Free Amazon AWS Certified Generative AI Developer - Professional AIP-C01 Exam Questions

The correct answers are B and C because they directly address hallucination reduction while maintaining high throughput and low latency.

Option B reduces hallucinations at their source by grounding model outputs in verified content through Retrieval Augmented Generation (RAG). Using an Amazon Bedrock knowledge base with semantic chunking ensures that long technical documents are broken into meaningfully coherent sections. This allows the model to retrieve only the most relevant chunks, rather than processing an entire document in one pass, which significantly improves factual accuracy and reduces cognitive overload on the model. This approach scales efficiently and supports processing more than 1,000 documents per hour.

Option C adds a defense-in-depth safety layer by using Amazon Bedrock guardrails to detect and block hallucination-like output patterns. Guardrails operate at inference time with minimal performance overhead, making them suitable for low-latency requirements. While guardrails do not eliminate hallucinations entirely, they effectively prevent unsafe or clearly fabricated outputs from reaching users.

Option A increases latency and cost due to explicit reasoning steps and does not scale well for high-throughput workloads. Option D increases randomness and worsens hallucinations. Option E repeats the existing flawed approach.

Therefore, Options B and C together provide scalable grounding and runtime protection that meet accuracy, performance, and throughput requirements.

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 B is the correct solution because Amazon Bedrock Knowledge Bases with the RetrieveAndGenerate API provide a fully managed Retrieval Augmented Generation (RAG) capability that directly addresses grounding, citation, and hallucination prevention with the least operational overhead.

Amazon Bedrock Knowledge Bases automatically manage document ingestion, chunking, embedding, retrieval, and ranking from approved data sources. When used with the RetrieveAndGenerate API, the model is constrained to generate responses only from retrieved, approved clinical documentation, significantly reducing the risk of hallucinations or unsupported claims. The API also returns explicit source citations, which satisfies regulatory and clinical transparency requirements without requiring custom comparison or validation logic.

This approach aligns with AWS best practices for healthcare GenAI workloads, where correctness and traceability are critical. Because retrieval and generation are tightly integrated, the system avoids multi-step orchestration, custom verification pipelines, or additional compute layers that would increase latency and maintenance burden.

Option A introduces Amazon Kendra and custom post-processing logic, increasing operational complexity. Option C focuses on entity extraction rather than controlled knowledge grounding and does not guarantee citation or hallucination prevention. Option D requires manual orchestration between retrieval and generation and custom verification logic, which increases development and maintenance effort.

Therefore, Option B delivers accurate, grounded, and cited clinical recommendations with minimal infrastructure and operational overhead.

The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.

To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.

To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.

Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.

By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.

Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation. AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.

Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.

Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling APIs with Lambda adds custom logic and operational overhead. Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the ''built-in Amazon Bedrock capabilities'' and ''minimal overhead'' requirements. Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing. Therefore, option A is the most AWS-aligned solution.

Option D is the correct solution because Amazon Bedrock Knowledge Bases natively support reranking by using Amazon-managed reranker models, which are specifically designed to improve contextual relevance after the initial vector retrieval step. This approach directly addresses the root cause of the issue: semantically similar but contextually irrelevant documents being passed to the foundation model.

By enabling the reranking configuration within Amazon Bedrock Knowledge Bases, the application can automatically reorder retrieved documents based on deeper contextual understanding, such as regulatory scope, legal applicability, and semantic intent. This significantly improves retrieval precision, which reduces hallucinations and improves the factual accuracy of generated regulatory guidance.

Option D requires no additional infrastructure, no custom orchestration logic, and no separate model hosting. The reranking is fully managed by Amazon Bedrock and integrates seamlessly with the existing RetrieveAndGenerateStream workflow. This makes it the lowest operational overhead solution.

Option A introduces operational complexity by requiring a custom SageMaker endpoint, API Gateway routing, and model lifecycle management. Option B combines multiple unrelated services and introduces significant complexity without being purpose-built for RAG relevance ranking. Option C improves relevance but requires explicitly calling the Rerank API and modifying the application pipeline, which increases operational and integration effort compared to built-in reranking.

Therefore, Option D provides the most efficient, scalable, and AWS-recommended method to improve RAG retrieval quality while minimizing operational burden.

