Amazon SageMaker endpoints support multiple production variants, which is the feature designed for A/B testing. By updating the existing endpoint configuration to add the new model as a second ProductionVariant, the firm can use the InitialVariantWeight parameter to divert a small percentage of live inference traffic to the new model. This allows for evaluating its accuracy and latency on real-world data. This approach is seamless to the client application, as it continues to invoke the same single endpoint URL. The existing model handles the majority of the traffic, ensuring its throughput is not significantly impacted, thus meeting all the scenario's requirements.

A. Deploying separate endpoints would require client-side changes to call the new endpoint or a complex traffic duplication mechanism, violating the "no changes to how clients invoke" requirement.

B. This describes a blue/green deployment strategy, which replaces the old model entirely rather than running both simultaneously for comparative evaluation on live traffic.

C. While technically feasible, this adds an unnecessary component (API Gateway) and complexity. SageMaker provides a native, more direct solution for A/B testing that doesn't require an intermediary service.

1. AWS SageMaker Developer Guide, "Test models in production": This section explicitly details the use of production variants for A/B testing. It states, "To test models in production, you can deploy multiple models to the same SageMaker endpoint... You configure the production variants to distribute traffic between two or more models by providing the percentage of traffic to route to each variant."

Source: AWS SageMaker Developer Guide, Section: "Deploy models for inference" -> "Test models in production".

2. AWS SageMaker API Reference, CreateEndpointConfig Action: The documentation for this API call defines the ProductionVariants parameter. It specifies that you can provide a list of variants, each with its own ModelName, InstanceType, and InitialVariantWeight, which "determines the proportion of traffic that flows to a variant."

Source: AWS SageMaker API Reference, CreateEndpointConfig -> ProductionVariants -> InitialVariantWeight.

3. AWS Machine Learning Blog, "A/B testing machine learning models in production using Amazon SageMaker": This official blog post provides a practical walkthrough. It confirms, "Amazon SageMaker makes this easy by allowing you to specify multiple models, or model variants, in the same endpoint. You can distribute traffic to each model by specifying the traffic distribution, or weight, for each model."

Source: AWS Machine Learning Blog, Post Title: "A/B testing machine learning models in production using Amazon SageMaker", Published: 20 OCT 2020.

Amazon Fraud Detector is a fully managed AWS service specifically designed for real-time fraud detection. It allows businesses to build and train fraud detection models without deep machine learning expertise. The service provides a simple API endpoint (GetEventPrediction) that applications can call to get a real-time fraud risk score and a decision outcome (e.g., Approve, Review, Deny) based on configured rules. This directly addresses the scenario's need for a real-time, automated solution that can assess and reject transactions with minimal operational effort, as AWS manages the underlying infrastructure and model deployment.

A. Amazon Lookout for Vision is a computer vision service for detecting defects in industrial products and is not suitable for analyzing financial transaction data.

B. Amazon Comprehend is a natural language processing (NLP) service. Retraining a model is a batch process and does not provide the required real-time inference capability.

C. While this approach can provide real-time inference, it requires significant operational effort to build, deploy, and manage custom code on EC2, contradicting the requirement for minimal effort.

1. Amazon Fraud Detector Developer Guide: In the "What is Amazon Fraud Detector?" section, the documentation states, "Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities... Amazon Fraud Detector automates the time consuming and expensive steps to build, train, and deploy an ML model for fraud detection... For a real-time fraud prediction, your application calls the GetEventPrediction API operation." This confirms its suitability for the real-time, automated, and low-effort requirements.

Source: AWS Documentation, Amazon Fraud Detector Developer Guide, "What is Amazon Fraud Detector?".

2. Amazon SageMaker Developer Guide: The guide details deployment options, including self-hosting on Amazon EC2. This section implicitly highlights the user's responsibility for "provisioning and managing the EC2 instances," which contrasts with the "minimal operational effort" required by the scenario.

Source: AWS Documentation, Amazon SageMaker Developer Guide, "Deploy a Model on Amazon EC2".

3. Amazon Lookout for Vision Developer Guide: The "What is Amazon Lookout for Vision?" section clearly defines its purpose: "You can use Amazon Lookout for Vision to find visual defects in industrial products, accurately and at scale." This confirms it is inappropriate for financial transaction analysis.

Source: AWS Documentation, Amazon Lookout for Vision Developer Guide, "What is Amazon Lookout for Vision?".

