A company is developing a generative AI (GenAI)-powered customer support application that uses Amazon Bedrock foundation models (FMs). The application must maintain conversational context across multiple interactions with the same user. The application must run clarification workflows to handle ambiguous user queries. The company must store encrypted records of each user conversation to use for personalization. The application must be able to handle thousands of concurrent users while responding to each user quickly.
Which solution will meet these requirements?
A. Use an AWS Step Functions Express workflow to orchestrate conversation flow. Invoke AWS Lambda functions to run clarification logic. Store conversation history in Amazon RDS and use session IDs as the primary key.
B. Use an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include Wait for a Callback patterns to manage the workflows. Store conversation history in Amazon DynamoDPurchase on-demand capacity and configure server-side encryption.
C. Deploy the application by using an Amazon API Gateway REST API to route user requests to an AWS Lambda function to update and retrieve conversation context. Store conversation history in Amazon S3 and configure server-side encryption. Save each interaction as a separate JSON file.
D. Use AWS Lambda functions to call Amazon Bedrock inference APIs. Use Amazon SQS queues to orchestrate clarification steps. Store conversation history in an Amazon ElastiCache (Redis OSS) cluster. Configure encryption at rest.
Show Answer
Correct Answer: B
Explanation: Step Functions Standard workflows support durable callback patterns, which let the application pause for a user’s clarification and resume when the user responds. DynamoDB on-demand capacity can scale with variable concurrent traffic, and server-side encryption protects the stored conversation records. Express workflows do not support the callback pattern needed for this clarification flow; S3 and ElastiCache are less suitable as the primary durable conversation store.
Question 5
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios. The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Choose three.)
A. Add the high-risk conversation patterns to a denied topics guardrail.
B. Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
C. Configure a content filter guardrail to filter prompts that contain competitor names.
D. Add the names of competitors as custom word filters. Set the input and output actions to block.
E. Set a low grounding score threshold.
F. Set a high grounding score threshold.
Show Answer
Correct Answer: A, D, F
Explanation: Use a denied-topics guardrail to block high-risk financial-advice topics, such as specific stock recommendations or guaranteed returns. Add competitor names as custom word filters and block matches in both inputs and outputs. Set a high grounding score threshold so responses must be well supported by the company’s approved financial guidance.
Question 5
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock knowledge base. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?
A. Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents from the bucket and to invoke knowledge base syncing.
B. Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded to the bucket. Use a second Lambda function to invoke Amazon Comprehend to detect and redact PII if a user is part of the engineer Cognito user group. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years from the bucket.
C. Set up an S3 Lifecycle configuration on the bucket to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, call the ApplyGuardrail configuration that matches the user's Cognito user group to redact PII from the agent’s responses if appropriate.
D. Create a second knowledge base. Set up an S3 Lifecycle configuration on the bucket to remove reports that are older than 3 years. Invoke an AWS Lambda function that syncs the bucket with the original knowledge base when a new report is uploaded to the bucket. Use Amazon Comprehend to detect and redact PII before syncing the bucket with the second knowledge base. When a user interacts with the AI assistant, redirect the model to the appropriate knowledge base depending on the user’s Cognito user group.
Show Answer
Correct Answer: C
Explanation: Use S3 Lifecycle to ensure reports older than 3 years are removed. Keep the knowledge base synchronized, and apply Amazon Bedrock Guardrails at response time based on the authenticated user's Cognito group so surgeons can see PII while engineers receive redacted responses. This preserves a single source of truth while enforcing role-based redaction.
Question 5
A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step-by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Bedrock Knowledge Bases with source attribution enabled. Use the Anthropic Claude Messages API with RAG to set high-relevance thresholds for source documents. Store reasoning and citations in Amazon S3 for auditing purposes.
B. Use Amazon Bedrock with Anthropic Claude models and extended thinking. Configure a 4,000-token thinking budget. Store reasoning traces and citations in Amazon DynamoDB for auditing purposes.
C. Configure Amazon SageMaker AI with a custom Anthropic Claude model. Use the model's reasoning parameter and AWS Lambda to process responses. Add source citations from a separate Amazon RDS database.
