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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q20-Q25):

NEW QUESTION # 20
A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
response = bedrock_runtime.invoke_model(
modelId="anthropic.claude-v2",
body=json.dumps(payload)
)
The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?

  • A. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
  • B. Modify the application to use the invokeModelWithResponseStream API instead of the invokeModel API.
  • C. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
  • D. Increase the number of model units (MUs) in the provisioned throughput configuration.

Answer: C

Explanation:
Option B is the correct solution because Amazon Bedrock provisioned throughput is only used when the application explicitly invokes the provisioned model ARN, not the base foundation model ID. In the provided code, the application is calling the standard model identifier (anthropic.claude-v2), which routes requests to on-demand capacity instead of the purchased provisioned throughput.
When the CreateProvisionedModelThroughput API is used, Amazon Bedrock returns a provisioned model ARN that represents the reserved capacity. Applications must reference this ARN in the modelId parameter when invoking the model. If the base model ID is used instead, Bedrock treats the request as on-demand traffic, which explains why CloudWatch metrics show unused provisioned capacity alongside throttled on- demand requests.
Option A would increase capacity but would not fix the root cause because the application is not using the provisioned resource at all. Option C adds resiliency but does not ensure usage of provisioned throughput and would still incur throttling. Option D changes the response delivery mechanism but does not affect capacity routing.
Therefore, Option B directly resolves the throttling issue by correctly routing traffic to the reserved capacity and ensures that the company benefits from the provisioned throughput it has purchased.


NEW QUESTION # 21
A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.
Which solution will meet these requirements?

  • A. Use AWS Step Functions to create an approval workflow. Store prompts in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.
  • B. Deploy Amazon SageMaker Canvas with prompt templates stored in Amazon S3. Use AWS CloudFormation for version control. Use AWS Config to enforce approval policies.
  • C. Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch dashboards to display prompt usage metrics. Store approval status in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.
  • D. Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use AWS Identity and Access Management policies to control approval permissions.
    Create parameterized prompt templates by specifying variables.

Answer: D

Explanation:
Option B is the correct solution because Amazon Bedrock Prompt Management is purpose-built to manage, govern, and standardize prompt usage at scale across teams and Regions. It provides native version control, allowing teams to track prompt changes over time and ensure that only approved versions are used in production workflows.
Prompt Management supports approval workflows that align with enterprise governance requirements.
Approval permissions can be enforced through IAM policies, ensuring that only authorized reviewers can approve or publish prompt versions. This removes the need for custom workflow engines or external storage systems, significantly reducing operational overhead.
Parameterized prompt templates enable consistent prompt structure while allowing controlled variation through defined variables. This ensures consistent quality standards and reduces prompt drift, which is critical when hundreds of prompts are reused across multiple applications and teams.
AWS CloudTrail integrates natively with Amazon Bedrock to provide immutable audit logs for prompt creation, updates, approvals, and usage. These detailed audit trails satisfy compliance requirements and allow security and governance teams to trace prompt activity across Regions and users.
Option A requires significant custom development to coordinate approvals and maintain state. Option C relies on general-purpose workflow services and manual versioning mechanisms that are error-prone and difficult to scale. Option D uses services not designed for large-scale GenAI prompt governance and introduces unnecessary complexity.
Therefore, Option B best meets the requirements for scalable, auditable, and low-overhead governance of AI- generated content using Amazon Bedrock.


NEW QUESTION # 22
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?

  • A. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in application code to switch Regions when throttling occurs. Use AWS Global Accelerator to route traffic based on user location.
  • B. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when quotas are reached.
  • C. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when calling the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
  • D. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts when usage approaches thresholds.

Answer: C

Explanation:
Option B is the most appropriate solution because it directly uses Amazon Bedrock cross-Region inference profiles, which are designed to provide resilience and load distribution while respecting data residency boundaries. Cross-Region inference profiles allow applications to distribute inference requests across multiple Regions within a defined geographic boundary, such as Europe or North America, without requiring custom failover logic.
By specifying geographical codes in the inference profile ID, the application ensures that European user data is processed only within Europe-based Regions, satisfying regulatory requirements. At the same time, Bedrock automatically routes requests to healthy Regions within that geography when traffic spikes or service quotas are reached, improving availability and maintaining low latency.
Using separate Amazon API Gateway HTTP APIs for Europe and North America provides a clean, simple routing layer that directs users to the appropriate regional inference profile. This avoids complex custom routing or retry logic in application code and minimizes operational overhead.
Option A relies on custom routing and manual monitoring, which increases complexity and does not provide automatic resilience. Option C introduces custom retry and fallback logic that risks violating data residency requirements if misconfigured. Option D requires significant application-level failover logic and adds operational burden with Global Accelerator configuration.
Therefore, Option B best meets the requirements for low latency, data residency compliance, resilience during traffic spikes, and minimal operational complexity.