Amazon Kendra is a highly accurate and intelligent search service powered by machine learning. It is specifically designed to understand natural language and perform semantic searches on unstructured data. By using the native Amazon Kendra S3 connector, the company can directly ingest its document repository. Kendra automatically handles the complex pipeline of document processing, indexing, and creating a semantic search engine. This provides a fully managed and optimized solution for the retrieval component (the 'R' in RAG), directly addressing the need for context-aware results based on the deeper meaning of queries.

A. Amazon QuickSight is a business intelligence service for creating visualizations and dashboards; it cannot perform semantic searches. DynamoDB is not optimized for vector similarity search.

B. Standard SQL queries do not support the vector similarity operations required for semantic search. SageMaker Feature Store is not a dedicated vector search index.

C. This approach is overly complex and not optimized. While OpenSearch supports vector search, Kendra offers a more integrated, managed, and purpose-built solution for this specific use case.

1. Amazon Kendra Developer Guide, "What is Amazon Kendra?": "Amazon Kendra is an intelligent search service powered by machine learning (ML). Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for... Unlike traditional search, which uses keywords to find content, Kendra uses natural language processing (NLP) and other ML models to understand the intent of a question and the context of documents to find the best answer."

2. AWS Machine Learning Blog, "Build a powerful question answering bot with Amazon Kendra and Amazon Lex": This article demonstrates Kendra's role in understanding user intent and retrieving precise answers, a core function of semantic search in a RAG architecture. It states, "Amazon Kendra is a highly accurate and easy-to-use enterprise search service that’s powered by machine learning."

3. Amazon Kendra Developer Guide, "Amazon S3 data source": This section details the use of the built-in S3 connector to directly index documents stored in S3 buckets, confirming the streamlined ingestion process described in the correct answer. "You can index documents in your Amazon S3 bucket using Amazon Kendra."

4. AWS Documentation, "What Is Amazon QuickSight?": "Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML)-powered business intelligence (BI) service built for the cloud." This confirms QuickSight's purpose is BI, not semantic search.

Amazon Transcribe's custom vocabulary feature is the designated solution for improving transcription accuracy for domain-specific terms, such as product names with uncommon spellings. This feature allows developers to provide a list of specific words and their pronunciations to the ASR engine. The vocabulary can be quickly updated and redeployed as developers test and refine the workflow. This method directly addresses the need for rapid customization iterations to improve recognition of specific terms without the overhead and time required for full model retraining.

A. Amazon Lex is designed for building conversational interfaces (bots), not as a primary transcription service for analyzing pre-recorded audio like voicemails.

B. Fine-tuning a foundation model with Amazon Bedrock is a form of model retraining, which the scenario explicitly seeks to avoid for rapid, lightweight updates.

C. Amazon Kendra is an enterprise search service used to index and query documents; it does not directly participate in or improve the core ASR transcription process.

1. AWS Documentation - Amazon Transcribe Developer Guide: "Custom vocabularies". This guide states, "A custom vocabulary is a list of specific words that you want Amazon Transcribe to recognize in your audio input. These are generally domain-specific words and phrases, such as product names... that are not in the base vocabulary." This directly supports using custom vocabularies for product names. The guide also details the UpdateVocabulary API call, which facilitates the required frequent updates. (Source: AWS Documentation, Amazon Transcribe Developer Guide, Section: "Custom vocabularies").

2. AWS Machine Learning Blog: "Improving domain-specific transcription accuracy with Amazon Transcribe custom vocabularies". This official blog post explains, "Custom vocabularies enable you to provide a list of out-of-vocabulary (OOV) words... to Amazon Transcribe. This helps improve the accuracy of the speech recognition for these specific words." This confirms the feature's purpose and its effectiveness for the scenario's requirements. (Source: AWS Machine Learning Blog, May 21, 2020).

3. AWS Documentation - Amazon Lex V2 Developer Guide: "What Is Amazon Lex V2?". The documentation defines Lex as "a service for building conversational interfaces into any application using voice and text," confirming it is not the appropriate tool for a pure transcription task. (Source: AWS Documentation, Amazon Lex V2 Developer Guide, Section: "What Is Amazon Lex V2?").

The primary requirement is minimal infrastructure management for a real-time model with moderate, potentially intermittent traffic (up to 50 concurrent requests). Amazon SageMaker Serverless Inference is specifically designed for this use case. It automatically provisions, scales, and manages the underlying compute resources, completely abstracting away server management. The user pays only for the compute time used to process inference requests, making it the solution with the least operational overhead while meeting the real-time performance needs for this scale.

