AIP Question Bank · Latest 50 Questions Free Preview (Page 4/5)
Questions 31–40 of the latest 50 AIP questions (5 pages total), with answers and explanations, no login required. Practice all 98 questions online.
Question 31 · No. 78 · Single choice
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians. The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?
- A. Create a custom approval workflow by using AWS Lambda functions and Amazon SQS queues for human review of AI recommendations. Store all review decisions in Amazon DynamoDB for audit purposes.
- B. Create an AWS Step Functions workflow that has a human approval step that uses the waitForTaskToken API to pause execution. After a human technician completes a review, use an AWS Lambda function to call the SendTaskSuccess API that has the approval decision. Store all review decisions in Amazon DynamoDB.
- C. Create an AWS Glue workflow that has a human approval step. After the human technician review, integrate the application with an AWS Lambda function that calls the SendTaskSuccess API. Store all human technician review decisions in Amazon DynamoDB.
- D. Configure Amazon EventBridge rules with custom event patterns to route AI recommendations to human technicians for review. Create AWS Glue jobs to process human technician approval queues. Use Amazon ElastiCache to cache all human technician review decisions.
Answer: B
Explanation: Option B is the correct answer because AWS Step Functions provides a native pattern for human-in-the-loop approval workflows. The waitForTaskToken API is specifically designed to pause workflow execution until a human task is completed, and SendTaskSuccess is used to resume the workflow with the approval decision. This is the AWS-recommended pattern for human oversight workflows. DynamoDB is appropriate for storing audit decisions because it provides durable, queryable storage. Option A uses SQS which lacks the orchestration and task tracking capabilities needed. Option C incorrectly uses AWS Glue, which is designed for ETL jobs, not human approval workflows, and doesn't have native human approval steps. Option D uses ElastiCache, which is an in-memory cache unsuitable for durable audit storage, and doesn't follow the standard human approval pattern.
Question 32 · No. 79 · Single choice
A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents.
The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions.
Which solution will meet these requirements?
- A. Create multiple prompt variants by using Prompt management in Amazon Bedrock. Manually test the prompts with simple clinical documents. Deploy the highest performing version by using the Amazon Bedrock console.
- B. Implement version control for prompts in a code repository with a test suite that contains complex clinical documents and quantifiable evaluation metrics. Use an automated testing framework to compare prompt versions and document performance patterns.
- C. Deploy each new prompt version to separate Amazon Bedrock API endpoints. Split production traffic between the endpoints. Configure Amazon CloudWatch to capture response metrics and user feedback for automatic version selection.
- D. Create a custom prompt evaluation flow in Amazon Bedrock Flows that applies the same clinical document inputs to different prompt variants. Use Amazon Comprehend Medical to analyze and score the factual accuracy of each version.
Answer: B
Explanation: Option B is the correct choice because it addresses all three requirements specified in the question. First, it diagnoses inconsistencies by including a test suite with complex clinical documents, which is exactly where the current system struggles. Second, it compares prompt performance against established metrics through quantifiable evaluation metrics and an automated testing framework. Third, it maintains historical records of prompt versions through version control in a code repository, which inherently tracks changes and allows rollback if needed. Option A fails because it only tests simple documents (where the system already works well) and uses manual testing. Option C focuses on A/B testing in production rather than systematic diagnosis against established metrics, and using user feedback for clinical document evaluation is problematic. Option D uses Bedrock Flows and Comprehend Medical but doesn't clearly provide the historical version tracking requirement through a proper version control system.
Question 33 · No. 80 · Single choice
A software company is using Amazon Q Business to build an AI assistant that allows employees to access company information and personal information by using natural language prompts. The company stores this information in an Amazon S3 bucket.
Each department in the company has a dedicated prefix in the S3 bucket. Each object name includes the S3 prefix of the department that it belongs to. Each department can belong to only a single group in AWS IAM Identity Center. Each employee belongs to a single department.
