Module 8: AI/ML and Data Analytics

Machine learning (ML) is a type of AI for training machines to perform complex tasks without explicit instructions. This training process involves finding patterns in vast amounts of historical data.

What is produced as a result of the ML training process?

An explicit ML rule book that dictates how to perform tasks

An ML model that can make predictions or decisions

An ML data report that summarizes the data

An ML database cataloging the types of data found

An ML model that can make predictions or decisions

Amazon Comprehend

Extracts important things from docs

Amazon Polly

Converts text to speech

Amazon Transcribe

Converts speech to text

Amazon Kendra

Search for answers within large enterprise content (like KARA?)

The owner of a car dealership wants to determine why her service department has lost business over the past year. She wants to analyze a large number of documented customer comments to better understand customer sentiment.

Which AWS service would work well for this use case?

Amazon Translate

Amazon Comprehend

Amazon Personalize

Amazon Textract

Amazon Comprehend

A healthcare company wants to add a conversational interface to its customer support application using a ready-made solution.

Which AWS service could they choose?

Amazon Translate

Amazon Personalize

Amazon Lex

Amazon Comprehend

Amazon Lex

An instructional designer is developing a new course on customer service skills. He wants to include several simulated calls to reinforce the learning. Because he doesn't have access to a recording studio, he needs a quick way to convert his scripts to speech.

Which service would work well for this use case?

Amazon Textract

Amazon Kendra

Amazon Translate

Amazon Polly

Amazon Polly

A small tech company wants to develop their own customized machine learning (ML) model without managing the underlying infrastructure. The company is looking for a solution that both their data scientists and business analysts can use.

Which AWS service should they choose?

Amazon Kendra

Amazon SageMaker AI

Amazon EC2

Amazon Lex

Amazon SageMaker AI

A team of machine learning (ML) engineers is developing a new ML model for a highly specialized application. They need complete control over the ML training process. So, they are developing their own custom solution using the PyTorch ML framework.

What is an ML framework?

A software library with pre-built, optimized components

An Amazon EC2 instance that is optimized for ML training

An integrated development environment (IDE) that provides simplified access to the company's ML projects

A managed service pre-trained to perform specific functions

A software library with pre-built, optimized components

Generative AI is a type of deep learning powered by extremely large ML models that are pre-trained on vast collections of data.

What are these models called?

Generative models

Feature models

Massive models

Foundation models

Foundation Models

A large advertising agency wants to quickly integrate a new content generation feature into its existing enterprise-wide design application. The new feature needs to be able to generate both text and images. The agency doesn't want to manage any new infrastructure.

Which service would work best for this use case?

Amazon SageMaker JumpStart

Amazon Bedrock

Amazon Q Business

Amazon Q Developer

Amazon SageMaker JumpStart

A software development company is working on a new product with a very tight deadline. The company needs a way to develop code faster without sacrificing reliability or security.

Which service could best help this company meet its deadline?

Amazon SageMaker JumpStart

Amazon Bedrock

Amazon Q Business

Amazon Q Developer

Amazon Q Developer

ETL Processes

Extra data from source systmes and store it

Data pipelines are automated assembly lines used to make the ETL process efficient and repeatable.

What does ETL stand for?

Explore, transfer, log

Extract, transform, load

Evaluate, test, launch

Export, translate, link

Extract, transform, load

Amazon Kinesis Data Streams

Real time ingestion of data process. Fully managed serverless service

Amazon Data Firehouse

Helps with the ETL process by automatic provisioning and scaling.

Data ingestion service

Moving data into chosen storage solution

Amazon Redshift

Fully managed data warehouse that can store structured data

AWS Glue Data Catalog

Data catalog that provides enhanced data discovery

AWS Glue

Data preparation service

Amazon EMR

Large scale data for processing with big data

Amazon Athena

Run SQL queries to analyze data in relational data sources

Amazon QuickSight

Create dashboards and reports from data sources without managing infrastructure

Amazon OpenSearch Service

Search content with keyword and queries

A data analytics team is creating an automated data pipeline on AWS.

Which AWS services could they choose for data ingestion? (Select TWO.)

• Amazon Redshift

• Amazon Kinesis Data Streams

• Amazon EMR

• AWS Glue Data Catalog

• Amazon Data Firehose

Amazon Kinesis Data Streams and Amazon Data Firehouse

The data analytics team must ingest vast amounts of unstructured data into its pipeline.

Which AWS service is the BEST choice for storing this data?