A. A standard SageMaker real-time endpoint requires you to provision and manage specific ML instance types and configure scaling policies, which incurs more operational overhead than a serverless option.

B. An asynchronous SageMaker endpoint is designed for large payloads and long-running inference jobs (up to 15 minutes), not for the low-latency responses required by a real-time fraud detection system.

C. Deploying on EC2 with an Auto Scaling group and a load balancer represents the highest operational overhead, as it requires managing the OS, runtime, scaling, and all underlying infrastructure components.

1. Amazon SageMaker Developer Guide, "Serverless Inference": "With SageMaker Serverless Inference, you can quickly deploy your machine learning models for inference without having to configure and manage the underlying infrastructure... Serverless Inference is ideal for workloads that have intermittent or unpredictable traffic patterns." This directly supports the choice of a serverless endpoint for minimal management.

2. Amazon SageMaker Developer Guide, "Real-time inference": This section details the process of creating a real-time endpoint, which includes "Specify the number and type of ML compute instances to deploy your model to." This confirms the management overhead associated with provisioned endpoints, making option A less suitable.

3. Amazon SageMaker Developer Guide, "Asynchronous Inference": "This option is ideal for requests with large payload sizes... and long processing times... It is not intended for low-latency (sub-second) inference requests." This confirms that asynchronous inference is inappropriate for the real-time requirement.

4. AWS Whitepaper, "Machine Learning Best Practices on AWS", Page 23: The paper discusses hosting options and highlights that "For workloads with infrequent or intermittent traffic patterns... AWS offers Amazon SageMaker Serverless Inference... This option removes the need to select instance types or manage scaling policies." This reinforces that serverless is the best choice for minimizing operational overhead in this scenario.

This solution correctly addresses all three requirements of the scenario. It employs server-side encryption with AWS Key Management Service (SSE-KMS) for data at rest in Amazon S3, providing robust, auditable encryption. AWS Identity and Access Management (IAM) roles are the standard and most secure method for granting least-privilege access to SageMaker resources, S3 data, and model artifacts. Amazon CloudWatch is the native AWS service for monitoring. SageMaker automatically publishes model performance metrics (e.g., latency, invocation counts) to CloudWatch, enabling continuous monitoring and alerting. All communication between SageMaker and other AWS services is encrypted in transit using TLS by default.

A. This option is incorrect because AWS CloudTrail monitors API activity and user access, not the operational performance metrics of a machine learning model like accuracy or latency.

B. This is incorrect as Amazon Macie is a data security service for discovering and protecting sensitive data. It does not monitor the performance metrics of a SageMaker model.

C. This option is critically flawed because it completely fails to address the requirement for data encryption at rest, which was a mandatory condition in the scenario.

1. AWS SageMaker Developer Guide, "Protect Data at Rest Using Encryption": This section details how to protect data in S3 buckets used for model artifacts and datasets. It explicitly recommends using AWS KMS keys (SSE-KMS) for encryption. (aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html)

2. AWS SageMaker Developer Guide, "SageMaker Roles": This documentation explains that SageMaker requires an IAM role to access other AWS services, such as reading data from and writing model artifacts to Amazon S3. This is the fundamental mechanism for access control. (aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)

3. AWS SageMaker Developer Guide, "Monitor Amazon SageMaker with Amazon CloudWatch": This page confirms that SageMaker sends metrics for endpoints, training jobs, and other components to CloudWatch. It states, "You can monitor your Amazon SageMaker resources using Amazon CloudWatch, which collects raw data and processes it into readable, near real-time metrics." This directly supports its use for performance monitoring. (aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html)

4. AWS SageMaker Developer Guide, "Protect Data in Transit": This document confirms that "Amazon SageMaker uses Transport Layer Security (TLS) to encrypt communication between your client and SageMaker API endpoints" and between SageMaker and other AWS services, fulfilling the in-transit encryption requirement. (aws.amazon.com/sagemaker/latest/dg/encryption-in-transit.html)

This solution comprehensively addresses all the specified requirements for a secure, compliant, and observable architecture. It correctly follows the standard workflow of fine-tuning a model in Amazon SageMaker and deploying it in Amazon Bedrock. It meets the security requirement by using AWS KMS for encrypting training data in S3 and the deployed model in Bedrock. It satisfies the compliance requirement by enabling AWS CloudTrail for auditing all API interactions. Finally, it achieves observability by using Amazon CloudWatch to monitor key performance metrics such as latency and throughput, which are critical for production workloads.

