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Amazon SageMaker AI shadow testing enables ML engineers to evaluate new model versions by sending a copy of live production traffic to a shadow variant without affecting production inference responses. AWS documentation specifies that shadow variants are configured separately from production variants and can use different instance types, including higher-performance GPU instances.

The correct approach is to create a new endpoint configuration using the CreateEndpointConfig API. This configuration includes the existing production variant and a separate ShadowProductionVariants list. The shadow variant can be assigned a larger instance type to meet GPU performance requirements while leaving the production variant unchanged. After creating the configuration, the engineer deploys it using the CreateEndpoint action.

Option A is incorrect because production variant configurations cannot be directly modified to include shadow variants. Option B is incorrect because shadow variants are not defined as standard production variants; defining two production variants would route traffic differently and could affect production behavior. Option C introduces unnecessary complexity and deviates from SageMaker’s built-in shadow testing functionality.

AWS explicitly documents that shadow variants are designed to isolate testing resources, support different instance types, and ensure zero impact on production inference. Therefore, Option D is the correct and AWS-recommended solution.

Provide flight departure, arrival, and delay information, and provide updates for low-latency workloads→ Real-time inference

Advertise holiday travel promotional deals to millions of users in multiple markets before holiday seasons for spiky workloads→ Serverless inference

Generate quarterly and annual flight reports and insights for trend analysis of large datasets→ Batch inference

Generate online image and audio stories for passengers to watch or listen to while waiting at an airport→ Asynchronous inference

The correct mapping depends on latency requirement, traffic pattern, payload size, processing duration, and whether the workload needs a persistent endpoint.

Real-time inference is the right choice for flight departure, arrival, and delay updates because this is an online user-facing workload that requires low latency. AWS states that SageMaker real-time inference is ideal for online inference workloads with low-latency or high-throughput requirements and uses a persistent fully managed endpoint. That fits flight status information because passengers and airline systems expect immediate responses.

Serverless inference is the best choice for holiday promotional deals because this traffic is spiky, seasonal, and unpredictable. AWS describes SageMaker Serverless Inference as suitable for intermittent or unpredictable traffic patterns. It is cost-effective because SageMaker manages the infrastructure and scales down when there are no requests, so the company does not pay for idle endpoint capacity.

Batch inference is correct for quarterly and annual flight reports because this workload analyzes large datasets offline and does not need an always-running endpoint. AWS says SageMaker batch transform is used to get inferences from large datasets and when a persistent endpoint is not required. Reports and trend analysis are scheduled, non-real-time analytics workloads, so batch inference is the most cost-effective option.

Asynchronous inference is the right choice for generating online image and audio stories. These requests can have larger payloads and longer processing times than normal low-latency API calls. AWS states that SageMaker Asynchronous Inference queues incoming requests and is ideal for large payloads, long processing times, and near-real-time latency requirements. Image and audio generation can take seconds or minutes, so asynchronous inference is more appropriate than real-time inference.

The day_of_week feature is a categorical variable with a small, fixed number of unique values and no inherent ordinal relationship. AWS machine learning best practices strongly recommend one-hot encoding for this type of categorical data when preparing features for classification models.

One-hot encoding converts each unique category into a separate binary feature (0 or 1). For example, “Monday” becomes a column where Monday = 1 and all other days = 0. This ensures that the ML model does not incorrectly assume a numeric or ordered relationship between categories.

Option B (label encoding) assigns integer values to categories (e.g., Monday = 1, Tuesday = 2). AWS documentation cautions against this approach for nominal data because models may incorrectly infer ordinal meaning, leading to biased or inaccurate predictions.

Option A (binary encoding) is typically used for high-cardinality categorical features to reduce dimensionality. With only seven categories, AWS recommends one-hot encoding for clarity and interpretability.

Option D (tokenization) is used for text processing, such as NLP tasks, and is not appropriate for structured categorical features.

AWS SageMaker feature engineering guidelines emphasize that one-hot encoding is the preferred method for low-cardinality categorical variables in classification models, especially when using algorithms such as logistic regression, neural networks, and tree-based models.

Therefore, Option C is the correct and AWS-aligned choice.