Free Amazon Web Services MLA-C01 Practice Exam with Questions & Answers | Set: 5

Large Parquet files can cause out-of-memory (OOM) issues during training if individual files exceed the memory capacity of the training instances. AWS documentation recommends repartitioning large datasets into smaller files to enable efficient streaming and parallel loading.

By using Apache Spark on Amazon EMR to repartition the Parquet files, the ML engineer can split the dataset into multiple smaller files that can be read incrementally during training. This approach avoids loading a single large file into memory and improves data parallelism.

Attaching larger EBS volumes increases storage capacity but does not solve memory constraints. Switching to memory-optimized instances increases cost and is not necessary when the dataset can be restructured. SageMaker distributed data parallelism focuses on model parameter synchronization across GPUs, not dataset file size.

AWS best practices explicitly recommend partitioning large Parquet datasets to improve memory efficiency during training.

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

 Why Linear Learner?

SageMaker ' s Linear Learner algorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options for weight balancing across classes.

Linear Learner can capture patterns in the data while being computationally efficient.

 Key Features of Linear Learner:

Automatically weights minority and majority classes.

Supports both classification and regression tasks.

Handles interdependencies among features effectively through gradient optimization.

 Steps to Implement:

Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.

Configure the hyperparameters to enable balanced class weights.

Train the model with the balanced dataset created using SageMaker Data Wrangler.

The task described is unsupervised topic modeling, where topics are unknown in advance. Latent Dirichlet Allocation (LDA) is a probabilistic generative model specifically designed to discover latent topics from a corpus of documents without labeled data. AWS provides LDA as a built-in algorithm in Amazon SageMaker, making it well suited for this requirement.

LDA models documents as mixtures of topics and topics as mixtures of words, enabling interpretable topic discovery at scale. This aligns precisely with the need to organize documents into topics when the topics are not predefined.

BlazingText is optimized for word embeddings and supervised text classification, not topic modeling. Sequence-to-sequence models are used for translation or summarization. Object2Vec creates embeddings but does not itself perform topic discovery without additional clustering steps.

Therefore, LDA is the correct and purpose-built solution.

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.

SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.

SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.

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.

For asynchronous inference on large datasets, AWS recommends SageMaker batch transform, which is designed for offline and large-scale inference workloads. Batch transform jobs do not require real-time endpoints and can process large volumes of data efficiently.

For monitoring data quality, AWS provides Amazon SageMaker Model Monitor, which supports scheduled monitoring of data quality, schema drift, and feature distribution drift. Model Monitor can publish metrics to Amazon CloudWatch and trigger alerts when thresholds are breached.

The other options lack native ML-specific monitoring capabilities. CloudTrail audits API activity, not data quality. Glue, Batch, and ECS would require extensive custom monitoring logic.

Therefore, SageMaker batch transform combined with Model Monitor is the correct solution.

Amazon Kendra is an AI-powered search service designed for semantic search use cases. It allows ingestion of documents from an Amazon S3 bucket using the Amazon Kendra S3 connector. Once the documents are ingested, Kendra enables semantic searches with its built-in capabilities, removing the need to manually generate embeddings or manage a vector database. This approach is efficient, requires minimal operational effort, and meets the requirements for a Retrieval Augmented Generation (RAG) application.

The AWS::SageMaker::Model resource in AWS CloudFormation is used to create an ML model in Amazon SageMaker. This model can then be hosted on an endpoint by using the AWS::SageMaker::Endpoint resource. The model resource defines the container or algorithm to use for hosting and the S3 location of the model artifacts.

A lift-and-shift migration requires minimal architectural changes. The company already uses Kubernetes and standalone Docker images, which are fully compatible with Amazon Elastic Kubernetes Service.

The lowest-overhead approach is to push the existing Docker images to Amazon Elastic Container Registry and deploy them directly to the EKS cluster using standard Kubernetes manifests or Helm charts. This approach preserves the existing ML workflows, training logic, and inference services without refactoring.

Option A requires redesigning services with Kubeflow, significantly increasing complexity. Option C changes both data storage and training methodology. Option D requires retraining models and changing operational workflows.

Therefore, uploading Docker images to Amazon ECR and deploying them to EKS is the correct lift-and-shift solution.