Amazon Web Services Updated MLA-C01 Exam Questions and Answers by baani

By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.

Amazon SageMaker Asynchronous Inference is specifically designed for long-running inference requests and large payloads. It supports payload sizes up to 1 GB and processing times of up to 1 hour, while automatically queuing requests.

Asynchronous inference stores results in Amazon S3 and allows clients to retrieve inference outputs after processing completes. It also supports auto scaling down to zero when there are no incoming requests, reducing cost.

Batch transform is intended for offline, bulk inference and does not return per-request results in an asynchronous request–response pattern. Serverless and real-time inference have strict payload size and timeout limits that do not support 1-hour processing.

Therefore, asynchronous inference is the only SageMaker inference option that meets all stated requirements.

For near real-time inference with low latency and variable traffic, AWS recommends deploying models to managed SageMaker endpoints. By enabling auto scaling, the endpoint automatically adjusts the number of instances based on request volume, ensuring consistent performance while optimizing cost.

Amazon SageMaker Endpoints abstracts infrastructure management, health checks, scaling, and model deployment. This provides lower operational overhead than managing EC2 instances manually.

Batch transform is for offline inference. API Gateway with S3 cannot serve ML models. EC2-based deployments require manual scaling and monitoring.

Therefore, a SageMaker endpoint with auto scaling is the correct solution.

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.