AWS Docs References Flashcards

1
Q

Types of Cloud Computing

A

https://aws.amazon.com/types-of-cloud-computing/

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2
Q

Shared Responsibility Model

A

https://aws.amazon.com/compliance/shared-responsibility-model/

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3
Q

Amazon Bedrock

A

https://aws.amazon.com/bedrock/

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4
Q

Amazon Bedrock - What is it?

A

https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html

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5
Q

Amazon Bedrock User Guide PDF (1655 pages)

A

https://docs.aws.amazon.com/pdfs/bedrock/latest/userguide/bedrock-ug.pdf

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6
Q

Amazon Bedrock FAQs

A

https://aws.amazon.com/bedrock/faqs/

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7
Q

Amazon Bedrock Knowledge Bases

A

https://aws.amazon.com/bedrock/knowledge-bases/

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8
Q

Machine Learning

A

https://aws.amazon.com/what-is/machine-learning/

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9
Q

Foundation Models

A

https://aws.amazon.com/what-is/foundation-models/

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10
Q

What’s the Difference Between Structured Data and Unstructured Data?

A

https://aws.amazon.com/compare/the-difference-between-structured-data-and-unstructured-data/

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11
Q

Overfitting

A

https://aws.amazon.com/what-is/overfitting/

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12
Q

Underfitting vs. Overfitting

A

https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

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13
Q

What’s the Difference Between Supervised and Unsupervised Learning?

A

https://aws.amazon.com/compare/the-difference-between-machine-learning-supervised-and-unsupervised/

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14
Q

Inference Parameters

A

https://docs.aws.amazon.com/bedrock/latest/userguide/inference-parameters.html

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15
Q

Amazon Titan

A

https://aws.amazon.com/bedrock/titan/

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16
Q

Amazon Titan Image Generator Watermark Detection

A

https://aws.amazon.com/about-aws/whats-new/2024/04/watermark-detection-amazon-titan-image-generator-bedrock/

17
Q

Amazon Titan Image Generator, Multimodal Embeddings, and Text models

A

https://aws.amazon.com/blogs/aws/amazon-titan-image-generator-multimodal-embeddings-and-text-models-are-now-available-in-amazon-bedrock/

18
Q

Best practices to build generative AI applications on AWS

A

https://aws.amazon.com/blogs/machine-learning/best-practices-to-build-generative-ai-applications-on-aws/

19
Q

Amazon Q Business - How Amazon Q Business works

A

https://docs.aws.amazon.com/amazonq/latest/qbusiness-ug/how-it-works.html

20
Q

Generative AI

A

https://aws.amazon.com/what-is/generative-ai/

21
Q

Retrieval-Augmented Generation (RAG)

A

https://aws.amazon.com/what-is/retrieval-augmented-generation/

22
Q

Regions and Availability Zones

A

https://aws.amazon.com/about-aws/global-infrastructure/regions_az/

23
Q

Responsible AI resources

A

https://aws.amazon.com/machine-learning/responsible-ai/resources/

24
Q

Amazon SageMaker Clarify

A

https://aws.amazon.com/sagemaker/clarify/

25
Q

ML Governance with Amazon SageMaker

A

https://aws.amazon.com/sagemaker/ml-governance/

26
Q

Using Amazon Q Developer on AWS apps and websites

A

https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/q-on-aws.html

27
Q

Amazon Q Developer

A

https://docs.aws.amazon.com/pdfs/amazonq/latest/qdeveloper-ug/amazonq-developer-ug.pdf

28
Q

Using Amazon Q Developer in the IDE

A

https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/q-in-IDE.html

29
Q

Multimodal Data Analysis with AWS Health and Machine Learning Services

A

https://aws.amazon.com/blogs/industries/multimodal-data-analysis-with-aws-health-and-machine-learning-services/

30
Q

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

A

https://aws.amazon.com/blogs/industries/training-machine-learning-models-on-multimodal-health-data-with-amazon-sagemaker/

31
Q

Amazon Q

A

https://aws.amazon.com/q/

32
Q

AWS Trainium

A

https://aws.amazon.com/machine-learning/trainium/

33
Q

Amazon Rekognition

A

https://aws.amazon.com/rekognition/

34
Q

Large Language Models (LLM)

A

https://aws.amazon.com/what-is/large-language-model/

35
Q

Amazon SageMaker JumpStart

A

https://aws.amazon.com/sagemaker/jumpstart/

36
Q

Amazon SageMaker Jumpstart - Fine-tune a foundation model

A

https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-fine-tuning.html