AWS Certified Machine Learning Engineer Associate Free Sample Questions

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 needs to use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?

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 is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?

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?

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 needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.Which action will meet this requirement?

A financial services company is building a real-time fraud detection system. The system ingests transaction data via Amazon Kinesis Data Streams. A Machine Learning Engineer needs to perform feature engineering on this streaming data (such as calculating rolling averages of transaction amounts over the last 10 minutes) before passing the features to a SageMaker endpoint for inference. Which approach offers the low-latency feature calculation required?

A Machine Learning Engineer is preparing a large dataset (50 TB) stored in Amazon S3 for training a computer vision model. The training job will run on a cluster of Amazon EC2 p4d.24xlarge instances using Amazon SageMaker. The dataset consists of millions of small image files. The engineer observes that the training job initialization is taking a long time due to S3 API latency when listing and downloading objects. Which storage configuration will MAXIMIZE data loading performance?

A retail company wants to train a customer churn prediction model. The dataset contains sensitive Personally Identifiable Information (PII) including customer names and email addresses. The company's security policy requires that all PII be redacted before the data is used for model training. Which solution provides the MOST automated and serverless way to identify and redact this PII in Amazon S3?

An ML Engineer is setting up a feature engineering pipeline. The requirement is to have a centralized repository where features can be stored, discovered, and shared across different teams. The solution must support both low-latency retrieval for real-time inference and high-throughput retrieval for batch training. Which AWS service component should be used?