The company configures Amazon Q Business to access data stored in an S3 bucket as a data source. The company needs to ensure that the AI assistant respects access controls based on the user's IAM Identity Center group membership.
Which solution will meet this requirement with the LEAST operational overhead?
- A. Create a JSON file named acl.json in each department folder. In each file, create access control entries that specify the IAM Identity Center group that should have access to that department's data. Indicate the location of the JSON file in the Access Control section of the data source settings.
- B. Create a single JSON file named acl.json at the top level of the S3 bucket. Add access control entries that map each department's S3 prefix to its corresponding IAM Identity Center group. Indicate the location of the JSON file in the Access Control section of the data source settings.
- C. For each IAM Identity Center group, create a separate permissions set that denies access to all prefixes in the S3 bucket. Add a StringNotEquals condition key to the permissions set for each group that specifies the department each group is associated with. Attach the permissions sets to the Identity Center groups.
- D. Create a metadata file named metadata.json at the top level of the S3 bucket. Add an AccessControlList object to the file that specifies the S3 path of each department's prefix. Specify the IAM Identity Center group that should have access to each department's prefix. Reference the file location in the data source metadata settings.
Answer: B
Explanation: The correct answer is B. Amazon Q Business supports document-level access control through Access Control Lists (ACLs) defined in JSON files stored in the S3 bucket. By creating a single acl.json file at the top level of the S3 bucket and mapping each department's S3 prefix to its corresponding IAM Identity Center group, this solution minimizes operational overhead compared to Option A (which would require creating and maintaining multiple ACL files, one per department). Option C involves complex IAM permissions configuration which is more operationally intensive. Option D is incorrect because Amazon Q Business uses acl.json files for access control configuration, not metadata.json with AccessControlList objects referenced in metadata settings. Option B provides the centralized, single-file approach that meets the requirement with the least operational overhead.
Question 34 · No. 81 · Single choice
A hospital is building an AI application to help medical clinicians to make treatment decisions. The application uses Amazon Bedrock to analyze patient case histories and suggest diagnoses. The application must maintain sub-500 ms response times to integrate with the hospital’s existing real-time clinical workflow. To comply with privacy regulations, the application must log all per-sonally identifiable information (PII) handling decisions for audits. The application must detect and remove PII from responses with at least 99% accuracy. After initial deployment, clinicians report that diagnostic summaries from the application occa-sionally include patient names and medical record numbers that were not present in the original case history inputs. An investigation reveals that Amazon Comprehend Medical successfully de-tects and removes PII from inputs with 95% accuracy, and the application replaces all detected entities with tokens before it sends inputs to Amazon Bedrock. However, the application contin-ues to generate patient-identifying information in approximately 3-5% of outputs. The company needs a solution to prevent the application from displaying PII in outputs while meeting all other operational requirements. Which solution will meet these requirements?
- A. Configure Amazon Bedrock guardrails with sensitive information filters to detect and block PII in model outputs.
- B. Implement a secondary PII detection layer by using regular expressions and custom entity recognition to detect identifiers that Amazon Comprehend Medical misses before sending inputs to Amazon Bedrock.
- C. Remove detailed medical context from case histories during pre-processing to prevent the model from generating patient-specific information based on clinical pattern associations.
- D. Enable session isolation in Amazon Bedrock API calls. Clear conversation history between requests to prevent patient information from persisting across multiple case analyses.
Answer: A
Explanation: Option A is the best solution because Amazon Bedrock Guardrails with sensitive information filters directly addresses the problem of PII appearing in model outputs. The issue is that even though Amazon Comprehend Medical detects and removes PII from inputs (with 95% accuracy), the model still generates new PII in 3-5% of outputs. Bedrock Guardrails can filter and block PII in outputs before they reach users, can be configured to meet the 99% accuracy requirement, supports audit logging for compliance, and maintains low latency to meet the sub-500ms response time requirement. Option B only addresses input PII, not output PII generation. Option C would degrade the application's diagnostic quality. Option D addresses cross-session contamination but doesn't prevent PII generation within a single request.
