A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities.
Which solution will meet these requirements?
A. Store the active model provider in AWS Systems Manager Parameter Store. Configure a Lambda function to read the parameter at runtime to determine which model to invoke.
B. Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.
C. Configure an Amazon API Gateway REST API to route requests to separate Lambda functions. Hardcode each Lambda function to a specific model provider. Switch the integration target manually.
D. Store the active model provider in a JSON file hosted on Amazon S3. Use AWS AppConfig to reference the S3 file as a hosted configuration source. Configure a Lambda function to read the file through AppConfig at runtime to determine which model to invoke.
Show Answer
Correct Answer: B
Explanation: AWS AppConfig is designed for runtime application configuration with deployment strategies for safe rollouts, built-in validation, monitoring, and automatic rollback. A Lambda function can fetch the active model provider configuration at runtime, allowing provider switches without code changes or redeployment. Systems Manager Parameter Store does not provide the same deployment, validation, and rollback capabilities. Using API Gateway with hardcoded Lambdas requires manual changes. Referencing an S3 file through AppConfig is unnecessary because AppConfig can natively manage hosted configuration while providing the required deployment features.
Question 42
A financial services company is developing a customer service AI assistant application that uses a foundation model (FM) in Amazon Bedrock. The application must provide transparent responses by documenting reasoning and by citing sources that are used for Retrieval Augmented Generation (RAG). The application must capture comprehensive audit trails for all responses to users. The application must be able to serve up to 10,000 concurrent users and must respond to each customer inquiry within 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
A. Enable tracing for Amazon Bedrock agents. Configure structured prompts that direct the FM to provide evidence presentations. Integrate Amazon Bedrock knowledge bases with data sources to enable RAG. Configure the application to reference and cite authoritative content. Deploy the application in a Multi-AZ architecture. Use Amazon API Gateway and AWS Lambda functions to scale the application. Use Amazon CloudFront to provide low-latency delivery.
B. Enable tracing for Amazon Bedrock agents. Integrate a custom RAG pipeline with Amazon OpenSearch Service to retrieve and cite sources. Configure structured prompts to present retrieved evidence. Deploy the application behind an Amazon API Gateway REST API. Use AWS Lambda functions and Amazon CloudFront to scale the application and to provide low latency. Store logs in Amazon S3 and use AWS CloudTrail to capture audit trails.
C. Use Amazon CloudWatch to monitor latency and error rates. Embed model prompts directly in the application backend to cite sources. Store application interactions with users in Amazon RDS for audits.
D. Store generated responses and supporting evidence in an Amazon S3 bucket. Enable versioning on the bucket for audits. Use AWS Glue to catalog retrieved documents. Process the retrieved documents in Amazon Athena to generate periodic compliance reports.
Show Answer
Correct Answer: A
Explanation: Using Amazon Bedrock native capabilities minimizes operational overhead. Bedrock Agents tracing provides reasoning traces and execution visibility, while Bedrock Knowledge Bases provide managed RAG with source attribution/citations. API Gateway, Lambda, CloudFront, and Multi-AZ deployment provide a fully managed, scalable architecture capable of handling high concurrency with low latency. Option B introduces a custom RAG pipeline with OpenSearch, increasing operational burden. Options C and D do not satisfy the transparency, RAG citation, audit, and scalability requirements.
Question 43
A company uses Amazon Bedrock to develop an AI assistant to provide customer support. Analysis shows that 40% of customer queries use varied phrasing or wording to ask the same questions.
The company wants a solution to reduce redundant model calls. The solution must ensure that semantically equivalent questions receive consistent answers. The solution must ensure low latency.
Which solution will meet these requirements?
A. Deploy an Amazon DynamoDB Accelerator (DAX) cluster as an in-memory cache. Specify the query text as the partition key and the model response text as the sort key. Query the cache by using a filter expression with the LIKE operator.
