Amazon SageMaker Overview and Model Building

Which of the following is a key characteristic of asynchronous inference in SageMaker?

What is a primary advantage of using batch transform in SageMaker for inference?

A data scientist needs to perform inference on a large dataset containing millions of records. The processing time for each record is not critical, but the entire dataset must be processed within a few hours. Which SageMaker inference option is most suitable?

A company is developing a machine learning application that requires end-to-end machine learning development, team collaboration, model tuning and debugging, and automated workflows. Which SageMaker service would provide these capabilities in a single interface?

Which of the following is a true statement regarding the maximum processing time for Batch Transform?

What is the primary benefit of using Amazon SageMaker for machine learning tasks?

Why is it typically challenging to handle all machine learning processes in one place without a service like SageMaker?

In the example provided, what is the role of historical data in building a model to predict exam scores using SageMaker?

What steps are involved in the end-to-end machine learning process using SageMaker, according to the content?

What does SageMaker do beyond deploying machine learning models?

The content mentions built-in algorithms in SageMaker, including 'KNN algorithms'. What type of machine learning task are KNN algorithms typically used for?

In the context of SageMaker, which of the following best describes the purpose of 'tuning' a machine learning model?

How might improved data collection, guided by monitoring model performance in SageMaker, lead to better exam score predictions?

Which of the following unsupervised learning algorithms is used to reduce the number of features in a dataset?

Which of the following machine learning tasks involves analyzing and understanding text data?

What is the primary goal of automatic model tuning (AMT) in SageMaker?

What does AMT automatically choose to optimize model performance?

Which of the following is a key benefit of using SageMaker for model deployment compared to a self-hosted solution?

Which SageMaker deployment option is best suited for applications requiring immediate responses with minimal configuration, but may experience a 'cold start'?

An application needs to process very large payloads (up to 1 GB) and can tolerate near real-time latency. Which SageMaker deployment option is most suitable?

When should you use the Batch Transform deployment option in SageMaker?

Which SageMaker inference type is characterized by low latency and small payload sizes (up to 6 MB), making it suitable for real-time predictions?

From an exam perspective, what is the main differentiator between Real-Time Inference and Serverless Inference?

An organization needs to detect fraudulent transactions within a large dataset. Which unsupervised learning algorithm would be most appropriate for this task?

A company wants to automatically adjust the hyperparameters of its machine learning model to achieve the best possible performance. Which SageMaker feature should they use?

What is a potential drawback of using serverless inference in SageMaker?

Which of the following SageMaker deployment options is suitable for processing input payloads up to 1 GB in size?

A data scientist needs to perform inference on a large dataset stored in an Amazon S3 bucket. Which SageMaker deployment option should they use?