D. Use Amazon Bedrock with Anthropic Claude models and chain-of-thought reasoning. Configure custom retrieval tracking with the Amazon Bedrock Knowledge Bases API. Use Amazon CloudWatch to monitor response latency metrics.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Knowledge Bases provides managed retrieval from company data and built-in source attribution, linking claims to source documents with less operational work than custom retrieval and citation pipelines. The application can present a concise, source-grounded rationale for its recommendations; exposing a model’s full internal reasoning trace is not required. Extended thinking and a 4,000-token budget could add latency and do not ensure the under-3-second target.
Question 6
A financial services company needs to build a document analysis system that uses Amazon Bedrock to process quarterly reports. The system must analyze financial data, perform sentiment analysis, and validate compliance across batches of reports. Each batch contains 5 reports. Each report requires multiple foundation model (FM) calls. The solution must finish the analysis within 10 seconds for each batch. Current sequential processing takes 45 seconds for each batch.
Which solution will meet these requirements?
A. Use AWS Lambda functions with provisioned concurrency to process each analysis type sequentially. Configure the Lambda function timeouts to 10 seconds. Configure automatic retries with exponential backoff.
B. Use AWS Step Functions with a Parallel state to invoke separate AWS Lambda functions for each analysis type simultaneously. Configure Amazon Bedrock client timeouts. Use Amazon CloudWatch metrics to track execution time and model inference latency.
C. Create an Amazon SQS queue to buffer analysis requests. Deploy multiple AWS Lambda functions with reserved concurrency. Configure each Lambda function to process different aspects of each report sequentially and then combine the results.
D. Deploy an Amazon ECS cluster that runs containers that process each report sequentially. Use a load balancer to distribute batch workloads. Configure an auto-scaling policy based on CPU utilization to handle demand fluctuations.
Show Answer
Correct Answer: B
Explanation: The independent analyses can run concurrently using a Step Functions Parallel state, reducing batch wall-clock time compared with sequential processing. Lambda timeouts and CloudWatch metrics help monitor whether Bedrock calls and the overall workflow meet the 10-second target. The other options retain sequential work or add queueing and scaling without directly addressing the latency requirement.
Question 6
A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size.
Which combination of solutions will meet these requirements? (Choose two.)
A. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.
B. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.
C. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.
D. Configure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.
E. Configure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.
Show Answer
Correct Answer: C, D
Explanation: Amazon Transcribe is required to convert the MP3 recordings to text. Because Transcribe is asynchronous, AWS Step Functions is appropriate to orchestrate the job and wait for completion. A Lambda function is then used to retrieve the transcript if needed, construct the prompt, and invoke Amazon Bedrock to produce structured JSON summaries and sentiment analysis. For event-driven execution, Amazon S3 object creation events are commonly routed through Amazon EventBridge, which can start the Step Functions state machine. S3 does not directly target Step Functions via bucket notification destinations.
Question 7
A GenAI developer is evaluating Amazon Bedrock foundation models (FMs) to enhance a Europe-based company's internal business application. The company has a multi-account landing zone in AWS Control Tower. The company uses SCPs to allow its accounts to use only the eu-north-1 Region and the eu-west-1 Region. All customer data must remain in private networks within the approved AWS Regions.
The GenAI developer selects an FM based on analysis and testing and hosts the model in the eu-central-1 Region and the eu-west-3 Region. The GenAI developer must enable access to the FM for the company’s employees. The GenAI developer must ensure that requests to the FM are private and remain with the same Regions as the FM.
Which solution will meet these requirements?
A. Deploy an AWS Lambda function that is exposed by a private Amazon API Gateway REST API to a VPC in eu-north-1. Create a VPC endpoint for the selected FM in eu-central-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application.
B. Deploy the FM on Amazon EC2 instances in eu-north-1. Deploy a private Amazon API Gateway REST API in front of the EC2 instances. Configure an Amazon Bedrock VPC endpoint. Integrate the REST API with the business application.
C. Configure the FM to use cross-Region inference through an eu.amazon.* endpoint to ensure that all calls remain within Europe. Configure an Amazon Bedrock VPC endpoint. Extend existing SCPs to allow employees to use the FM through inference profiles in Europe-based Regions where the FM is available. Use an inference profile to integrate Amazon Bedrock with the business application.
