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MLA-C01 Dumps With Exact Questions and Answers

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MLA-C01 Questions and Answers

Question # 1

An ML engineer is setting up a continuous integration and continuous delivery (CI/CD) pipeline for an ML workflow in Amazon SageMaker AI. The pipeline needs to automate model re-training, testing, and deployment whenever new data is uploaded to an Amazon S3 bucket. New data files are approximately 10 GB in size. The ML engineer wants to track model versions for auditing.

Which solution will meet these requirements?

A.

Use AWS CodePipeline, Amazon S3, and AWS CodeBuild to retrain and deploy the model automatically and to track model versions.

B.

Use SageMaker Pipelines with the SageMaker Model Registry to orchestrate model training and version tracking.

C.

Create an AWS Lambda function to re-train and deploy the model. Use Amazon EventBridge to invoke the Lambda function. Reference the Lambda logs to track model versions.

D.

Use SageMaker AI notebook instances to manually re-train and deploy the model when needed. Reference AWS CloudTrail logs to track model versions.

Question # 2

An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.

The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.

What should the ML engineer do to transform the data and make the data suitable for training?

A.

Apply principal component analysis (PCA) to oversample the minority class in the training dataset.

B.

Apply Synthetic Minority Oversampling Technique (SMOTE) to generate new synthetic samples of the minority class in the training dataset.

C.

Randomly oversample the majority class in the validation dataset.

D.

Apply k-means clustering to undersample the minority class in the test dataset.

Question # 3

A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.

Which solution will optimize the scaling process without affecting response times?

A.

Change to a multi-model endpoint configuration.

B.

Integrate Amazon API Gateway and AWS Lambda to manage invocations.

C.

Decrease the scale-in cooldown period and increase the maximum instance count.

D.

Increase the cooldown period after scale-out activities.

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