SageMaker Feature Store is designed for scalable, reusable, and consistent feature engineering. It provides built-in transformations, manages offline (for training) and online (for inference) stores automatically, and ensures feature consistency. This is more scalable and integrated than custom Processing scripts (A) or external systems like Redshift (D). Option C is incorrect as XGBoost’s built-in handling is limited and doesn’t create a reusable pipeline for complex data types like text.

The core issue is insufficient instance capacity to handle concurrent requests. Increasing the instance count and configuring auto-scaling based on backlog (a direct measure of pending requests) is the standard, immediate solution for throughput bottlenecks. Switching to GPUs (B) might help latency but is not necessarily cost-effective and doesn’t directly address concurrency limits. Multi-model endpoints (C) are for hosting many models, not scaling a single one. API Gateway (D) manages client-side throttling but doesn’t solve the backend capacity issue and could worsen latency.

Batch Transform can split large payloads across instances. Setting `SplitType` to ‘Line’ ensures each record is processed independently, preventing a single large record from crashing an instance. Reducing `MaxPayloadInMB` provides finer control. This is a configuration fix that adds resilience at no extra cost. Simply using larger instances (A) increases cost and doesn’t prevent issues from malformed records. Code changes (C) are more invasive, and retrying from the beginning (D) is inefficient and doesn’t solve the root cause.

SageMaker Experiments is the native, fully managed service for experiment tracking within the SageMaker ecosystem. It integrates seamlessly with SageMaker training jobs and Studio, providing automatic capturing of job details and a built-in UI for comparison with minimal setup. While MLflow (B) is a popular tool, it requires managing the tracking server. CloudWatch (C) and a custom S3/QuickSight solution (D) are fragmented, require significant custom work, and lack the specialized experiment comparison features.

This configuration provides end-to-end private connectivity. The VPC configuration for the hosting instance places it in a private subnet. The SageMaker VPC interface endpoint (via AWS PrivateLink) allows the SageMaker API calls (e.g., from your application) to stay within the AWS network without internet exposure. Setting `NetworkIsolation=True` encrypts traffic between the ML instance and its container and prevents internet access from the container. IAM policies (A) don’t enforce network paths. An ALB (C) adds complexity and doesn’t address the SageMaker control plane traffic. Multi-model endpoints (D) do not inherently provide network isolation.

SageMaker Pipelines is the native, fully managed CI/CD service for ML workflows. It is specifically designed for this purpose: it creates a DAG of steps (like Processing, Training, Condition), supports step caching to avoid re-running successful steps, can be triggered on a schedule, and provides visual lineage and execution history within SageMaker Studio. While Step Functions (A) can orchestrate jobs, it requires more manual setup and lacks the ML-specific caching and native Studio integration. A Lambda-based approach (C) is more complex to manage and monitor. A monolithic job (D) is not modular, hard to debug, and lacks the required scheduling and visualization.

A high false positive rate shift in production, while original test set performance holds, is a classic sign of training-serving skew. This occurs when the data the model sees in production differs from the data it was trained on (e.g., due to changes in data sources, preprocessing code, or user behavior). The first investigative step is to validate this hypothesis by comparing feature distributions (mean, variance, categories) between training and current production data. Concept drift (D) is also possible, but skew is more common initially. Overfitting (A) would have been caught earlier. Data leakage (C) would likely have caused high performance from the start, not degradation later.

For a very large model requiring tensor parallelism (model partitioning across devices/instances), SageMaker’s model parallelism library integrated with hosting is the designed solution. It allows you to split a single model across multiple instances (e.g., multiple GPUs on multiple hosts) to overcome single-instance memory limits. It can be combined with auto-scaling policies. A single large instance (A) has a physical limit and may not suffice. Asynchronous inference (B) is for long-running, non-real-time jobs, not chat. Serverless inference (C) has strict payload and memory limits unsuitable for large LLMs and does not support model partitioning.

Ground Truth supports this exact scenario through its audit workflow. You can specify a primary workforce (e.g., public) to perform the initial labeling and a secondary, more trusted workforce (e.g., a private team of experts) in ‘audit’ mode. The audit workforce reviews a configurable percentage of the primary workforce’s labels, and Ground Truth uses this to calculate confidence scores and adjust the consolidated label. This balances cost, speed, and quality. Option A is expensive and slow. Option B uses automation, not a second human workforce. Option C requires building a complex custom orchestrator, which is unnecessary as the functionality is built-in.

SageMaker Feature Store is designed for scalable, reusable, and consistent feature engineering. It provides built-in transformations, manages offline (for training) and online (for inference) stores automatically, and ensures feature consistency. This is more scalable and integrated than custom Processing scripts (A) or external systems like Redshift (D). Option C is incorrect as XGBoost’s built-in handling is limited and doesn’t create a reusable pipeline for complex data types like text.0