AWS Certified Generative AI Developer Professional Exam Prep

Discover AWS management tools—the management console, CLI, and SDK—and learn infrastructure as code with CDK and CloudFormation, plus IDE tooling like the AWS toolkit.

Compare traditional programming with explicit rules to machine learning that learns patterns from data, builds models, and predicts unseen data through training, testing, and evaluation.

Learn supervised and unsupervised learning by modeling input-output relationships with labeled data and error-driven training. Explore classification, regression, clustering, using examples like ice cream sales vs temperature and grouping data.

Explore the end-to-end ml pipeline—from data collection, preprocessing and analysis, training, evaluation, operation, deployment, and inference—highlighting key considerations for each stage.

learn how to train ai models using prepared data, tune hyperparameters, avoid overfitting with train-test splits, and optimize performance with gradient descent and hyperparameter tuning.

Explore the technical evaluation and social responsibility of AI, focusing on data bias, fairness across groups, and GDPR-related risks, using tools like Amazon SageMaker Clarify.

Explore MLOps, the DevOps for machine learning, automating from development to production with CI, CD, continuous training, drift monitoring, and SageMaker experiments.

Compare custom and pre-trained models and apply transfer learning to adapt general knowledge to your tasks. Leverage AWS AI services to access pre-trained models via API with fine-tuning options.

Generative artificial intelligence creates new original content—text, images, audio, or code—by learning patterns from massive data and applying them to novel outputs.

Leverage foundation models trained on internet-scale data that use self-supervised learning to predict masked text from unlabeled data, and apply transfer learning to diverse tasks.

Identify and explain the main prompt attacks in generative AI, including prompt injection, Jane breaking, prompt rigging or exposure, prompt poisoning, and hijacking, and their impact on safety.

Explore how AWS layers AI services from managed APIs to generative AI platforms, usable without AI expertise. Discover Amazon Bedrock, Amazon SageMaker, and infrastructure for language, speech, and image processing.

Explore Amazon Polly text-to-speech in the AWS console, compare neural and standard engines, select English Joanna voice, use SSML for emphasis and pauses, and save synthesized speech to S3.

Amazon Q is a fully managed generative AI assistant for business chat, enabling developer coding help, VS Code, QuickSight visualizations, and Connect and AWS console integrations.

Explore Amazon Bedrock Playground with hands-on text and image generation using Titan Text G1. Compare models from the model catalog and adjust prompts; learn pay as you go, serverless Bedrock.

Do a hands-on creation and testing of an Amazon Bedrock knowledge base from the AWS whitepaper, using S3 data, Titan embeddings, and OpenSearch for retrieval-augmented generation.

Create a SageMaker feature group in the feature store with online and offline options, configure an S3 offline bucket and IAM role, and ingest data via Jupyter Lab.

Learn how to deploy Amazon SageMaker endpoints, including real-time serverless, real-time inference, asynchronous inference, and batch transform, with multi-model endpoints, multi-container endpoints, and serial inference pipelines.

Train a SageMaker model on the iris dataset using XGBoost to classify three iris species with softmax, using 100 training rounds and data prepared in S3.

Conduct a hands-on evaluation of a deployed iris classifier using iris dataset in SageMaker Studio, computing accuracy, confusion matrix, and classification report on a 30-sample validation set, achieving 100% accuracy.

Explore SageMaker inference recommender to run load tests and select optimal machine learning inference instance types by latency and throughput, and compare with AWS Compute Optimizer's CloudWatch-based recommendations.

Discover the SageMaker studio model registry: create a model group and collection, register a Jumpstart image classification model (mobilenet v2 1.0), and manage training and deletion permissions.

Leverage Amazon SageMaker Clarify to detect bias and explain model decisions, assess fairness and transparency, and evaluate foundation models on text summarization and Q&A with rouge and bot score.

Install, configure, and start the CloudWatch agent via System Manager, attach the CloudWatch agent Admin policy and CloudWatch Agent Server policy with least privilege, and configure metrics and logs.

