2026 Updated Amazon AIP-C01 Dumps PDF – Want To Pass AIP-C01 Fast [Q57-Q74]

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2026 Updated Amazon AIP-C01 Dumps PDF – Want To Pass AIP-C01 Fast

AIP-C01 Practice Exam Dumps – 99% Marks In Amazon Exam

NEW QUESTION 57
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.
The 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.
Which solution will meet these requirements?

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.

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.

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.

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.

NEW QUESTION 58
A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake.
The data includes some customer personally identifiable information (PII).
The company must design an access control solution that prevents PII from appearing in a production environment. The FM must access only authorized data subsets that have PII redacted from specific data columns. The company must capture audit trails for all data access.
Which solution will meet these requirements?

Create a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.

Configure the FM to authenticate by using AWS Identity and Access Management roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.

Use direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the FM.

Configure the FM to request temporary credentials from AWS Security Token Service. Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.

NEW QUESTION 59
A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds.
The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention.
Which solution will meet these requirements?

Use Amazon Bedrock with provisioned throughput to host model versions. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released.
Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.

Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use Amazon CloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performance falls below thresholds.

Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions.
Use variant weights to shift traffic. Use Amazon CloudWatch and SageMaker Model Monitor to trigger rollbacks. Use EventBridge to roll back deployments if an anomaly is detected.

Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on inference logs.

NEW QUESTION 60
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models.
The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?

Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.

Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends.

Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that automatically adjust baselines for each tool’s token metrics.

Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real time.Manually update CloudWatch alarm thresholds based on trends identified by the Lambda function.

NEW QUESTION 61
A global healthcare company is deploying a GenAI application on Amazon Bedrock to produce treatment recommendations. Regulations vary for each country where the company operates. Some countries require the company to retain all model inputs and outputs for 2 years. Other countries require the company to submit data for local audits only. Medical providers require consistent medical terminology across all locations.
However, the treatment recommendations that the model produces must adapt to local patient demographics.
The solution must also integrate with existing electronic health record (EHR) systems. The application must support up to 10,000 healthcare provider queries every day with sub-second response times. The company must be able to review the application before deployments and approve of prompt changes. The application must produce comprehensive logs for prompts, responses, and user context. Which solution will meet these requirements?

Use AWS CloudTrail to log API calls. Create standard prompts in Amazon Bedrock Prompt Management that include variables for patient demographics. Implement IAM policies to ensure that only approves users can access prompts.

Use Amazon CloudWatch Logs to collect detailed model invocation logs. Store the logs in Amazon S3.
Create parameterized prompts in Amazon Bedrock Prompt Management that include variables for treatment options. Enable prompt versioning and set up an approval workflow.

Create AWS Lambda functions to dynamically generate prompts that enforce clinical language requirements. Use Amazon CloudWatch Logs to track model invocations. Use Amazon SQS queues to implement a prompt approval workflow.

Store prompt templates in Amazon S3. Use S3 Object Lock to implement version control. Use Amazon EventBridge to track model invocations. Use AWS Config to monitor changes to prompt templates.

NEW QUESTION 62
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?

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.

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 with the approval decision. Store all review decisions in Amazon DynamoDB.

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.

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.

NEW QUESTION 63
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?

Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS Lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.

Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.

Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics.

Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants.Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.

NEW QUESTION 64
A healthcare company is deploying an AI system that uses a foundation model (FM) to help clinicians make diagnostic decisions. The company’s ethics board requires the AI system to demonstrate fairness across patient demographic groups and comply with medical AI governance policies. During initial testing, the AI system provides recommendations without clear explanations or decision tracing. Clinicians are unable to review how the AI system produces diagnostic conclusions.
The company needs to implement a solution that provides transparent reasoning for AI outputs, enables systematic fairness testing, and ensures policy compliance for responsible AI use in healthcare settings. The solution must balance comprehensive explainability with real-time performance requirements. The solution must support rapid iteration for bias testing across multiple demographic variables. The solution must integrate seamlessly with existing clinical workflows while maintaining strict data privacy controls. The solution must handle complex medical and regulatory terminology.
Which solution will meet these requirements?

Use Amazon SageMaker Clarify to generate model explanations. Use Amazon Augmented AI (Amazon A2I) to implement human review workflows. Use AWS Config to enforce compliance policies across the AI system.

Use Amazon Comprehend Medical to analyze medical terminology. Use Amazon Textract to process documents. Use AWS CloudFormation to standardize deployment configurations.

Use Amazon Bedrock agent tracing to provide reasoning traces. Use Amazon Bedrock Prompt Management with A/B testing to perform fairness evaluations. Use Amazon Bedrock Guardrails to ensure policy compliance.

Use Amazon CloudWatch to collect performance metrics. Use Amazon EventBridge to trigger compliance checks. Use AWS Lambda functions to generate custom explanation reports.