B. Use Amazon Bedrock to generate embeddings from customer queries. Use Amazon MemoryDB for Valkey to store hash sets of vector embeddings and model responses. Use a RANGE query to find similar queries and their responses.
C. Deploy Amazon OpenSearch Service that has k-nearest neighbor (k-NN) capabilities to store query-response text pairs. Use an approximate k-NN technique to find similar queries and their responses.
D. Create a caching solution by using Amazon DynamoDB to create a global secondary index on the normalized query text. Apply stemming to incoming queries. Query the index of cached customer queries.
Show Answer
Correct Answer: C
Explanation: Semantically equivalent queries are best handled with vector embeddings and similarity search rather than exact text matching or stemming. Amazon OpenSearch Service with k-nearest neighbor (k-NN) supports efficient approximate vector similarity search, enabling low-latency retrieval of previously cached query-response pairs so equivalent questions receive consistent answers. DAX and DynamoDB text normalization do not provide semantic matching, and MemoryDB for Valkey does not support RANGE queries for vector similarity in the manner described.
Question 44
A wildlife conservation agency operates zoos globally. The agency uses various sensors, trackers, and audiovisual recorders to monitor animal behavior. The agency wants to launch a generative AI (GenAI) assistant that can ingest multimodal data to study animal behavior.
The GenAI assistant must support natural language queries, avoid speculative behavioral interpretations, and maintain audit logs for ethical research audits.
Which solution will meet these requirements?
A. Ingest raw videos into Amazon Rekognition to detect animal postures and expressions. Use Amazon Data Firehose to stream sensor and GPS data into an Amazon S3 data lake. Prompt an Amazon Bedrock foundation model (FM) by using basic templates that are stored in AWS Systems Manager Parameter Store. Use IAM policies to control access. Use AWS CloudTrail for audit logging.
B. Use Amazon SageMaker Processing and Amazon Transcribe to pre-process multimodal data. Ingest summaries into an Amazon Bedrock Retrieval Augmented Generation (RAG) knowledge base. Apply Amazon Bedrock guardrails to restrict speculative outputs. Use AWS AppConfig to manage prompt templates. Use AWS CloudTrail to log research activity for audits.
C. Use Amazon OpenSearch Serverless to index behavioral logs and telemetry events. Use Amazon Comprehend to extract entities. Use Amazon Bedrock to build a layer to answer questions. Embed study summaries into OpenSearch Serverless documents. Use IAM to control access. Use AWS CloudTrail to log user interactions with the AI assistant.
D. Configure Amazon Q Business to federate data across Amazon S3, Amazon Kinesis, and Amazon SageMaker Feature Store. Configure Amazon EventBridge to invoke data ingestion jobs. Use custom AWS Lambda functions to filter large language model (LLM) outputs for ethical compliance before returning results to users.
Show Answer
Correct Answer: B
Explanation: Option B best satisfies all requirements. It preprocesses multimodal inputs, uses Amazon Bedrock Retrieval Augmented Generation (RAG) to ground responses in ingested knowledge for natural language queries, applies Amazon Bedrock Guardrails to reduce speculative behavioral interpretations, and uses AWS CloudTrail to maintain audit logs. The other options lack a strong grounding mechanism and/or Bedrock Guardrails, or rely on custom filtering instead of purpose-built controls.
Question 45
A company upgraded its Amazon Bedrock powered foundation model (FM) that supports a multilingual customer service assistant. After the upgrade, the assistant exhibited inconsistent behavior across languages. The assistant began generating different responses in some languages when presented with identical questions.
The company needs a solution to detect and address similar problems for future updates. The evaluation must be completed within 45 minutes for all supported languages. The evaluation must process at least 15,000 test conversations in parallel. The evaluation process must be fully automated and integrated into the CI/CD pipeline. The solution must block deployment if quality thresholds are not met.
Which solution will meet these requirements?
A. Create a distributed traffic simulation framework that sends translation-heavy workloads to the assistant in multiple languages simultaneously. Use Amazon CloudWatch metrics to monitor latency, concurrency, and throughput. Run simulations before production releases to identify infrastructure bottlenecks.
