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Make $20 of credits by using an AI foundation model using AWS Bedrock

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Overview

This article seeks to be a tutorial to logically present a hands-on experience to learn how to use AI by Amazon Bedrock.

This is not a 5 minute summary to run mindlessly, but a step-by-step guided course so you master this like a pro, enough to pass the 3-year AIF-C01 AWS Certified AI Practioner: 65 questions in 90 minutes for $100. The Exam Guide Content Domains:

  1. 20%: Fundamentals of AI and ML
    • Task Statement 1.1: Explain basic AI concepts and terminologies.
    • Task Statement 1.2: Identify practical use cases for AI.
    • Task Statement 1.3: Describe the ML development lifecycle.
  2. 24%: Fundamentals of GenAI
    • Task Statement 2.1: Explain the basic concepts of GenAI.
    • Task Statement 2.2: Understand the capabilities and limitations of GenAI for solving business problems.
    • Task Statement 2.3: Describe AWS infrastructure and technologies for building GenAI applications.
  3. 28%: Applications of Foundation Models
    • Task Statement 3.1: Describe design considerations for applications that use foundation models (FMs).
    • Task Statement 3.2: Choose effective prompt engineering techniques.
    • Task Statement 3.3: Describe the training and fine-tuning process for FMs.
    • Task Statement 3.4: Describe methods to evaluate FM performance.
  4. 14%: Guidelines for Responsible AI
    • Task Statement 4.1: Explain the development of AI systems that are responsible.
    • Task Statement 4.2: Recognize the importance of transparent and explainable models.
  5. 14%: Security, Compliance, and Governance for AI Solutions
    • Task Statement 5.1: Explain methods to secure AI systems.

    • Task Statement 5.2: Recognize governance and compliance regulations for AI systems.

    • https://www.youtube.com/watch?v=WZeZZ8_W-M4 Freecodecamp with cheat sheets from ExamPro
    • https://youtube.com/playlist?list=PL7Jj8Ba9Yr6BLf2lS4Nfa2jdl0KpdvqYC&si=mZrE2_8vdDkZSLyl CloudExpert Solutions India
    • https://www.youtube.com/watch?v=6cMLDgFrWqs
    • https://www.youtube.com/watch?v=kv0VAH6at9E

  6. Take the 65-question free practice exam at:

    CodeNinja

??? Those who created an AWS account can get a $20 credit for completing this.

Establish AWS Account

Begin by following my aws-onboarding tutorial to securely establish an AWS account.

https://docs.aws.amazon.com/cli/latest/userguide/cli-authentication-user.html

Amazon AI products & services

DEFINITION: The list of the many AI-related services and brands from AWS:

Rule-Based Systems: Early AI relied on explicit programming and fixed rules

Machine Learning: Introduced data-driven pattern recognition

Deep Learning: Enables complex pattern processing through neural networks:

Amazon developed AI utilities:

Amazon developed a suite of industry-specific AI services:

Generative AI: creates new content from learned patterns.

Amazon Q capabilities are (as of Jan 2026) provided within
Amazon Kiro CLI and Kiro.app created based on a fork of Microsoft’s VSCode IDE GUI app.

DEFINITION: A Prompt is the input text or message that a user provides to a foundation model to guide its response. Prompts can be simple instructions or complex examples. Prompts provided to generative AI (that works like ChatGPT) are processed within the
Amazon Bedrock GUI.

A “system prompt” defines the model’s behavior, personality, and constraints, applied to many prompts.

“fine-tuning” a LLM trains a pre-trained model on additional data (weights) for specific tasks. Used to teach the model a specific style, format, or domain expertise.

A hallucination is when a model generates plausible-sounding but factually incorrect information

DEFINITION: Zero-Shot Prompting is a type of prompt that provides no examples—just a direct instruction for the model to complete a task based on its pre-trained knowledge.

