I have given 5 of TD practice exam and got like 66/78/67/73/73 all in timed mode. submitted by /u/Grouchy-Geologist407 [link] [comments]

Hello! I have been working in IT industry for 7 years now 4 years at a Service provider being a network analyst then past three years with Nokia as Network engineer obtained various certs in the Cisco, Juniper, Fortinet, but now I feel like I am stuck in my career, unable to find opportunities to grow and evolve. I want to transition myself into Cloud Network/Architect roles. I tried to push myself to study AWS multiple times and find it relatively easy specially with 7+ years of networking experience I still wonder how do I get an entry level job with no cloud exposure? Please dont advise on doing open source projects Its hard to believe I would be even able to land an interview let alone a job with just some DIY projects. Has anyone made the jump to a more cloud/serverless network engineering roles and like to share their journey? TIA! submitted by /u/sheryyj [link] [comments]

Hello everyone, I have a small issue that keeps me from registering for the exam. So I have been studying for over a month now using the usual combo (Stephane Maarek + Tutorials Dojo). Right now I’m done with the SM course and I’m doing the TD exams on review mode. However, with every exam that I do I find questions that involve knits and grits in some services that are somewhat too in-depth for the usual SAA curriculum (especially in the Networking and Security sections). Mind you I score around 65-85% on each exam, the wrong questions are usually those that involve these knits and grits I just mentioned. I’ve been trying to push myself to register for the exam for the past 10 days but I keep holding back out of fear that I might face a lot of these questions in the exam. At the same time I’m so tired of studying and I just want to get over this phase as quick as I can. A small detail that contributes to the fear is that I will be paying for the exam using some money i saved up from freelancing (I rarely get work) so I don’t want it going to waste. Any advice? Is my fear valid or am I over-worried? Thanks! submitted by /u/RandomAverageUser [link] [comments]

Starting today, Amazon EC2 Hpc7a instances are available in additional AWS Region Europe (Paris). EC2 Hpc7a instances are powered by 4th generation AMD EPYC processors with up to 192 cores, and 300 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for fast and low-latency internode communications. Hpc7a instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in memory. Hpc7a instances are ideal for compute-intensive, tightly coupled, latency-sensitive high performance computing (HPC) workloads, such as computational fluid dynamics (CFD), weather forecasting, and multiphysics simulations, helping you scale more efficiently on fewer nodes. To optimize HPC instances networking for tightly coupled workloads, you can access these instances in a single Availability Zone within a Region. To learn more, see Amazon Hpc7a instances.

Starting today, Amazon EC2 Hpc6id instances are available in additional AWS Region Europe (Paris). These instances are optimized to efficiently run memory bandwidth-bound, data-intensive high performance computing (HPC) workloads, such as finite element analysis and seismic reservoir simulations. With EC2 Hpc6id instances, you can lower the cost of your HPC workloads while taking advantage of the elasticity and scalability of AWS. EC2 Hpc6id instances are powered by 64 cores of 3rd Generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.5 GHz, 1,024 GB of memory, and up to 15.2 TB of local NVMe solid state drive (SSD) storage. EC2 Hpc6id instances, built on the AWS Nitro System, offer 200 Gbps Elastic Fabric Adapter (EFA) networking for high-throughput inter-node communications that enable your HPC workloads to run at scale. The AWS Nitro System is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software. It delivers high performance, high availability, and high security while reducing virtualization overhead. To learn more about EC2 Hpc6id instances, see the product detail page.

Amazon Aurora PostgreSQL is now available as a quick create vector store in Amazon Bedrock Knowledge Bases. With the new Aurora quick create option, developers and data scientists building generative AI applications can select Aurora PostgreSQL as their vector store with one click to deploy an Aurora Serverless cluster preconfigured with pgvector in minutes. Aurora Serverless is an on-demand, autoscaling configuration where capacity is adjusted automatically based on application demand, making it ideal as a developer vector store. Knowledge Bases securely connects foundation models (FMs) running in Bedrock to your company data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, context-specific, and accurate responses that make your FM more knowledgeable about your business. To implement RAG, organizations must convert data into embeddings (vectors) and store these embeddings in a vector store for similarity search in generative artificial intelligence (AI) applications. Aurora PostgreSQL, with the pgvector extension, has been supported as a vector store in Knowledge Bases for existing Aurora databases. With the new quick create integration with Knowledge Bases, Aurora is now easier to set up as a vector store for use with Bedrock. The quick create option in Bedrock Knowledge Bases is available in these regions with the exception of AWS GovCloud (US-West) which is planned for Q4 2024. To learn more about RAG with Amazon Bedrock and Aurora, see Amazon Bedrock Knowledge Bases. Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started using Amazon Aurora PostgreSQL as a vector store for Amazon Bedrock Knowledge Bases, take a look at our documentation.

Amazon CloudWatch now offers centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. This enhanced visibility enables central DevOps teams, system administrators, and service teams to identify potential gaps in their infrastructure monitoring setup. The telemetry configuration auditing experience seamlessly integrates with AWS Config to discover AWS resources, and can be turned on for the entire organization using the new AWS Organizations integration with Amazon CloudWatch. With visibility into telemetry configurations, you can identify monitoring gaps that might have been missed in your current setup. For example, this helps you identify gaps in your EC2 detailed metrics so that you can address them and easily detect short-lived performance spikes and build responsive auto-scaling policies. You can audit telemetry configuration coverage at both resource type and individual resource levels, refining the view by filtering across specific accounts, resource types, or resource tags to focus on critical resources. The telemetry configurations auditing experience is available in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) regions. There is no additional cost to turn on the new experience, including for AWS Config. You can get started with auditing your telemetry configurations using the Amazon CloudWatch Console, by clicking on Telemetry config in the navigation panel, or programmatically using the API/CLI. To learn more, visit our documentation.