B. Deploy the assistant in multiple AWS Regions with Amazon Route 53 latency-based routing and AWS Global Accelerator to improve global performance. Store multilingual conversation logs in Amazon S3. Perform weekly post-deployment audits to review consistency.
C. Create a pre-processing pipeline that normalizes all incoming messages into a consistent format before sending the messages to the assistant. Apply rule-based checks to flag potential hallucinations in the outputs. Focus the evaluation on the normalized text to simplify testing across languages.
D. Set up standardized multilingual test conversations with identical meaning. Run the test conversations in parallel by using Amazon Bedrock model evaluation jobs. Apply similarity and hallucination thresholds. Integrate the process into the CI/CD pipeline to block releases that fail.
Show Answer
Correct Answer: D
Explanation: The requirement is to automatically detect multilingual quality regressions before deployment, evaluate large numbers of conversations in parallel, integrate with CI/CD, and block releases when thresholds are not met. Amazon Bedrock model evaluation jobs are designed for automated model evaluation using standardized datasets and metrics. Using standardized multilingual test conversations with equivalent meaning, evaluating similarity and hallucination metrics in parallel, and enforcing quality gates in the CI/CD pipeline directly satisfies the functional and operational requirements. The other options focus on infrastructure performance, post-deployment review, or preprocessing rather than systematic multilingual model quality evaluation.
Question 46
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company's data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application's decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?
A. Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes and data processing activities. Manually track compliance certifications across Regions.
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit the decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
C. Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.
D. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring to track model performance and compliance with data residency requirements. Create manual reports for each regulatory jurisdiction.
Show Answer
Correct Answer: C
Explanation: The requirements are to keep data processing and storage within the same continent, provide audit trails, and classify data. Option C keeps preprocessing within the originating Region before sending data to Amazon Bedrock, uses Region-specific S3 bucket policies to support residency, Amazon Macie for data classification, and AWS CloudTrail immutable logs for auditing. Option A uses Bedrock cross-Region inference, which can route requests across Regions and conflicts with strict residency requirements. Option B focuses on Organizations and model deployment but does not address data classification. Option D replaces Bedrock with SageMaker and relies on manual compliance reporting, which does not best meet the stated requirements.
Question 47
An international company is building an AI assistant that uses RAG. The company wants the AI assistant to have near real-time, low-latency performance. The AI assistant must provide service to several geographic areas. The company's customers will use proprietary data with the AI assistant. The proprietary data must not leave the company's immediate geographic area.
Which solution will meet these requirements?
A. Deploy an Amazon Bedrock model with a cross-Region model inference profile. Create Amazon S3 buckets in each AWS Region the company operates in. Store a knowledge base in each respective S3 bucket. In each Region, configure Amazon Kendra to interact with the respective knowledge base. In each Region, configure an AWS Lambda function that uses Kendra and Amazon Bedrock to process AI assistant prompts.
B. Deploy an Amazon Bedrock model in each AWS Region the company operates in. Configure an Amazon Bedrock cross-Region model inference profile. Configure a vector database that uses Amazon Bedrock Knowledge Bases. Store the knowledge bases in Amazon S3 in each Region the company operates in.
C. Use AWS Outposts to deploy an outpost in each AWS Region the company operates in. Create Amazon S3 buckets to store knowledge bases in each corresponding Region. Deploy Amazon RDS configured as a vector database to each outpost. Deploy an Amazon Bedrock model with a cross-Region inference profile in a central Region.
D. Configure a knowledge base stored in the Amazon S3 Express One Zone storage class in each AWS Local Zone the company operates in. Use Amazon RDS to deploy a vector database in each Local Zone the company operates in. Deploy a large language model (LLM) to Amazon EC2 instances in each Local Zone. Configure the AI assistant to route prompts to the model in the respective Local Zone.