Amazon Athena

Amazon Redshift

Amazon Data Firehose

Amazon S3

Amazon S3

Which AWS service is BEST suited for data processing in a data pipeline?

AWS Glue

Amazon QuickSight

Amazon Data Firehose

Amazon S3

AWS Glue

Which AWS services could the data analytics team choose for data visualization? (Select TWO.)

• Amazon Data Firehose

• Amazon QuickSight

• Amazon Athena

• AWS Glue

• Amazon OpenSearch Service

Amazon QuickSight and Amazon OpenSearch Service

A financial services company is developing an application to analyze real-time stock data so its team of analysts can make immediate trading decisions. The company needs to ingest real-time stock market data without worrying about servers or scaling capacity.

Which AWS service would meet their needs?

Amazon Kinesis Data Streams

Amazon EMR

Amazon Athena

Amazon QuickSight

Amazon Kinesis Data Streams

An e-commerce company wants to add a product recommendation engine to its online application to increase sales. The development team wants the recommendations to be relevant for each individual customer.

Which pre-built AWS AI service would work well for this use case?

Amazon Personalize

Amazon Kendra

Amazon Lex

Amazon Comprehend

Amazon Personalize

A small development team is looking to add a feature to its application that converts text to speech. 

Which pre-built AWS AI service can be used for this task?

Amazon Personalize

Amazon Textract

Amazon Polly

Amazon Comprehend

Amazon Polly

The extract, transform, load (ETL) process is often used to provide clean and accessible data in a format that is usable by analytics tools and AI algorithms.

How does a data pipeline improve this process?

Data pipelines make the ETL process more efficient and repeatable.

Data pipelines eliminate the need for data transformations.

Data pipelines increase the amount of raw data collected.

Data pipelines reduce the variety of data sources used by ETL.

Data pipelines make the ETL process more efficient and repeatable.

Amazon Bedrock is a fully managed service that was specifically designed for working with large foundation models (FMs) and building generative AI applications.

What does the service provide to access FMs from Amazon and leading AI startups?

A single API

Free, unlimited use

An open source repository

Dedicated cloud storage

A single API

Which AWS service can be used to build, train, and deploy a customized machine learning (ML) model without worrying about the underlying infrastructure?

Amazon Comprehend

Amazon EMR

Amazon Personalize

Amazon SageMaker AI

Amazon SageMaker AI

Both classical programming and machine learning can be used to train computers to perform tasks.

What is the main difference between the two approaches?

Classical programming is used for mathematical operations, whereas machine learning is exclusively used for pattern recognition in images and text.

Classical programming creates explicit rules for the computer to follow. In machine learning, computers make predictions using patterns learned from historical data.

Classical programming requires more computational resources than machine learning, making it less efficient for complex tasks.

Classical programming is used for simple tasks, whereas machine learning is reserved for extremely complex tasks.

Classical programming creates explicit rules for the computer to follow. In machine learning, computers make predictions using patterns learned from historical data.

A large healthcare organization wants to improve employee productivity. The company is searching for a pre-built generative AI assistant that can answer questions, help solve problems, and take actions using the data and expertise found in its information repositories.

Which AWS service would work well for this use case?

Amazon Q Business

Amazon SageMaker JumpStart

Amazon Bedrock

Amazon Q Developer

Amazon Q Business

Data can come from many different sources. To provide insights, the data must be consolidated in a single location. There are two storage options for this. Data lakes store vast amounts of raw data, and data warehouses are optimized for business intelligence.

Which AWS services are typically used as a data lake and data warehouse?

Amazon Redshift is a popular choice for data lakes, whereas Amazon S3 is a data warehouse service.

Amazon S3 is a popular choice for data lakes, whereas Amazon Athena is a data warehouse service.

Amazon EMR is a popular choice for data lakes, whereas Amazon Redshift is a data warehouse service.

Amazon S3 is a popular choice for data lakes, whereas Amazon Redshift is a data warehouse service.

Amazon S3 is a popular choice for data lakes, whereas Amazon Redshift is a data warehouse service.

Generative AI is a type of deep learning powered by extremely large machine learning (ML) models known as foundation models (FMs).

What are characteristics of FMs? (Select TWO.)

• FMs are pre-trained on vast collections of data.

• FMs are programmed with explicit rules.

• FMs are only used to create images.

• FMs are trained to perform singular tasks.

• FMs can be adapted to perform multiple tasks.

FMs can be adapted to perform multiple tasks.

FMs are pre-trained on vast collections of data.