Question 35 · No. 82 · Single choice
A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type.\nApplication users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing.\nThe company needs a solution to fix these performance issues and enhance the user experience.\nWhich solution will meet these requirements?
- A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
- B. Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
- C. Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
- D. Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.
Answer: A
Explanation: The AWS Amplify AI Kit is specifically designed to integrate AI services like Amazon Bedrock with Amplify applications. It provides built-in support for streaming responses from GraphQL APIs, which directly addresses the timeout issue for complex questions that take longer to process. By implementing streaming responses, users see results incrementally rather than waiting for the entire response, which significantly improves user experience. Additionally, the kit optimizes client-side rendering. Option B doesn't solve the root cause as timeouts will still occur for complex queries, and retry logic would only add more waiting time. Option C introduces polling delays via SQS, which would worsen the user experience rather than enhance it. Option D requires major architectural changes (WebSocket API and different Bedrock API), making it overly complex compared to using the purpose-built Amplify AI Kit.
Question 36 · No. 83 · Single choice
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.\nWhich 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.
Answer: B
Explanation: chose B because hierarchical chunking with parent-child relationships is ideally suited for technical documents, which typically have inherent hierarchical structures (chapters, sections, subsections). The parent chunks (8,000 tokens) preserve broader contextual information while child chunks (2,000 tokens) provide specific details. Amazon Bedrock Knowledge Bases is a fully managed AWS service specifically designed for retrieval-augmented generation (RAG) with large documents, providing built-in retrieval that automatically selects relevant parent chunks based on query context. This approach ensures consistent and complete responses by maintaining semantic continuity through overlap tokens. Option A is flawed because sequentially linking chunks to reach the 200,000-token limit is unreliable and contradicts the purpose of chunking. Option C's semantic chunking doesn't preserve document structure as effectively for technical documents. Option D requires custom implementation without leveraging AWS managed services and risks losing cross-segment context when processing segments independently.
Question 37 · No. 84 · Single choice
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.\nWhich 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.
Answer: B
Explanation: Option B is the correct answer because: 1) Step Functions Standard workflows with the Wait for Callback pattern (using .waitForTaskToken) are specifically designed to orchestrate workflows that need to pause and wait for external input, which is exactly what clarification workflows need when handling ambiguous queries. 2) DynamoDB with on-demand capacity automatically scales to handle thousands of concurrent users with single-digit millisecond latency, meeting the performance requirement. 3) DynamoDB has server-side encryption enabled by default, satisfying the security requirement. 4) Using session IDs as the primary key in DynamoDB is an efficient pattern for retrieving user conversation history quickly. The other options fail because: Option A uses Express workflows (5-minute limit, not ideal for clarification waiting), and RDS doesn't scale well for thousands of concurrent fast responses. Option C using S3 with separate JSON files would require multiple file retrievals to reconstruct conversation context, which is inefficient. Option D uses SQS which is a message queue, not an orchestrator suitable for managing clarification workflow state.
Question 38 · No. 85 · Single choice
A software as a service (SaaS) company is building a recommendation model that uses Amazon SageMaker AI to support an application that recommends airline cabin upgrades to customers. The company will host SageMaker AI models on Amazon Bedrock by using Amazon Bedrock Custom Model Import. Airline companies will use the application to send customized offers to customers. The model must examine the travel history of customers to help make more relevant recommendations. The company stores customer travel history data in an Amazon RDS database. The company must ensure that the application delivers consistent, relevant, and accurate results across multiple airlines and customer populations. Which solution will meet these requirements?
