MLA-C01 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

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.

SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.

With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.

Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.

Therefore, Option D is the correct and AWS-recommended choice.

Option C is correct because the requirement has two separate parts: first, the company must track model versions; second, it needs recommendations for which instance types to use for hosting the model in SageMaker AI. AWS documentation identifies the SageMaker Model Registry as the SageMaker feature used to register and manage model packages and versions as part of the ML lifecycle. For hosting recommendations, AWS documentation says Amazon SageMaker Inference Recommender helps select the best instance type and configuration for ML models and workloads by automating benchmarking and load testing across SageMaker AI instances.

The AWS Inference Recommender documentation is especially important here because it explicitly says you can use it after you register a model to the SageMaker Model Registry with model artifacts. It also states that Inference Recommender helps choose the best endpoint type and configuration and returns recommendations that include the instance type in the resulting endpoint configuration. That is an exact match to the question’s requirement to recommend hosting instance types for a model that has not yet been hosted on SageMaker AI.

The other options do not match AWS service purposes. Amazon ECR stores container images, not model versions. AWS Compute Optimizer is not the SageMaker-native service documented for inference benchmarking of ML models. SageMaker Autopilot is for automated model building, not inference instance recommendations. SageMaker Experiments tracks experiment metadata, trials, and runs, but it does not recommend deployment instance types. Therefore, the fully verified AWS-docs answer is C.

For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additional processing but do not store data, can use Spot Instances to reduce costs without compromising the cluster ' s resilience or data integrity. This configuration balances cost-effectiveness and reliability.

Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model ' s performance.

The problem described is a patient segmentation use case, which is a classic example of unsupervised learning. The objective is to group patients with similar characteristics without predefined labels. AWS documentation clearly states that Amazon SageMaker k-means is designed specifically for clustering and segmentation tasks.

The SageMaker k-means algorithm groups data points into clusters based on feature similarity and requires the user to define the number of clusters using the k hyperparameter. This directly satisfies the requirement to “determine the number of groups by using hyperparameters.” AWS recommends k-means for applications such as customer segmentation, risk grouping, and pattern discovery in healthcare data.

Option A (XGBoost) is a supervised learning algorithm used for classification and regression. The max_depth hyperparameter controls tree complexity, not the number of groups, making it unsuitable for this task.

Option C (DeepAR) is a time-series forecasting algorithm optimized for predicting future values, not clustering patients.

Option D (Random Cut Forest) is an anomaly detection algorithm. While useful for identifying outliers or unusual patient behavior, it does not perform clustering or group segmentation.

AWS SageMaker documentation explicitly identifies k-means as the correct choice when the goal is to partition data into a predefined number of clusters using a tunable hyperparameter.

Therefore, Option B is the correct and AWS-verified answer.

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.

Option A is correct because AWS Glue provides the lowest-overhead managed pattern for this scenario: use a crawler to keep the AWS Glue Data Catalog updated for the S3 dataset, then use AWS Glue Data Quality to evaluate rules against that cataloged data. AWS documentation states that crawlers can automatically discover and catalog new or updated S3 data sources, reducing manual metadata management overhead. AWS Glue Data Quality is also documented as a managed, serverless capability for measuring and monitoring data quality.

This option is especially strong because the dataset is already in Amazon S3 and partitioned by date, and the requirement is simply to automatically check quality before the data is sent to QuickSight. AWS Glue Data Quality supports the Data Catalog as an entry point, and AWS documentation notes that this approach is intended for datasets that are already cataloged and for ongoing governance-style quality evaluation. The docs also show that scheduling is supported for Data Catalog-based quality checks. That means the company can run the crawler monthly, then evaluate a ruleset against the cataloged table without needing custom ETL code.

The other options add more operational work. Option B requires a custom PySpark job and custom functions, which is more maintenance-heavy than using catalog-based quality rules directly. Option C depends on bespoke Lambda scripts. Option D does not use the right service for data quality validation. Because the question asks for the least operational overhead, the most AWS-aligned answer is A: monthly Glue crawler plus Glue Data Quality rules.

AWS Glue Data Quality provides managed, declarative data quality checks with minimal configuration. Combined with Glue crawlers, it enables automatic schema discovery and quality validation without custom code.

Option A uses native AWS services designed for this exact purpose, minimizing operational overhead. Options B and C require custom code and maintenance. Option D is not designed for data validation.

AWS documentation explicitly recommends Glue Data Quality rules for scalable, automated data quality checks in analytics pipelines.

Therefore, Option A is the correct and AWS-aligned solution.

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.