A. This option is incorrect because it uses Amazon Macie, which is a data security service for discovering sensitive data, not for performance monitoring like latency and throughput.

B. This option proposes a completely different architecture using Amazon EC2 for inference, which contradicts the scenario's requirement to deploy the model via Amazon Bedrock.

C. This option is incorrect because it omits the critical step of fine-tuning the model, which is a primary requirement of the scenario, and it fails to mention KMS encryption or CloudWatch monitoring.

1. KMS Encryption for Fine-Tuning and Deployment: The AWS Bedrock User Guide states that when fine-tuning a model, you can encrypt training datasets with a customer-managed key. It also specifies that for provisioned throughput, the deployed model can be encrypted with a customer-managed key.

Source: AWS Bedrock User Guide, "Data protection in Amazon Bedrock," Section: "Encryption at rest."

2. CloudTrail for API Auditing: Official documentation confirms that Amazon Bedrock is integrated with AWS CloudTrail to record actions taken by a user, role, or AWS service, fulfilling the compliance and auditing requirement.

Source: AWS Bedrock User Guide, "Logging and monitoring in Amazon Bedrock," Section: "Logging Amazon Bedrock API calls with AWS CloudTrail."

3. CloudWatch for Performance Monitoring: Amazon Bedrock integrates with Amazon CloudWatch to collect and track metrics. This allows for monitoring of operational health, including key performance indicators like InvocationLatency and Invocations (throughput).

Source: AWS Bedrock User Guide, "Monitoring Amazon Bedrock," Section: "Monitoring Amazon Bedrock with Amazon CloudWatch."

4. Custom Models Workflow: The process of fine-tuning a model (e.g., in SageMaker) and importing it into Bedrock is a standard pattern. The model artifacts are stored in Amazon S3, and encryption is a key security consideration.

Source: AWS Bedrock User Guide, "Custom models," Section: "Import a model."

For SageMaker's built-in classification algorithms like XGBoost, the input data must be numeric. The client identifier is a high-cardinality, non-informative feature that should be excluded to prevent overfitting and improve model performance. The target variable, operation status, must be converted from its original format (likely a string) into numeric labels (e.g., 0, 1) for the algorithm to process it as a classification target. This preprocessing ensures the data is in the correct format and structure for effective model training in SageMaker.

A. Using Amazon Comprehend is inappropriate for simple label encoding, and retaining the unique client identifier is a poor modeling practice.

B. Excluding the operation status field removes the target variable, which is essential for training a supervised learning model.

C. SageMaker's built-in algorithms like XGBoost require numeric data, not strings, for both features and the target label.

1. Amazon SageMaker Developer Guide: In the section "Input/Output Interface for the XGBoost Algorithm," the documentation specifies the data format requirements. For CSV format, it states, "The first column must contain the target variable... The data in the target column must be numeric." This supports encoding the operation status and implies that non-numeric, non-feature columns like identifiers must be handled.

Source: Amazon SageMaker Developer Guide, "XGBoost Algorithm," section "Input/Output Interface for the XGBoost Algorithm."

2. AWS Documentation - "Preparing Data for Amazon SageMaker": General machine learning best practices outlined by AWS emphasize the importance of feature engineering, which includes removing irrelevant or non-predictive columns like unique identifiers. These identifiers do not generalize and can negatively impact model training.

Source: Amazon SageMaker Developer Guide, "Prepare Data," subsection on "Feature Engineering."

3. Ng, A. (2023). CS229 Machine Learning Course Notes. Stanford University. In lecture notes covering supervised learning and practical advice for applying learning algorithms, it is a standard preprocessing step to remove features with no predictive value, such as unique ID numbers. It also details the necessity of converting categorical labels into a numerical representation for classification algorithms.

Source: Stanford University, CS229: Machine Learning, "Advice for applying machine learning" and "Classification" sections.

The core requirement is to monitor for feature attribution drift in a production environment. The Amazon SageMaker ModelExplainabilityMonitor is the specific tool designed for this purpose. It integrates with SageMaker Clarify and uses explainability algorithms like SHAP (SHapley Additive exPlanations) to calculate feature importance scores for live inference data. By comparing these scores against a pre-computed baseline, it can detect if the model's reliance on certain features has shifted over time, directly addressing the scenario's need to monitor changes in feature prioritization.