Create an IAM role for Lambda and build a Python 3.11 function that outputs EMF formatted logs to CloudWatch metrics. Deploy, test, and view EMF-based metrics by namespace and dimensions.

Learn hands-on how AWS Config detects non-compliant EC2 instances by type and automatically stops them via a remediation action using an IAM role.

Discover AWS X-Ray, a distributed tracing service that monitors complex microservices, visualizes service maps and traces, and helps identify bottlenecks, latency, and errors across requests.

Explore AWS Systems Manager as a hybrid environment operational tool, covering patch management with a patch baseline, maintenance windows, run command, inventory, and compliance reporting across managed instances.

Learn how AWS CloudFormation templates define resources through sections like parameters, mappings, conditions, transform, and outputs, and deploy across accounts using stacks and stack sets.

Preview changes before deployment with AWS CloudFormation change sets, showing what is added, removed, or modified. Create, review, and execute the change set, or delete it if issues arise.

Set up a cross-account IAM role with an external ID on the trust policy, grant Amazon S3 read-only access, and test with STS assume-role to verify its behavior.

Learn how AWS KMS creates and manages cryptographic keys, including CMKs and AWS managed keys, with bring your own key, automatic rotation, and the key lifecycle from creation to deletion.

Create and configure secrets in AWS Secrets Manager using key-value pairs, optionally rotate with Lambda, and retrieve, update, or delete secrets via Cloud Shell commands.

Compare object, block, and file storage, noting object storage uses blobs with metadata and http/https access for scalable archiving and web content, with aws s3 options.

Explore how S3 secures data with access control via IAM policies, bucket policies, and ACLs, enables signed URLs, server-side or client-side encryption with KMS, versioning, and object locking.

Configure S3 cross-origin requests by creating a resource and a website bucket, applying policies, enabling static website hosting, and updating CORS to let the website fetch the resource bucket's images.

Explore creating and mounting an Amazon EFS file system to an EC2 instance, configure security groups for NFS, and customize throughput modes, backups, and encryption settings.

Explore Amazon EC2 virtual servers, high availability across multiple availability zones, and pay-as-you-go pricing. Learn about instance types, AMI templates, and placement groups for scalable, fault-tolerant computing.

Explore how aws ec2 instance types classify workloads across general-purpose, compute, memory, storage, and accelerated categories, including t series bursts and dedicated host or dedicated instance options.

Learn how to create and run ECS clusters on Fargate, define tasks with nginx, configure networking and metrics, and enable capacity providers, Fargate spot, scheduling, and EC2 alternatives.

Discover the AWS Lambda execution environment, its init, invoke, and shutdown lifecycle, cold and warm starts, and how provisioned and reserved concurrency manage latency and throttling.

Explore how AWS Lambda uses event source mapping to batch items from SQS, Kinesis, and DynamoDB streams, providing at least once delivery, with optional filtering and asynchronous invocation.

Link the api gateway to a lambda function using version aliases to separate development and production, and implement a canary release with weighted routing for the new feature.

Learn how AWS batch runs hundreds of thousands of containerized jobs in parallel for large-scale batch processing, choosing Fargate or EC2 and saving results to S3.

This hands-on session guides you through creating security groups for public and private subnets and configuring a network ACL, with SSH inbound rules and subnet-level firewall behavior.

Discover how CloudFront secures content with default ssl, ddos protection via waf and shield, geolocation and ip-based distribution, origin access controls using oai and oac, plus signed url and cookies.

Explore CloudFront edge functions that run at edge locations to customize requests and responses, enable basic authentication and authorization, and reduce origin load with CloudFront functions and Lambda@Edge in Node.js/Python.

Explore AWS Direct Connect, offering dedicated and hosted connections with private, public, and transit virtual interfaces, Direct Connect gateway and transit gateway to connect VPCs across regions, with VPN backup.