DEFINITION: Few-Shot Prompting is a prompt that includes a few examples to show the model how to respond to similar inputs.

https://www.coursera.org/learn/getting-started-aws-generative-ai-developers/supplement/IOgNx/prompt-engineering-guide

https://d2eo22ngex1n9g.cloudfront.net/Documentation/User+Guides/Titan/Amazon+Titan+Text+Prompt+Engineering+Guidelines.pdf Amazon Titan Text Prompt Engineering Guidelines

https://community.aws/content/2tAwR5pcqPteIgNXBJ29f9VqVpF/amazon-nova-prompt-engineering-on-aws-a-field-guide-by-brooke?lang=en Amazon Nova: Prompt Engineering on AWS - A Field Guide

“Agentic AI” is an industry-wide term for software that exhibits agency, adapting its behavior to achieve specific goals in dynamic environments.

  1. View YouTube videos about Bedrock:

    https://www.youtube.com/playlist?list=PLhr1KZpdzukfmv7jxvB0rL8SWoycA9TIM

  2. Text-based “Workshop” introducing Bedrock

Amazon Bedrock GUI Menu

Amazon Bedrock is a service fully managed by AWS to provide you the infrastructure to build generative AI applications without needing to manage (using Cloud Formation, etc.).

  1. On the AWS Console GUI web page, press Option+S or click inside the Search box. Type enough of “Amazon Bedrock” and press Enter when that appears. It’s one of Amazon’s AI services: aws-search-1318x170.png

  2. Cursor over the “Amazon Bedrock” listed to reveal its “Top features”:

    Agents, Guardrails, Knowledge Bases, Prompt Management, Flows

    DEFINITION: Guardrails are Configurable controls in Amazon Bedrock used to detect and filter out harmful or sensitive content from model inputs and outputs.

    Overview Menu Model catalog

  3. Click on the “Amazon Bedrock” choice for its “Overview” screen and left menu:

    bedrock-menu-512x2182.png

  4. Notice the menu’s headings reflect the typical sequence of usage:

    Discover -> Test -> Infer -> Tune -> Build -> Assess

  5. Although “Configure and Learn” is listed at the bottom of the Bedrock menu, that’s where we need to start.

  6. Click “User Guide” to open a new tab to click “Key terminology”.

  7. Scroll down to “View related pages” such as Definitions from the Generate AI Lens that opens in another tab.

    Pricing

  8. Click on “Amazon Bedrock pricing” from the left menu.

    Bedrock pricing

    https://docs.aws.amazon.com/bedrock/latest/userguide/sdk-general-information-section.html

  9. Click “Bedrock pricing” for a new tab.

    “Pricing is dependent on the modality, provider, and model” choices. Also by Region selected.

    Discover Model choice

  10. Click the Amazon Bedrock web browser tab (green icon ) to return to the Overview page.

  11. Click on “View Model catalog” to see the Filters to select the provider and Modality you want to use.

    Notice that choosing “Anthropic” as your provider involves filling out “paperwork”. So let’s avoid that.

    VIDEO: Between Anthropic and AWS have a massive circular investment in AI data center build-out in Indiana that use Amazon-designed Trinium chips (rather than NVIDIA). 30 buildings will consume 2.2 gigawatts.

    VIDEO: Try several models so you’re not guessing what works best. Models behave differently depending on your data and goals.

    TODO: Use Ray.io to track run times and evaluate.

    Amazon models

    PROTIP: Explore AWS AI Service Cards which describe each of Amazon’s own models.

    https://aws.amazon.com/nova/models/?sc_icampaign=launch_nova-models-reinvent_2025&sc_ichannel=ha&sc_iplace=signin

    Amazon Q Developer regions

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

    DEFINITION: Token is a unit of text processed by the model. A token could be a word, part of a word, or punctuation. Both input prompts and output responses are measured in tokens.

    DEFINITION: Max Tokens is a setting that limits the length of the model’s response by defining the maximum number of tokens (chunks of words or characters) it can generate.

    DEFINITION: Temperature is a parameter used during inference to control the randomness of model output. Higher values produce more creative or varied responses; lower values yield more consistent and focused results.

    Manage IAM policies for permissions

  12. Click “Settings” near the bottom of the menu.
  13. Click “Manage IAM policies” for a new tab.

AWS Bedrock IAM role

  1. Set up your Amazon Bedrock IAM role
  2. Sign into Amazon Bedrock in the console
  3. Request access to foundation models (Amazon Nova) by following the steps in the Amazon Bedrock user guide.
  4. Generate Bedrock API key (good for 30 days).

