MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.

Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.

Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.

Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.

Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.

Option D is correct because Amazon SageMaker Model Registry is the AWS feature specifically designed to catalog models, manage model versions, and organize them into model groups. AWS documentation states that you can create a Model Group and then register each model you train, and the Model Registry adds it to the Model Group as a new model version. That directly matches the requirement to automatically create versions of ML models as models are created.

The AWS docs also describe a standard workflow in which you first create a Model Group, then create an ML pipeline, and for each run of the ML pipeline, create a model version that is registered in that group. This means Model Registry supports exactly the kind of lifecycle management and version tracking needed in an MLOps workflow where new model artifacts are produced over time and need consistent governance.

The other options do not meet the requirement. Amazon ECR stores container images, not model version history. SageMaker Marketplace model packages are for discovering or subscribing to third-party or prebuilt solutions, not for automatically versioning your organization’s trained models. SageMaker ML Lineage Tracking helps trace artifacts, datasets, and processing steps for reproducibility, but AWS documentation distinguishes lineage from the actual model version management capability provided by Model Registry. Model Registry is the AWS-native feature for keeping versioned records of trained models and managing approval status, deployment readiness, and metadata in a centralized way.

Therefore, the fully verified AWS-docs answer is D, because SageMaker Model Registry is the service purpose-built to create and manage model versions as new models are produced.

City (name): One-hot encoding

Type_year (type of home and year the home was built): Feature splitting

Size of the building (square feet or square meters): Standardized distribution

City (name): One-hot encoding

Why? The " City " is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like " New York " or " Los Angeles " ), which the model can interpret.

Type_year (type of home and year the home was built): Feature splitting

Why? " Type_year " combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: " Type of home " and " Year built, " enabling the model to process each feature independently.

Size of the building (square feet or square meters): Standardized distribution

Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.

By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.