Pass the Amazon Web Services AWS Certified Associate MLA-C01 Questions and answers with CertsForce

Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size. AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low-latency ingestion.

By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.

DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.

AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.

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

AWS documentation recommends using RecordIO format with lazy loading to optimize data input pipelines for image-based training workloads. RecordIO is a binary data format that enables sequential reads, reducing I/O overhead and improving throughput during training.

By converting JPEG images into RecordIO format, the training job can read data more efficiently from Amazon S3. Lazy loading ensures that only the required data is loaded into memory when needed, which optimizes CPU utilization during computationally intensive preprocessing steps.

Option A (file mode) results in many small S3 GET requests, which can become a bottleneck for large image datasets. Option B changes training behavior and can negatively affect convergence and performance. Option C reduces image quality, which directly impacts model accuracy and violates the requirement.

AWS SageMaker documentation explicitly highlights RecordIO and lazy loading as best practices for high-performance image training pipelines, especially when preprocessing is CPU-intensive.

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

Class imbalance occurs when one class significantly outnumbers another, causing models to bias predictions toward the majority class. In this case, images without defects (1,800) vastly outnumber images with defects (200). AWS ML best practices recommend oversampling the minority class to improve class representation without discarding valuable data.

Oversampling techniques—such as duplicating minority samples or applying data augmentation—help the model better learn defect-related features. This approach preserves all available data and improves recall and precision for underrepresented defect classes.

Option B is incorrect because undersampling the minority class would further worsen imbalance. Option A unnecessarily reduces dataset size. Option D does not address the imbalance problem.

Thus, oversampling defect images is the correct solution.

AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.

For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.

AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.

AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.

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

The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.

For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.

This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.

Therefore, using Kinesis Data Streams with Flink’s built-in Random Cut Forest is the most scalable and low-overhead solution.

Amazon SageMaker supports direct file system access for training jobs through Amazon FSx. AWS documentation states that FSx for NetApp ONTAP file systems can be mounted directly to SageMaker training instances when they are located in the same VPC.

Mounting the FSx file system allows SageMaker to stream large datasets efficiently without copying data into Amazon S3. This is ideal for very large datasets such as 6 TB of image data and avoids unnecessary storage duplication.

Options involving SageMaker Data Wrangler are intended for data preparation and exploration, not large-scale training data access. Mountpoint for Amazon S3 is not required and introduces additional complexity.

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

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.

In distributed training and processing jobs, multiple instances and containers communicate with each other over the network to exchange gradients, parameters, and intermediate results. Even when jobs run inside a private VPC, network traffic between instances is not automatically encrypted at the application layer.

AWS documentation for Amazon SageMaker specifies that inter-container traffic encryption is the supported mechanism for encrypting data in transit between containers that participate in distributed training or processing jobs. When enabled, SageMaker uses TLS to encrypt all communication between containers across instances, ensuring confidentiality and compliance with security requirements.

Option A (network isolation) prevents containers from making outbound network calls but does not encrypt traffic between distributed instances. Option B is incorrect because security groups control traffic access, not encryption. Option D (VPC flow logs) is a monitoring feature and does not provide encryption.

Therefore, enabling inter-container traffic encryption is the correct and AWS-recommended solution.

Step 1

Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.

This role must include:

Permissions for AWS Glue Data Catalog (e.g., glue:GetDatabase, glue:GetTables)

Permissions for the Amazon S3 bucket that stores the underlying data

Step 2

Attach the IAM role to the Redshift namespace.

Redshift Serverless uses a namespace, not a cluster, so the role must be associated with the namespace to allow Redshift to assume it when querying external data.

Step 3

Create an external schema in Amazon Redshift to point to the Data Catalog database.

The external schema maps Redshift to the Glue Data Catalog database so Redshift can query the tables stored in S3.

Amazon SageMaker Pipelines is purpose-built for orchestrating end-to-end ML workflows, including data ingestion, training, evaluation, and deployment. It supports automation, versioning, and deployment of the latest model versions.

MWAA orchestrates general workflows but lacks ML-native features. Lambda cannot handle long-running ML training. Spark processes data but does not manage ML lifecycle.

Therefore, Option A is the correct AWS-native solution.