  5. Make your first API call.

  6. https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-ex-cli.html Run example Amazon Bedrock API requests with the AWS Command Line Interface

  7. https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-ex-python.html Run example Amazon Bedrock API requests through the AWS SDK for Python (Boto3)

  8. https://docs.aws.amazon.com/nova/latest/nova2-userguide/customization.html Customizing Amazon Nova 2 models

    https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agentcore-get-started-toolkit.html

    Agent frameworks getting in your way?

    To Develop intelligent agents with Amazon Bedrock, AgentCore, and Strands Agents by Eduardo Mota

    • strands-python.md and
    • agentcore_strans_requirements.md

    Create a IAM user

    If you used a root account email and password to sign in:

  9. Click “Users” on the left menu, then the orange “Create user” for a new tab (with a red icon).
  10. NAMING CONVENTION: Do not enter an email address, but make up ??? (up to 64 characters).
  11. Check “Provide user access to the AWS Management Console”.
  12. Leave “Custom password” unchecked.
  13. Leave “users must create a new password at next sign-in” unchecked.
  14. Click “Next”.
  15. Click “Attach policies directly”.
  16. Type “Bedrock” in the search field.
  17. To see long Policy names, expand the column | in front of the “Type” heading text.

  18. Check “AmazonBedrockFullAccess” (not the best practice)???

    “Effect”: “Allow”, “Action”: “bedrock:InvokeModel”, “Resource”: “arn:aws:bedrock:::foundation-model/”

  19. Click “Next”.
  20. Tag???
  21. Click “Create”.
  22. Click “Download .csv file” or:
    1. Click the icon to copy the “Console sign-in URL” value and paste that in your Password Manager entry.
    2. Click the icon to copy the “User name” value and paste that in your Password Manager entry.
    3. Click “Show”, then click the icon to copy the “Console password” value. In your Password Manager, paste the value.
  23. Optionally Click “Enabled without MFA”, then ???
  24. Click “Create access key” blue link.
  25. Click “Command Line Interface (CLI)”.
  26. Click “I understand…”
  27. Click “Next”, check to confirm. “Next” again.
  28. Type a Description. ???
  29. Click “Create access key” orange pill.
  30. Click the icon to copy the “Access key” and paste
  31. Click “Done”.
  32. Click “Generate API Key”.
  33. Set Expiry. ???
  34. Click “Generate API key”.
  35. Click the blue copy icon among “Variables”. Paste that in your Password Manager.

  36. Close.

    Access

    ???

  37. Model Access Not Enabled

You need to explicitly request access to models in Bedrock Go to AWS Console → Bedrock → Model access Select the models you want to use and request access Access is usually granted immediately for most models

  1. Region Mismatch

The model you’re trying to use isn’t available in your current region Check which models are available in your region Claude models are available in us-east-1, us-west-2, ap-northeast-1, ap-southeast-1, eu-central-1, and eu-west-3

  1. Incorrect Model ID

Make sure you’re using the correct model identifier For Claude models, use formats like:

anthropic.claude-3-5-sonnet-20241022-v2:0 anthropic.claude-3-5-haiku-20241022-v1:0

https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html#model-access-permissions

### Chat / Text playbaround”

  1. Click the web app green icon browser tab to return to Amazon Bedrock.
  2. Click “Chat / Text playbaround” menu.
  3. …
  4. Click “Run”.

Well architected

https://aws.amazon.com/architecture/well-architected/

Permissions

REMEMBER: First-time Amazon Bedrock model invocation requires AWS Marketplace permissions.

  1. Open the Amazon Bedrock console at https://console.aws.amazon.com/bedrock/
  2. Ensure you’re in the US East (N. Virginia) (us-east-1) Region
  3. From the left navigation pane, choose “Model access” under Bedrock configurations
  4. In What is model access, choose Enable specific models
  5. Select Nova Lite from the Base models list
  6. Click Next
  7. On the Review and submit page, click Submit
  8. Refresh the Base models table to confirm that access has been granted[4][5]

Your IAM User or Role should have the AmazonBedrockFullAccess AWS managed policy attached[5].