Option B best satisfies the requirements while minimizing development effort by combining managed semantic search capabilities with fully managed foundation models. AWS Generative AI guidance describes semantic search as a vector-based retrieval pattern where both documents and user queries are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves results based on meaning rather than exact keywords.

Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving sub-second latency for the majority of queries. Because OpenSearch is a distributed, managed service, it automatically scales during peak traffic periods and provides cost-effective performance compared with building and tuning custom vector search pipelines on relational databases.

Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS manages the foundation models, eliminates the need for custom model hosting, and ensures consistency by using the same FM for both document embeddings and query embeddings. This aligns directly with AWS-recommended semantic search architectures and removes the need for model lifecycle management.

Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.

Option A does not meet the semantic search requirement because keyword-based search cannot reliably interpret complex natural language intent. Option C introduces scalability and performance risks by running large-scale vector similarity searches inside PostgreSQL, which increases operational complexity. Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented generation, not high-throughput semantic search.

Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and minimal development effort using AWS Generative AI services.

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.

Option A is the correct solution because it satisfies authentication, private connectivity, fine-grained authorization, and auditing using AWS-recommended patterns.

SAML federation between Microsoft Entra ID and IAM is a mature, well-supported integration that enables centralized enterprise authentication. Department-specific IAM roles allow precise control over which Bedrock ModelId values each department can invoke, enforcing access by model family.

Using AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services ensures that all inference traffic stays on private AWS network paths, with no public internet exposure. NAT gateways and public endpoints, as used in other options, violate this requirement.

AWS CloudTrail provides authoritative audit logs of all Bedrock API calls, which is required for compliance. Amazon Bedrock model invocation logging complements CloudTrail by capturing detailed prompt and response metadata for deeper auditing and investigation.

Option B uses public endpoints via NAT. Option C incorrectly claims public endpoints can be private. Option D relies on IdP-side logs, which do not capture Bedrock API activity.

Therefore, Option A is the only solution that fully meets security, compliance, and observability requirements.

Option B provides the strongest match among the listed alternatives because it combines managed speaker identification, multimodal generative analysis, and a native search-oriented datastore without unnecessary custom orchestration.

Amazon Transcribe speaker diarization distinguishes speakers and assigns identifiers such as spk_0, spk_1, and so forth. Its output includes speaker labels and timestamps, allowing individual statements to be associated with particular speakers and positions in the meeting timeline.

Anthropic Claude models on Amazon Bedrock support multimodal prompts that combine text and images. The application can therefore supply speaker-attributed transcript segments together with extracted presentation/video frames and instruct the model to generate summaries that associate spoken comments with relevant visual information. AWS's Claude Messages API documentation explicitly supports mixed image-and-text input.

Amazon OpenSearch Service is appropriate for storing the enriched meeting records and supporting document indexing and full-text searching. It provides substantially more natural full-text retrieval than attempting to create a custom indexing layer over DynamoDB.

A uses Transcribe and Rekognition effectively, but it states that a Lambda function itself ''generates'' the summaries without specifying a generative model and requires custom correlation logic. C chains BDA, Transcribe, Rekognition, DynamoDB, and Bedrock, increasing service count and operational complexity; DynamoDB also does not natively satisfy the full-text-search requirement. D similarly depends on DynamoDB plus a custom indexing mechanism.

Current AWS capabilities make Bedrock Data Automation even more capable than the wording of C suggests: BDA can produce video summaries, scene-level summaries, detected video text, full audio transcripts, and speaker identification. However, among the supplied choices, B remains the cleanest architecture satisfying multimodal GenAI analysis, diarization, scalable managed processing, and native searchable storage.

Option B is correct because the application is currently invoking the base foundation model identifier, which routes traffic to the on-demand capacity pool rather than the company's purchased provisioned throughput. In Amazon Bedrock, provisioned throughput is attached to a specific provisioned resource created through the provisioned throughput APIs. To consume that reserved capacity, inference requests must target the provisioned resource identifier that represents the purchased throughput, not the generic model identifier used for on-demand inference.