A. Analyzing the training dataset with SageMaker Clarify is a pre-deployment step for bias and explainability, not for continuous production monitoring of drift.

B. The ModelQualityMonitor class tracks predictive performance metrics (e.g., accuracy, recall) against ground truth, not the underlying feature importance or attribution.

C. While technically possible, building a custom pipeline is more complex and less efficient than using the purpose-built ModelExplainabilityMonitor provided by SageMaker.

1. Amazon SageMaker Developer Guide: Under the section "Monitor model explainability," it states, "Amazon SageMaker Model Monitor provides the capability to monitor models in production for drift in the attribution of features... The model explainability monitor periodically generates a report that provides insights into which features are most important to your model's predictions."

Source: AWS Documentation, Amazon SageMaker Developer Guide, "Monitor models for data and model quality, bias, and explainability" -> "Monitor model explainability".

2. AWS Machine Learning Blog: In the post "Monitor in-production model explainability with Amazon SageMaker Clarify and Model Monitor," it is detailed that, "The model explainability monitor helps you understand how your model makes predictions in production. It detects feature attribution drift, which is when the relative importance of the features for a model's predictions changes over time."

Source: AWS Machine Learning Blog, "Monitor in-production model explainability with Amazon SageMaker Clarify and Model Monitor," October 20, 2021.

3. Amazon SageMaker Python SDK Documentation: The documentation for the sagemaker.modelmonitor.ModelExplainabilityMonitor class explicitly describes its function as handling "Amazon SageMaker Model Monitor explainability monitoring jobs," confirming it as the correct tool for this task.

Source: AWS Documentation, Amazon SageMaker Python SDK, sagemaker.modelmonitor module, ModelExplainabilityMonitor class definition.

AWS Glue is a serverless ETL (extract, transform, and load) service designed for data preparation for analytics and machine learning. An AWS Glue job can connect to an on-premises Microsoft SQL Server database using a JDBC connection. To meet the security requirement, this connection can be routed through an AWS Site-to-Site VPN, which establishes a secure IPsec tunnel between the on-premises data center and the AWS VPC where the Glue job runs. The Glue job's script can be written to execute a SQL query that selects only the specific non-sensitive columns and rows. This ensures filtering occurs at the source, and sensitive data never traverses the network or leaves the data center, satisfying all stated requirements.

A. While AWS DMS can filter data, AWS Glue is the more appropriate, purpose-built ETL service for preparing data for analytics and machine learning, offering greater flexibility.

B. Using Amazon Data Firehose with AWS Lambda for filtering would mean sensitive data leaves the on-premises data center before being filtered, violating the core security requirement.

D. Transferring an entire database backup file to S3 would move all data, including sensitive records, into the AWS Cloud before any filtering could occur, which is explicitly forbidden.

1. AWS Glue Developer Guide, "Adding a connection to your data store": This guide explains that for AWS Glue to access on-premises JDBC data stores, a network path must be established. It states, "If your data store is on-premises, you must provide a network route between AWS Glue and your data store, for example, using AWS Direct Connect or a virtual private network (VPN)." This supports using a VPN for connectivity.

2. AWS Site-to-Site VPN User Guide, "What is AWS Site-to-Site VPN?": This document confirms that AWS Site-to-Site VPN provides a secure connection using IPsec. "A Site-to-Site VPN connection creates a secure connection between your data center or branch office and your AWS cloud resources... We use IPsec to secure traffic through the tunnels." This directly addresses the IPsec requirement.

3. AWS Glue Developer Guide, "Authoring jobs in AWS Glue": The documentation on creating Glue jobs details how custom scripts (PySpark or Scala) can be used to define precise data extraction logic. This includes specifying SQL queries with WHERE clauses and column selections (SELECT col1, col2...) to ensure only non-sensitive data is pulled from the source database.

4. AWS Whitepaper, "AWS Database Migration Service Best Practices" (Page 10): This whitepaper discusses security for DMS, mentioning, "For on-premises to AWS migrations, you can use AWS Direct Connect or a VPN connection to establish private connectivity." While this shows DMS can use a VPN, the whitepaper also details DMS's primary purpose as migration and replication, making Glue a better fit for a recurring ETL task for machine learning.