NEW QUESTION # 23
A financial services company is deploying a generative AI (GenAI) application that uses Amazon Bedrock to assist customer service representatives to provide personalized investment advice to customers. The company must implement a comprehensive governance solution that follows responsible AI practices and meets regulatory requirements.
The solution must detect and prevent hallucinations in recommendations. The solution must have safety controls for customer interactions. The solution must also monitor model behavior drift in real time and maintain audit trails of all prompt-response pairs for regulatory review. The company must deploy the solution within 60 days. The solution must integrate with the company's existing compliance dashboard and respond to customers within 200 ms.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Configure Amazon Bedrock guardrails to apply custom content filters and toxicity detection. Use Amazon Bedrock Model Evaluation to detect hallucinations. Store prompt-response pairs in Amazon DynamoDB to capture audit trails and set a TTL. Integrate Amazon CloudWatch custom metrics with the existing compliance dashboard.
  • B. Use Amazon SageMaker Model Monitor to detect model behavior drift. Use AWS WAF to filter content. Store customer interactions in an encrypted Amazon RDS database. Use Amazon API Gateway to create custom HTTP APIs to integrate with the compliance dashboard.
  • C. Use Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to ground responses. Use Amazon Bedrock Guardrails to enforce content safety. Use Amazon OpenSearch Service to store and index prompt-response pairs. Integrate OpenSearch Service with Amazon QuickSight to create compliance reports and to detect model behavior drift.
  • D. Deploy Amazon Bedrock and use AWS PrivateLink to access the application securely. Use AWS Lambda functions to implement custom prompt validation. Store prompt-response pairs in an Amazon S3 bucket and configure S3 Lifecycle policies. Create custom Amazon CloudWatch dashboards to monitor model performance metrics.

Answer: A

Explanation:
Option A is the correct solution because it uses native Amazon Bedrock governance and evaluation capabilities to meet regulatory, performance, and deployment timeline requirements with the least operational overhead.
Amazon Bedrock guardrails provide built-in safety controls that enforce responsible AI policies directly during inference. Custom content filters and toxicity detection protect customer interactions and prevent disallowed investment guidance patterns without requiring custom application logic. Guardrails operate inline and are optimized for low latency, which helps meet the strict 200 ms response-time requirement.
Hallucination detection is addressed through Amazon Bedrock Model Evaluation, which supports automated evaluation at scale using LLM-as-a-judge techniques. This enables the company to detect factual inaccuracies and policy violations systematically, without building custom evaluation pipelines or requiring extensive human review. Evaluation outputs can be surfaced as metrics.
Storing all prompt-response pairs in Amazon DynamoDB provides a low-latency, highly scalable audit store that aligns with financial regulatory requirements. Using TTL enforces data retention policies automatically, reducing compliance risk and storage overhead.
Amazon CloudWatch custom metrics integrate seamlessly with existing compliance dashboards, allowing near-real-time monitoring of safety interventions, hallucination rates, and drift indicators. CloudWatch anomaly detection can be applied to these metrics to surface behavior changes quickly.
Option B relies on custom Lambda logic and S3-based auditing, increasing latency and operational complexity. Option C introduces additional services that increase setup time and may exceed the 60-day deployment window. Option D uses non-Bedrock-native monitoring and adds unnecessary infrastructure layers.
Therefore, Option A provides the most complete, compliant, and low-overhead governance solution for a regulated GenAI financial services application.


NEW QUESTION # 24
A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake.
The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in the OpenSearch Neural plugin for Amazon OpenSearch Service.
  • B. Use Amazon SageMaker AI to deploy a sentence-transformer model. Use the model to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL database that has the pgvector extension.
  • C. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL Serverless database that has the pgvector extension.
  • D. Use Amazon Comprehend to extract topics from the digitized files. Store the topics and file metadata in an Amazon Aurora PostgreSQL database. Query the abstract metadata against the data in the Aurora database.

Answer: C

Explanation:
Option D is the best choice because it delivers true semantic search with the smallest operational footprint by combining a fully managed embedding service with an automatically scaling vector-capable database. The university's requirement is explicitly semantic: the metadata has no keywords, and the system must match abstracts based on similarity of meaning. This is a direct fit for an embeddings-based approach, where each abstract is converted into a vector representation and searched using vector similarity. Amazon Titan Embeddings in Amazon Bedrock provides a managed way to generate these vectors without hosting or maintaining an ML model, eliminating the operational work of model provisioning, patching, scaling, and lifecycle management.
For storage and retrieval, Amazon Aurora PostgreSQL Serverless with the pgvector extension supports vector storage and similarity search while minimizing infrastructure operations. Aurora Serverless reduces capacity planning and scaling tasks because it can automatically adjust to changes in workload, which is valuable for a university search application with variable usage patterns. With fewer than 1 million files, a PostgreSQL-based vector store is commonly operationally simpler than running a dedicated search cluster, while still meeting the requirement to query using both text-derived similarity and associated metadata filters stored alongside the vectors.
Option A can also enable vector search, but operating an OpenSearch domain typically introduces additional concerns such as domain sizing, shard strategy, cluster scaling, and performance tuning for k-NN workloads.
Option C increases operational overhead the most because it requires deploying and operating a sentence- transformer model endpoint in SageMaker AI, including scaling, monitoring, and model management. Option B does not meet the semantic similarity requirement reliably because topic extraction is not equivalent to embedding-based semantic matching, especially when the metadata lacks keywords and the system must compare abstracts by meaning.
Therefore, D best satisfies semantic search needs with the least operational overhead.


NEW QUESTION # 25
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