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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 10 )

Updated On: 9-Feb-2026
Page 9 of 47
PDF with 230 Questions

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.

The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.

Which change to the architecture will meet these requirements?

  1. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
  2. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
  3. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
  4. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.

Answer(s): A

Explanation:

Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting the buffering hints to zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning the batch size in the PutRecordBatch operation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.



A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.

The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.

How should the company deploy the model into production to meet these requirements?

  1. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
  2. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
  3. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
  4. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.

Answer(s): A

Explanation:

Amazon SageMaker real-time inference endpoints are designed to provide low-latency predictions in production environments. They offer built-in auto scaling to handle unpredictable bursts of requests, ensuring high availability and responsiveness. This approach is fully managed, reduces operational complexity, and is optimized for the range of request sizes (1 KB to 3 MB) specified in the requirements.



An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.

Which instance purchasing option will meet these requirements MOST cost-effectively?

  1. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  2. Run the primary node, core nodes, and task nodes on Spot Instances.
  3. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  4. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

Answer(s): D

Explanation:

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.



A company wants to improve the sustainability of its ML operations.

Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

  1. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
  2. Use Amazon SageMaker Ground Truth for data labeling.
  3. Deploy models by using AWS Lambda functions.
  4. Use AWS Trainium instances for training.
  5. Use PyTorch or TensorFlow with the distributed training option.

Answer(s): A,D

Explanation:

SageMaker Debugger can identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainium instances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.



A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.

The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.

Which solution will meet these requirements?

  1. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  2. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  3. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  4. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.

Answer(s): D

Explanation:

Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.






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