To allow only Amazon Nova Lite v1:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": [
        "arn:aws:bedrock:*::inference-profile/us.amazon.nova-lite-v1:0",
        "arn:aws:bedrock:*::foundation-model/amazon.nova-lite-v1:0"
      ]
    }
  ]
}

From Activity

If you clicked an Activity to earn credits, you would be at:

https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/text-generation-playground?tutorialId=playground-get-started Amazon Bedrock > Chat / Text playground

Follow the blue pop-ups (but don’t click “Next” within them):

  1. Click the orange “Select model” orange button.

    PROTIP: Do not select “Anthropic” to avoid their mandatory “use case survey” and possibly thrown into https://console.aws.amazon.com/support/home hell.

  2. Click the “+” at the top of your browser bar to see the difference between the various LLM models: Supported foundation models in Amazon Bedrock

    PROTIP: Some models are available only in a single region. Amazon Nova Lite v1 is only available in us-east-1

    https://wilsonmar.github.io/aws-benchmarks

    • “Google” generally has the lowest cost
    • AI21 Labs
    • Moonshot AI
    • Writer
    • DeepSeek

  3. Switch back to the Amazon Bedrock tab.

  4. Click “Amazon” to select a model provider. The “Cross-region” selection should appear.

  5. Click “Nova Lite v1”.

    PROTIP: Generally, for least cost, select the smallest number of “B” or tokens, such as “Gemma 3 4B IT v1”.

  6. Ignore the “Inference” selections that appear.
  7. Click “Apply”.

  8. Type in a question under “Write a prompt and choose Run to generate a response.”

    PROTIP: Refer to a chat template to craft a prompt. See Prompt engineering concepts:

    • Task you want to accomplish from the model
    • Role the model should assume to accomplish that task
    • Response Style or structure that should be followed based on the consumer of the output.
    • Success evaluation criteria: bullet points or as specific as some evaluation metrics (Eg: Length checks, BLEU Score, Rouge, Format, Factuality, Faithfulness).

      Examples:
    • Create a social media post about my dog’s birthday.
    • What time is it?
    • Where is NYC?
    • Who am I?
    • When will the ISS next pass over NYC
  9. Click “Run”.
  10. Celebrate if you don’t get “ValidationException Operation not allowed” in a red box.

CloudTrail Troubleshooting

If it’s an IAM policy issue, you may be able to check the error details in the CloudTrail event history. https://docs.aws.amazon.com/awscloudtrail/latest/userguide/view-cloudtrail-events-console.html

https://docs.aws.amazon.com/cli/v1/userguide/cli_cloudtrail_code_examples.html CloudTrail examples using AWS CLI

When encountering the “ValidationException: Operation Not Allowed” error in Amazon Bedrock, there are several potential causes and solutions to consider:

Account verification: If your AWS account is new, it may need further verification. Some users have reported that creating an EC2 instance can help verify the account.

Regional availability: Verify that the models you’re trying to access are available in your selected region. Different models have different regional availability.

Model access permissions: Even with administrator rights, specific model access must be granted. Since you mentioned the “Model Access” page is no longer available, this could be part of the issue.

CloudTrail investigation: You can check CloudTrail event history for more detailed error information that might provide insights into the specific cause.

Contact AWS Support: For Bedrock access issues, you can open a case with AWS Support under “Account and billing” which is available at no cost, even without a support plan. This appears to be the recommended solution for your situation.

You can attempt to troubleshoot the issue by using the Amazon Bedrock API, construct a POST request to the endpoint https://runtime.bedrock.{region}.amazonaws.com/agent/{agentName}/runtime/retrieveAndGenerate, where {region} is your AWS region and {agentName} is the name of your Bedrock agent. The request body should follow the provided syntax, filling in the necessary fields such as knowledgeBaseId, modelArn, and text for the input prompt.

