential in multi-stage prompt chains.

The other options are incomplete. B improves experimentation and observability but does not enforce safety controls or stabilize latency. C can improve performance, but it does not provide validated safety enforcement at inference time. D can help retrieval relevance, but it does not address unsafe outputs or inference stability. Therefore, A is the only option that simultaneously targets predictable latency, governance of prompt behavior, and strong safety controls within Amazon Bedrock.