SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.

AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.
AWS documentation clearly specifies the required order:
* Create a baseline using training data statistics
* Create a monitoring schedule to compare incoming data against the baseline Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.
Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.
Therefore, Option B is the correct and AWS-verified answer.

Thetemperatureparameter controls the randomness in the model's responses. Lowering the temperature makes the model produce more deterministic and consistent answers.
Thetop_kparameter limits the number of tokens considered for generating the next word. Reducing top_k further constrains the model's options, ensuring more predictable responses.
By decreasing both parameters, the responses become more focused and consistent, reducing variability in similar queries.

SageMaker Debugger provides built-in rules to automatically detect issues like vanishing gradients, underutilized GPU, and overfitting during training jobs. It generates real-time metrics and allows users to define predefined actions that are triggered when specific issues occur. This solution minimizes operational overhead by leveraging the managed monitoring capabilities of SageMaker Debugger without requiring custom setups or extensive manual intervention.

Amazon Kendra is an AI-powered search service designed for semantic search use cases. It allows ingestion of documents from an Amazon S3 bucket using the Amazon Kendra S3 connector. Once the documents are ingested, Kendra enables semantic searches with its built-in capabilities, removing the need to manually generate embeddings or manage a vector database. This approach is efficient, requires minimal operational effort, and meets the requirements for a Retrieval Augmented Generation (RAG) application.