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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps

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



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



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



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 ТВ 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






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