D. Deploy the FM in Amazon SageMaker AI in eu-north-1. Configure a SageMaker AI VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker AI endpoint. Integrate the FM in SageMaker AI with the business application.
Show Answer
Correct Answer: C
Explanation: Cross-Region inference with the Europe geographic endpoint (eu.amazon.*) and an inference profile is the Amazon Bedrock feature designed to route requests only among supported European Regions while keeping traffic within Europe. Using an Amazon Bedrock VPC endpoint provides private connectivity. Updating the SCPs to permit the required Bedrock inference profile access enables use from the organization's allowed accounts without replacing the architecture or self-hosting the model.
Question 7
An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits.
Which solution will resolve this problem?
A. Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.
B. Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
C. Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the Amazon Bedrock RetrieveAndGenerate API call to dynamically select the most relevant chunks based on embedding similarity scores.
D. Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.
Show Answer
Correct Answer: C
Explanation: Semantic chunking preserves meaningful sections of the technical documents, while RetrieveAndGenerate retrieves only the chunks relevant to each query. This avoids sending entire documents beyond the FM’s context window and reduces the loss of relevant context that can cause inconsistent answers.
Sources:
https://www.p2pexams.com/free-questions/amazon-aip-c01-mock-exam-by-pickett.pdf
Question 8
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company's engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company's marketing team tracks business metrics such as conversion rates and revenue impact in its own systems.
The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics.
Which solution will meet these requirements?
A. Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.
B. Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions. Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.
C. Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.
D. Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.
Show Answer
Correct Answer: D
Explanation: CloudWatch can combine native operational metrics with imported/custom business metrics on dashboards. CloudWatch composite alarms, together with anomaly detection, provide automated detection of correlated operational issues and Amazon SNS can notify stakeholders. This satisfies unified visibility and automatic alerting without requiring additional visualization or remediation services.
Question 8
Company configures a landing zone in AWS Control Tower. The company handles sensitive data that must remain within the European Union. The company must use only the eu-central-1 Region. The company uses SCPs to enforce data residency policies. GenAI developers at the company are assigned IAM roles that have full permissions for Amazon Bedrock.
The company must ensure that GenAI developers can use the Amazon Nova Pro model through Amazon Bedrock only by using cross-Region inference (CRI) and only in eu-central-1. The company enables model access for the GenAI developer IAM roles in Amazon Bedrock. However, when a GenAI developer attempts to invoke the model through the Amazon Bedrock Chat/Text playground, the GenAI developer receives the following error.
User: arn:aws:sts::123456789012:assumed-role/AssumedDevRole/DevUserName
Action: bedrock:InvokeModelWithResponseStream
On resource(s): arn:aws:bedrock:eu-west-3::foundation-model/amazon.nova-pro-v1:0
Context: a service control policy explicitly denies the action
The company needs a solution to resolve the error. The solution must retain the company's existing governance controls and must provide precise access control. The solution must comply with the company's existing data residency policies.
Which combination of solutions will meet these requirements? (Choose two.)
A. Add an AdministratorAccess policy to the GenAI developer IAM role.
B. Extend the existing SCPs to enable CRI for the eu.amazon.nova-pro-v1:0 inference profile.
C. Enable Amazon Bedrock model access for Amazon Nova Pro in the eu-west-3 Region.
D. Validate that the GenAI developer IAM roles have permissions to invoke Amazon Nova Pro through the eu.amazon.nova-pro.v1:0 inference profile on all European Union AWS Regions that can serve the model.
E. Extend the existing SCP to enable CRI for the eu.* inference profile.
Show Answer
Correct Answer: B, D
Explanation: Update the SCPs to permit cross-Region inference for the specific EU Nova Pro inference profile, while retaining the existing residency restrictions. Also ensure the developer roles can invoke that inference profile and the underlying Nova Pro model in every EU Region that can serve it. CRI can route the request to a destination Region such as eu-west-3, so permissions and SCP controls must account for those destinations. A broad `eu.*` exception would be less precise, and adding AdministratorAccess or enabling model access in eu-west-3 would not resolve the SCP deny while preserving the required controls.
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