Free AWS Certified Machine Learning Engineer - Associate Practice Questions

Step 1: Create a feature group.

Step 2: Ingest the records.

Step 3: Access the store to build datasets for training.

Step 1: Create a Feature Group

Why? A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.

How? Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.

Step 2: Ingest the Records

Why? After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.

How? Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.

Step 3: Access the Store to Build Datasets for Training

Why? Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.

How? Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.

Order Summary:

Create a feature group.

Ingest the records.

Access the store to build datasets for training.

This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.

Int8 quantization

Low-rank adaptation (LoRA)

Fully Sharded Data Parallel (FSDP)

These three mappings align with AWS documentation for large-model training and optimization in SageMaker AI.

For 1, the correct answer is Int8 quantization. AWS documentation explains that quantization reduces hardware and memory requirements by using a less precise data type for weights and activations, including INT8 formats. AWS Prescriptive Guidance also states that quantization reduces memory footprint by converting higher-precision weights to lower-precision formats such as FP16 to INT8. That matches the description of reducing memory usage by representing model weights with lower precision.

For 2, the correct answer is Low-rank adaptation (LoRA). In practice, LoRA is a standard parameter-efficient fine-tuning (PEFT) method that updates only a small subset of trainable parameters instead of full-model fine-tuning. In SageMaker JumpStart workflows for large language models, LoRA is used specifically to make fine-tuning large models more efficient by training adapter-style low-rank updates rather than all original model weights. This matches the description of a PEFT technique that trains only a subset of model parameters.

For 3, the correct answer is Fully Sharded Data Parallel (FSDP). AWS documentation states that SageMaker model parallelism integrates with PyTorch FSDP, which shards model states across GPUs and supports large-scale distributed training. That directly matches the description of a distributed training technique used to share or shard model parameters across GPUs.