Show Answer
Correct Answer: B
Explanation: The requirements are multi-geography, near real-time low latency, RAG, and data residency. Deploying Amazon Bedrock and Knowledge Bases in each Region keeps proprietary data local while providing regional inference. A cross-Region inference profile can improve availability/capacity but requests are still handled with regional deployments and regional knowledge bases. Option A uses Kendra instead of the native Bedrock Knowledge Bases pattern. Option C centralizes Bedrock inference, violating the data locality requirement. Option D relies on self-managed LLMs on EC2 in Local Zones rather than the managed Bedrock-based architecture expected by the scenario.
Question 48
A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock knowledge base that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket.
The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time.
The company determines that the underlying models will not benefit from additional fine tuning. The company must improve retrieval accuracy by applying smarter constraints. The company wants a solution that requires minimal changes to the existing architecture.
Which solution will meet these requirements?
A. Enhance embeddings by using a domain-adapted model that is specifically trained on emergency news content for improved vector similarity.
B. Migrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the scope of results retrieval.
C. Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3 object metadata.
D. Migrate to an Amazon Q Business index to perform structured metadata filtering and document categorization during retrieval.
Show Answer
Correct Answer: C
Explanation: The problem is excessive retrieval scope rather than embedding quality. Amazon Bedrock Knowledge Bases support metadata-aware filtering by indexing S3 object metadata, allowing retrieval to be constrained by attributes such as content type, date, or other metadata. This improves relevance and response time while requiring minimal architectural changes because it builds on the existing Bedrock knowledge base. Options B and D require migration to different services, and A focuses on embeddings even though the scenario states fine-tuning/model changes are not the solution.
Question 49
An ecommerce company is using Amazon Bedrock to build a customer service AI assistant. The AI assistant needs to process over 50,000 customer inquiries every day. The AI assistant occasionally experiences traffic spikes of up to 150,000 inquiries every day during promotional events. Analysis shows that 40% of inquiries follow similar patterns that share the same context.
A GenAI developer must design a solution that will ensure low latency and consistent performance for the AI assistant during traffic spikes.
Which solution will meet these requirements MOST cost-effectively?
A. Configure latency-optimized inference by setting the latency parameter to optimized in the performance configuration of the request to Amazon Bedrock. Use prompt caching to handle the repetitive inquiries.
B. Purchase provisioned throughput and model units (MUs) that are sized to handle peak traffic loads. Use Amazon ElastiCache (Redis OSS) to cache repetitive inquiries.
C. Use Amazon Bedrock Agents and custom knowledge bases to pre-process customer inquiries. Configure cross-Region inference to distribute traffic.
D. Use AWS Lambda functions to pre-process requests by using a custom prompt routing mechanism. Use Amazon DynamoDB as a caching layer to handle frequently asked questions.
Show Answer
Correct Answer: A
Explanation: Latency-optimized inference in Amazon Bedrock improves response latency while retaining the on-demand, auto-scaling consumption model, avoiding the cost of provisioning for peak demand. Prompt caching is a native Bedrock feature that is well suited when many requests share the same context, reducing repeated prompt processing, latency, and cost for the 40% of similar inquiries. Provisioned throughput sized for peak is more expensive, and the other options do not directly address low-latency inference as cost-effectively.
Question 50
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: The key differentiator is the need to run clarification workflows that can pause while waiting for additional user input. AWS Step Functions Standard supports the Wait for a Callback pattern, whereas Express does not. DynamoDB with on-demand capacity provides low-latency, highly scalable storage for conversation history and server-side encryption satisfies the encryption requirement. This combination also supports thousands of concurrent users better than RDS or S3 for conversational state, while ElastiCache alone is not appropriate as the durable record of conversations.
Sources:
https://www.p2pexams.com/free-questions/amazon-aip-c01-dumps-by-bowen.pdf
https://dev.to/aws-builders/aws-certified-generative-ai-developer-professional-aip-c01-study-reference-4h84
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