- A. Use Amazon Bedrock Knowledge Bases to implement a RAG architecture to analyze customer travel history data to give the application semantic search capabilities. Use the semantic search capabilities to retrieve relevant booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
- B. Implement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
- C. Use Amazon OpenSearch Service to implement vector searches of customer travel history embeddings. Use the vector searches to give the application the ability to perform similarity-based retrieval of booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.
- D. Implement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.
Answer: B
Explanation: The question requires a solution that delivers consistent, relevant, and accurate results across multiple airlines using structured customer travel history data stored in Amazon RDS. Option B is the best choice because: (1) Text-to-SQL transformations are the most appropriate method for retrieving data from structured relational databases like Amazon RDS, enabling precise queries on booking patterns, preferences, and loyalty information. (2) SQL validations ensure the accuracy and consistency of retrieved data, which is critical for delivering the same quality of recommendations across different airlines. (3) Using AWS Step Functions and AWS Lambda to orchestrate validation workflows provides a deterministic, rule-based approach to reduce hallucinations, ensuring reliable and reproducible results. In contrast, Option A's RAG architecture with Knowledge Bases is better suited for unstructured data; Option C's vector searches with OpenSearch work well on embeddings rather than structured relational data; and Option D's combination of semantic similarity searches with text-to-SQL introduces non-deterministic behavior that could lead to inconsistencies across different customer populations, failing the key requirement of consistent results across multiple airlines.
Question 39 · No. 86 · Single choice
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.\nThe 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.\nWhich solution will meet these requirements?
- A. Develop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to estimate token usage before sending requests to Amazon Bedrock. Configure the Lambda function to publish metrics to Amazon CloudWatch and trigger alarms when requests approach thresholds. Store detailed token usage in Amazon DynamoDB to report costs.
- B. Implement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected requests. Configure Amazon EventBridge rules to trigger notifications based on Amazon Bedrock Guardrails metrics. Use Amazon CloudWatch dashboards to visualize token usage trends across models.
- C. Deploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda function to analyze token-related failures. Use Amazon CloudWatch Logs Insights to generate reports on token usage patterns based on error logs from Amazon Bedrock API responses.
- D. Use Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request throttling based on custom usage plans with predefined token quotas. Configure API Gateway to reject requests that will exceed token limits.
Answer: A
Explanation: Option A is the best solution because it addresses all three requirements: (1) Proactive alerting - the Lambda function estimates token usage BEFORE sending requests to Amazon Bedrock and triggers CloudWatch alarms when approaching limits; (2) High throughput - Lambda functions can scale to handle more than 5,000 requests per minute; (3) Cost allocation - DynamoDB stores detailed token usage metrics that can be reported across business units. Option B only captures metrics on rejected requests (reactive, not proactive) and Bedrock Guardrails is primarily for content filtering, not token quota management. Option C is reactive (handles failures after they occur) and doesn't provide proactive alerting. Option D uses API Gateway throttling which is for rate limiting, not token-level tracking, and cannot accurately measure token usage based on input/output content.
Question 40 · No. 87 · Single choice
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations. Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.\nWhich solution will meet these requirements?
- A. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
- B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations. Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
- C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
- D. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.
Answer: C
Explanation: Option C is the correct answer because it best meets all the requirements with minimal custom development. Amazon Bedrock Guardrails with contextual grounding checks is a native, built-in feature specifically designed to detect hallucinations in near real-time by evaluating whether model responses are grounded in the source material. This is essential for the healthcare company's clinical document summarization use case. Amazon CloudWatch anomaly detection alarms provide automatic early warnings of cost anomalies and abnormal token consumption without requiring custom code or Lambda functions. Both services are fully managed, reducing maintenance overhead. In contrast, Option A requires building custom Glue and Athena workflows, Option B requires a custom Lambda function and relies on batch evaluation jobs that are not near real-time, and Option D involves CloudTrail (auditing, not monitoring) and SageMaker Model Monitor (designed for traditional ML models, not Bedrock LLM outputs), all of which require significant custom development and maintenance.