This requirement combines asynchronous inference on large datasets with automated data quality monitoring and alerting. AWS documentation explicitly recommends Amazon SageMaker batch transform for large- scale, asynchronous inference workloads. Batch transform jobs process large datasets stored in Amazon S3 without requiring a persistent endpoint, making them cost-effective and scalable.
For data quality monitoring, Amazon SageMaker Model Monitor is the AWS-native solution. Model Monitor can be scheduled to analyze inference data, compare it against a baseline, and detect data quality issues such as missing values, schema changes, or statistical drift. When violations occur, Model Monitor emits metrics to Amazon CloudWatch, where alarms can trigger alerts.
Options A, B, and C lack ML-aware data quality monitoring capabilities. AWS Glue and Batch are not designed for model data quality analysis, and CloudTrail tracks API activity-not data quality.
AWS best practices clearly position Batch Transform + Model Monitor as the correct architecture for asynchronous inference with automated monitoring and alerting.
Therefore, Option D is the correct and AWS-verified solution.

AWS Key Management Service (AWS KMS) is the recommended service for encryption and key access control. By creating a customer-managed KMS key, the company can define granular IAM policies that control which applications and roles can use the key.
The AWS Encryption CLI integrates directly with KMS and enables client-side encryption of files before storing them in Amazon S3. This approach ensures data is encrypted at rest and that only authorized principals can decrypt it.
SSH keys and API keys are not designed for data encryption. IAM roles alone do not create or manage encryption keys-they only grant permissions.
AWS documentation explicitly states that KMS customer-managed keys provide centralized key management, auditing, and access control.
Therefore, Option D is the correct and AWS-aligned solution.

Option C is correct because the company needs Kubernetes hosting , does not want to manage the control plane , wants repeatable infrastructure provisioning , and requires that provisioning be done by using Python . Amazon EKS is AWS's managed Kubernetes service, so it satisfies the requirement to avoid managing the Kubernetes control plane directly. The AWS CDK documentation also confirms that Python is a fully supported client language for defining infrastructure as code.
AWS CDK is the best fit because it lets engineers define cloud infrastructure programmatically in Python and deploy it in a repeatable way. The AWS CDK EKS construct library specifically supports defining Amazon EKS clusters and related Kubernetes resources. This makes it a strong match for infrastructure that must be reproducible and expressed in code rather than provisioned manually. Since the model is already packaged in a container, storing the image in Amazon ECR and then deploying it to Amazon EKS follows the normal AWS container workflow.
The other options are less suitable. Option A requires setting up and managing a Kubernetes cluster on EC2, which violates the requirement to avoid control-plane management. Option B uses the AWS CLI, but the question specifically requires infrastructure provisioning by using Python , not command-line provisioning.
Option D uses CloudFormation, which is repeatable infrastructure as code, but the question explicitly says the infrastructure must be provisioned by using Python . AWS CDK uniquely satisfies both the IaC and Python requirements while using managed Kubernetes with EKS.
Therefore, the best verified AWS-docs answer is C .

This scenario indicates overfitting. AWS documentation for XGBoost recommends increasing the L2 regularization parameter (lambda) to reduce overfitting and improve generalization.
Increasing num_round worsens overfitting. Changing evaluation metrics does not change model behavior.
Collecting more data is effective but requires significant effort.
Regularization is a low-effort, high-impact fix.
Therefore, Option D is correct.

The text feature contains high-cardinality categorical values with minor spelling variations, which is a common real-world data quality issue. Traditional encoding techniques such as ordinal encoding (Option A) or one-hot encoding (Option C) would treat each misspelled value as a completely separate category, resulting in poor generalization and very high dimensionality.
Similarity encoding addresses this problem by grouping or encoding categories based on string similarity.
Categories that differ only slightly-such as spelling errors-are encoded with similar numerical representations. Amazon SageMaker Data Wrangler supports similarity encoding specifically for such noisy categorical features.
Target encoding (Option D) depends on the target label distribution and risks target leakage if not carefully applied.
Therefore, similarity encoding is the most appropriate and AWS-recommended solution.