Free Amazon MLA-C01 Exam Dumps

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Question No: 1

MultipleChoice

Case Study

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a

central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

Options

AUse SageMaker Experiments to facilitate the approval process during model registration.

BUse SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

CUse SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

DUse SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to 'Approved.'

Question No: 2

MultipleChoice

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

Options

AUse Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

BUse Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

CUse AWS Glue DataBrew built-in features to oversample the minority class.

DUse the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

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