Option C is the best answer because the scenario is centered on a foundation-model GenAI application that needs explainability, fairness iteration, and responsible AI controls inside an Amazon Bedrock workflow.
Amazon Bedrock Agents support tracing, and AWS documentation states that traces can show the agent’s path from user input to response, including action group inputs and outputs, knowledge base queries, and the reasoning the agent uses to decide which action or query to take. AWS also describes trace data as a way to understand how an agent arrived at a response. This directly addresses clinicians’ need to review diagnostic reasoning and decision flow.
Amazon Bedrock Guardrails are also the right service for policy compliance in GenAI applications. AWS documentation states that Guardrails can implement safeguards aligned with responsible AI policies, configure denied topics, filter harmful content, and remove sensitive information. For healthcare and life sciences GenAI use cases, AWS Prescriptive Guidance recommends evaluating bias, fairness, and hallucinations and implementing guardrails to prevent harmful responses. This supports strict governance and privacy-sensitive clinical workflows.
The prompt testing and A/B testing component supports rapid iteration. AWS guidance for generative AI operations recommends prompt template management, creating and testing prompt variants, using A/B testing workflows for prompt variants, and analyzing performance against metrics. This is relevant for testing fairness behavior across demographic prompt sets and clinical scenarios.
Option A includes SageMaker Clarify, which is useful for bias detection and model explainability, but it is less directly aligned to Bedrock-native real-time tracing and policy enforcement for an FM application.
Option B handles medical text extraction and terminology but not reasoning transparency or governance.
Option D provides operational metrics and custom reports, but not native Bedrock reasoning traces or guardrails. Therefore, option C best meets the GenAI governance requirements.

NEW QUESTION 65
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM’s responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?

Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.

Use Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.

Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.

Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.

NEW QUESTION 66
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:
python
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?

Increase the number of model units (MUs) in the provisioned throughput configuration.

Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.

Add exponential backoff retry logic to handle throttling exceptions during peak hours.

Modify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.

NEW QUESTION 67
A company is using Amazon Bedrock to build a customer-facing AI assistant that handles sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross- Region failover capabilities.
Which solution will meet these requirements?

Configure Amazon Bedrock guardrails with content filters set to high to protect against prompt injection attacks. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.

Configure Amazon Bedrock guardrails with content filters set to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls.

Deploy Amazon Comprehend custom classifiers to detect prompt injection attacks. Use Amazon API Gateway request validation. Use CloudWatch Logs to capture intervention events.

Configure Amazon Bedrock guardrails with custom content filters and word filters set to high.Configure cross-Region guardrail replication for failover. Store logs in AWS CloudTrail for compliance auditing.

NEW QUESTION 68
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?

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.

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.

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.

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.

NEW QUESTION 69
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity.
The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content.
Which solution will meet these requirements?

Configure a single guardrail and set content filters to high for all categories. Set up denied topics for investment advice and include sample phrases to block. Set up sensitive information filters that apply the block action for all PII entities. Apply the guardrail to all model inference calls.

Configure multiple guardrails by using tiered policies. Create one guardrail and set content filters to high. Configure the guardrail to block PII for public interactions. Configure a second guardrail and set content filters to medium. Configure the second guardrail to mask PII for internal use. Configure multiple topic-specific guardrails to block investment advice and set up contextual grounding checks.

Configure a guardrail and set content filters to medium for harmful content. Set up denied topics for investment advice and include clear definitions and sample phrases to block. Configure sensitive information filters to mask PII in responses and to block financial information in inputs. Enable both input and output evaluations that use custom blocked messages for audits.

Create a separate guardrail for each use case. Create one guardrail that applies a harmful content filter.Create a guardrail to apply topic filters for investment advice. Create a guardrail to apply sensitive information filters to block PII. Use AWS Step Functions to chain the guardrails sequentially.

NEW QUESTION 70
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?

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.

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.

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.

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.

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 71
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?

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.

Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.

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.

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.

NEW QUESTION 72
A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ‘ s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?

Use the Amazon Transcribe streaming API with standard settings to convert speech to text. Use Amazon Bedrock batch processing to perform inference. Store call recordings and metadata in Amazon S3. Use S3 Lifecycle policies to manage the storage.

Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table.

Use Amazon Transcribe batch processing to perform post-call analysis. Configure AWS Lambda functions to generate responses by using the Amazon Bedrock InvokeModel operation. Use Amazon CloudWatch to log supervisor feedback.

Use Amazon Transcribe to convert speech to text and to perform real-time analytics. Use Amazon Comprehend to perform sentiment analysis. Use Amazon SQS to queue processing tasks. Run the Amazon Bedrock InvokeModel operation to generate responses.

NEW QUESTION 73
A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted. Which solution will meet these requirements?

Create a usage dashboard for Amazon Q Developer. Use the usage dashboard to track aggregated usage adoption metrics.

Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company’s organization in AWS Organizations.

Collect user-level metrics in Amazon Q Developer. Store the metrics in an Amazon S3 bucket. Use Amazon QuickSight to visualize the usage data. Create dashboards to show adoption metrics for users and teams.

Configure AWS CloudTrail to track all Amazon Q Developer API calls in the company’s organization in AWS Organizations. Use an AWS Lambda function to process the logs. Store the processed logs in Amazon DynamoDB. Create custom dashboards in Amazon Managed Grafana to visualize the data.

NEW QUESTION 74
An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company’s product catalog. The company must resolve these issues.
Which solution will meet this requirement?

Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.

Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.

Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation RAG. Set the PerformanceConfigLatency parameter to optimized.

Store product catalog data in Amazon OpenSearch Service. Validate the model’s product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.

Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model’s parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company’s product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.
Alright, after comparing List B (txt file) against List A (Word file) , I have identified the unique questions.
These questions cover scenarios or architectural configurations that were not present in the existing list.
Here are the unique questions from List B, formatted as requested:

Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Topic 2	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 3	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Topic 4	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
Topic 5	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.

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2026 Updated Amazon AIP-C01 Dumps PDF – Want To Pass AIP-C01 Fast [Q57-Q74]

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