curl -X POST \
  https://runtime.bedrock.us-east-1.amazonaws.com/agent/{yourAgent}/runtime/retrieveAndGenerate \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_ACCESS_TOKEN' \
  -d '{
        "input": {
            "text": "What is the capital of Dominican Republic?"
        },
        "retrieveAndGenerateConfiguration": {
            "knowledgeBaseConfiguration": {
                "generationConfiguration": {
                    "promptTemplate": {
                        "textPromptTemplate": "The answer is:"
                    }
                },
                "knowledgeBaseId": "YOUR_KNOWLEDGE_BASE_ID",
                "modelArn": "YOUR_MODEL_ARN",
                "retrievalConfiguration": {
                    "vectorSearchConfiguration": {
                        "numberOfResults": 5
                    }
                }
            },
            "type": "YOUR_TYPE"
        },
        "sessionId": "YOUR_SESSION_ID"
      }'

Projects

A. VIDEO When is the next time the ISS (International Space Station) will fly over where I am? Strand MCP Agent.

B. Add that time as an event in my Google Calendar.

Sagemaker

  1. Become a member of an Amazon SageMaker Unified Studio domain. Your organization will provide you with login information; contact your administrator if you don’t have your login details.

  2. https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-ex-sm.html Run example Amazon Bedrock API requests using an Amazon SageMaker AI notebook in SageMaker Unified Studio

  3. Find serverless models in the Amazon Bedrock model catalog. Generate text responses from a model by sending text and image prompts in the chat playground or generate and edit images and videos by sending text and image prompts to a suitable model in the image and video playground.

  4. Generate text responses from a model by sending text and image prompts in the chat playground or generate and edit images and videos by sending text and image prompts to a suitable model in the image and video playground.

Python Boto3 in a Jupyter Notebook

VIIDEO

  1. Setup your CLI Jupyter Notebook environment to run Python code:

    https://www.coursera.org/learn/getting-started-aws-generative-ai-developers/supplement/CABQW/exercise-invoking-an-amazon-bedrock-foundation-model

    • Task 1: Install Python (Windows)
    • Task 2: Install Visual Studio Code 2022 (Windows)

    • Task 3: Install AWS CLI (Windows)

    • Task 1: Install Brew + Python (Mac)
    • Task 2: Install Visual Studio Code 2022 (Mac)

    • Task 3: Install AWS CLI (Mac)

    • Task 4: Create an AWS Account
    • Task 5: Create an IAM user and configure AWS CLI
    • Task 6: Create Python environment and installing packages
    • Task 7: Enable Amazon Bedrock Models
    • Task 8: Create an S3 bucket for video generation
    • Task 9: Using Amazon Bedrock to create videos
    • Task 10: Using Amazon Bedrock for text generation
    • Task 11: Using Amazon Bedrock for streaming text generation
    • Task 12: Cleanup

  2. View Python program code samples at: https://aws-samples.github.io/amazon-bedrock-samples/ based on coding at
    https://github.com/aws-samples/aws-bedrock-samples

    PROTIP: The Free tier of Pricing for Amazon Q Developer provides for 50 agentic requests per month, so try a different example each month.

    Python coding files have a file extension of .ipynb because they were created to be run within a Jupyter Notebook environment.

    An agentic request is any Q&A chat or agentic coding interaction with Q Developer through either the IDE or Command Line Interface (CLI). All requests made through both the IDE and CLI contribute to your usage limits.

    The $19/month “Pro” plan automatically opts your code out from being leaked to Amazon for their training.

    DEFINITION: Synchronous Inference is real-time interaction where the model returns results immediately after a prompt is submitted. DEFINITION: Asynchronous Inference is a delayed interaction where the prompt is processed in the background, and the results are delivered later. This is useful for long-running tasks or DEFINITION: batch inference - a method to process multiple prompts at once, often used for offline or high-volume jobs.