The code snippet uses modelId='anthropic.claude-v2'. This value selects the on-demand endpoint for that model. As a result, requests are subject to on-demand quotas and throttling behavior, while the provisioned throughput remains idle. This directly explains the CloudWatch observation: provisioned capacity metrics show unused capacity because no traffic is being directed to the provisioned resource, and the on-demand path is throttling because it is exceeding the applicable on-demand limits during peak volume.

Replacing the modelId value with the provisioned throughput ARN returned by the CreateProvisionedModelThroughput workflow ensures the runtime invocation is routed to the reserved capacity. Once traffic is directed correctly, the purchased model units provide the consistent throughput required for predictable performance during business hours, which is exactly why provisioned throughput is used.

Option A could increase capacity, but it does not fix the core issue that the application is not using the provisioned resource at all. Option C can reduce the impact of throttling temporarily, but it adds latency and does not guarantee consistent throughput; it also still wastes the provisioned capacity. Option D changes the response delivery mechanism, but throttling is a capacity routing and quota issue, not a streaming API issue.

Option B is the best solution because it directly addresses the Step Functions 256 KB state payload quota by externalizing large intermediate artifacts to Amazon S3 and passing only lightweight references (URIs/keys) between states. This is a standard AWS pattern for workflows that produce large intermediate results, and it avoids introducing additional databases, compression logic, or cross-state-machine coordination that increases operational overhead.

In a multi-agent ReAct workflow, intermediate reasoning traces can be verbose and grow quickly as each agent produces chain-of-thought style artifacts, structured outputs, and supporting evidence. Step Functions is designed to orchestrate state transitions and pass JSON payloads, but large payloads should be stored outside the state machine and referenced by pointer values. Using Amazon S3 for intermediate outputs is operationally efficient because the application already uses S3 for storage, and S3 provides durable, low-cost storage with simple access patterns.

ResultPath and ResultSelector allow each state to store or reshape results so that only the required reference fields (such as s3Uri, object key, metadata, trace IDs) are forwarded to subsequent states. This preserves observability because the workflow can still log trace references, correlate steps with S3 objects, and store structured metadata for debugging. It also preserves the sequential validation design, keeping the existing ReAct pattern intact while preventing failures due to oversized payloads.

Option A adds additional services and read/write patterns that increase operational complexity. Option C introduces custom compression/decompression logic that is fragile, adds latency, and complicates troubleshooting. Option D increases orchestration overhead by splitting workflows and coordinating with events, which makes debugging harder and increases failure modes.

Therefore, Option B meets the payload limit requirement while keeping the architecture simple and observable.

Option C is the correct solution because it directly addresses both correctness and performance issues by grounding the model's responses in authoritative product data using Retrieval Augmented Generation. Amazon Bedrock Knowledge Bases are designed to connect foundation models to trusted enterprise data sources, ensuring that generated responses are constrained to known, validated content.

By ingesting the product catalog into a knowledge base, the GenAI application retrieves only products that actually exist in the catalog. This prevents hallucinated or unavailable recommendations, which is a common issue when models rely solely on prompt instructions without retrieval grounding. RAG ensures that the model's output is based on retrieved facts rather than learned generalizations.

Setting the PerformanceConfigLatency parameter to optimized enables Bedrock to prioritize lower-latency retrieval and inference paths, improving responsiveness for real-time recommendation scenarios. This directly addresses the reported performance issues without requiring provisioned throughput or caching strategies that are ineffective for mostly unique interactions.

Option A improves safety and latency predictability but does not ensure recommendations are limited to valid products. Option B relies on prompt constraints, which are not sufficient to prevent hallucinations. Option D introduces additional validation and caching layers but increases complexity and does not improve generation relevance.

Therefore, Option C best resolves both relevance and latency challenges using AWS-native, low-maintenance GenAI integration patterns.

: AWS Step Functions provides native service integrations that minimize code and operational overhead. For this workflow, Step Functions can directly invoke Amazon Transcribe and, upon completion, directly invoke Amazon Bedrock foundation models. Modern foundation models can be prompted to return outputs in JSON format, fulfilling the requirement for structured analysis without needing an intermediate Lambda function to format the request or response. To trigger the process 'as soon as new files are created,' configuring Amazon S3 to send events to Amazon EventBridge is the recommended event-driven pattern. This allows for fine-grained routing and decouples the storage layer from the processing logic, ensuring the workflow scales reliably as call volume grows.

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.