    Notice there are code that stream

References:

Here’s how to use Bedrock’s InvokeModel API for text generation:

The InvokeModel API is a low-level API for Amazon Bedrock. There are higher level APIs as well, like the Converse API. This course will first explore the low level APIs, then move to the higher level APIs in later lessons.

import boto3
import json

# Initialize Bedrock client

bedrock_runtime = boto3.client('bedrock-runtime')

model_id_titan = "amazon.titan-text-premier-v1:0"

# Text generation example
def generate_text():
   payload = {
         "inputText": "Explain quantum computing in simple terms.",        
         "textGenerationConfig": {            
                "maxTokenCount": 500,                  
                "temperature": 0.5,            
                "topP": 0.9        
           } 
     }
     response = bedrock_runtime.invoke_model(         
           modelId=model_id_titan,        
           body=json.dumps(payload)    
     )

return json.loads(response['body'].read())

generate_text()

Create apps

  1. Create a chat agent app to chat with an Amazon Bedrock model through a conversational interface.

  2. Create a flow app that links together prompts, supported Amazon Bedrock models, and other units of work such as a knowledge base, to create generative AI workflows.

Evaluate models

  1. Evaluate models for different task types.

    https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html

  2. Bedrock Guardrails for Governance

    See https://www.coursera.org/learn/getting-started-aws-generative-ai-developers/supplement/mJUnP/exercise-amazon-bedrock-guardrails

    Amazon Bedrock Guardrails helps build trust with your users and protect your applications.

    https://github.com/aws-samples/amazon-bedrock-samples/tree/main/responsible_ai/bedrock-guardrails

    DEFINITION: “Transparency” means being clear about AI capabilities, limitations, and when AI is being used.

    “Explainability” is the ability to understand and interpret how an AI model makes its predictions

    DEFINITION: Guardrails are configurable filters that operate on both input and output sides of model interaction:

    DEFINITION: Input Protection - screening user inputs before they reach the model:

    • Filters harmful user inputs before reaching the model

    • Prevents prompt injection attempts

    • Control topic boundaries

    • Blocks denied topics and custom phrases

    • Protect sensitive information

    • Ensure response quality through grounding and relevance checks

    Output Safety to validate model responses before they reach users:

    • Screens model responses for harmful content

    • Masks or blocks sensitive information

    • Ensures responses meet quality thresholds

  3. Under the “Build” menu category, click Guardrails. Notice there are also Guardrails for Control Tower (unrelated).

  4. At the bottom, click “info” to the right of “System-defined guardrail profiles” for the current AWS Region selected for for routing inference.

    Notice that a Source Region defines where the guardrail inference request orginates. A Destination Region is where the Amazon Bedrock service routes the guardrail inference request to process.

    Guardrails are written in JSON format.

    Creating policies will generate a separate and individual policy for all the AWS Regions included in this profile.

  5. Click “Create guardrail”. VIDEO:

    1. Provide guardrail details
    2. Configure content filters
    3. Add denied topics: Harmful categories and Prompt attacks (Hate, Insults, Sexual, Violence, Misconduct, etc.)
    4. Add word filters (for profanity)
    5. Add sensitive information filters:
    6. Add contextual grounding check
    7. Review and create

    Each Guardrail can be applied across multiple foundation models.

  6. Production monitoring with CloudWatch

    Share your app

  7. Share your app to collaborate with your team.

POC to Production

Cross-Region inference with Amazon Bedrock Guardrails lets you manage unplanned traffic bursts by utilizing compute across different AWS Regions for your guardrail policy evaluations.

https://github.com/aws-samples/amazon-bedrock-samples/tree/main/poc-to-prod From Proof of Concept (PoC)

Tags for resource cost management with automatic cleanup.

Generate Documentation

https://www.coursera.org/learn/getting-started-aws-generative-ai-developers/lecture/r9sgh/demo-amazon-q-developer-documentation-agent Spring Boot

Notes

A neural network’s activation function is how it stores training data. Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns and relationships in data. Without activation functions, a neural network would only be able to learn linear relationships, regardless of how many layers it has.

Embeddings are Numerical vector representations of data that capture semantic meaning.

The Transformer architecture’s key innovation enables efficient processing by its self-attention mechanism. This enables parallel processing and captures long-range dependencies better than recurrent networks.

References

https://aws.amazon.com/bedrock/getting-started/

SOP Why your AI agents give inconsistent results, and how Agent SOPs fix it

https://www.youtube.com/watch?v=ab1mbj0acDo Integrating Foundation Models into Your Code with Amazon Bedrock by Mike Chambers, Dev Advocate

26-02-07 v005 cert :aws-bedrock.md created 2026-01-25