AWS Config added support for a service-linked recorder, a new type of AWS Config recorder that is managed by an AWS service and can record configuration data on service-specific resources, such as the new Amazon CloudWatch telemetry configurations audit. By enabling the service-linked recorder in Amazon CloudWatch, you gain centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. With service-linked recorders, an AWS service can deploy and manage an AWS Config recorder on your behalf to discover resources and utilize the configuration data to provide differentiated features. For example, an Amazon CloudWatch managed service-linked recorder helps you identify monitoring gaps within specific critical resources within your organization, providing a centralized, single-pane view of telemetry configuration status. Service-linked recorders are immutable to ensure consistency, prevention of configuration drift, and simplified experience. Service-linked recorders operate independently of any existing AWS Config recorder, if one is enabled. This allows you to independently manage your AWS Config recorder for your specific use cases while authorized AWS services can manage the service-linked recorder for feature specific requirements. Amazon CloudWatch managed service-linked recorder is now available in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney) Europe (Frankfurt), Europe (Ireland), Europe (Stockholm) regions. The AWS Config service-linked recorder specific to Amazon CloudWatch telemetry configuration feature is available to customers at no additional cost. To learn more, please refer to our documentation.  

Today, we are introducing the new ModelTrainer class and enhancing the ModelBuilder class in the SageMaker Python SDK. These updates streamline training workflows and simplify inference deployments. The ModelTrainer class enables customers to easily set up and customize distributed training strategies on Amazon SageMaker. This new feature accelerates model training times, optimizes resource utilization, and reduces costs through efficient parallel processing. Customers can smoothly transition their custom entry points and containers from a local environment to SageMaker, eliminating the need to manage infrastructure. ModelTrainer simplifies configuration by reducing parameters to just a few core variables and providing user-friendly classes for intuitive SageMaker service interactions. Additionally, with the enhanced ModelBuilder class, customers can now easily deploy HuggingFace models, switch between developing in local environment to SageMaker, and customize their inference using their pre- and post-processing scripts. Importantly, customers can now pass the trained model artifacts from ModelTrainer class easily to ModelBuilder class, enabling a seamlessly transition from training to inference on SageMaker. You can learn more about ModelTrainer class here, ModelBuilder enhancements here, and get started using ModelTrainer and ModelBuilder sample notebooks.

We are excited to announce two new capabilities in SageMaker Inference that significantly enhance the deployment and scaling of generative AI models: Container Caching and Fast Model Loader. These innovations address critical challenges in scaling large language models (LLMs) efficiently, enabling faster response times to traffic spikes and more cost-effective scaling. By reducing model loading times and accelerating autoscaling, these features allow customers to improve the responsiveness of their generative AI applications as demand fluctuates, particularly benefiting services with dynamic traffic patterns. Container Caching dramatically reduces the time required to scale generative AI models for inference by pre-caching container images. This eliminates the need to download them when scaling up, resulting in significant reduction in scaling time for generative AI model endpoints. Fast Model Loader streams model weights directly from Amazon S3 to the accelerator, loading models much faster compared to traditional methods. These capabilities allow customers to create more responsive auto-scaling policies, enabling SageMaker to add new instances or model copies quickly when defined thresholds are reached, thus maintaining optimal performance during traffic spikes while at the same time managing costs effectively. These new capabilities are accessible in all AWS regions where Amazon SageMaker Inference is available. To learn more see our documentation for detailed implementation guidance.  

Amazon RDS (Relational Database Service) Performance Insights expands the availability of its on-demand analysis experience to 15 new regions. This feature is available for Aurora MySQL, Aurora PostgreSQL, and RDS for PostgreSQL engines. This on-demand analysis experience, which was previously available in only 15 regions, is now available in all commercial regions. This feature allows you to analyze Performance Insights data for a time period of your choice. You can learn how the selected time period differs from normal, what went wrong, and get advice on corrective actions. Through simple-to-understand graphs and explanations, you can identify the chief contributors to performance issues. You will also get the guidance on the next steps to act on these issues. This can reduce the mean-time-to-diagnosis for database performance issues from hours to minutes. Amazon RDS Performance Insights is a database performance tuning and monitoring feature of RDS that allows you to visually assess the load on your database and determine when and where to take action. With one click in the Amazon RDS Management Console, you can add a fully-managed performance monitoring solution to your Amazon RDS database. To learn more about RDS Performance Insights, read the Amazon RDS User Guide and visit Performance Insights pricing for pricing details and region availability.  

I describe my journey here given this was my first aws certification exam. https://www.youtube.com/watch?v=yR7R1v6B-tA&t=413s&ab_channel=DaliCodes submitted by /u/DaliCodes [link] [comments]

submitted by /u/ProperDevelopment807 [link] [comments]

I got a 742 but a pass is a pass, right? I studied for this exam for just two weeks because I was enrolled in a 7 week course that required us to take it. The last three weeks were dedicated to studying for it (the first four were focused on something else entirely). I procrastinated and waited until the last two weeks and only used tutorials dojo exams. I did all the practice exams and never scored above 60%. I studied about 4 hours a day but there were a few days where I didn’t study at all. I think it helped that I already had the Cloud practitioner cert so I had an okay understanding of what the services were. The exam did feel a little more difficult than TD mostly because it was much easier to eliminate answer choices within TD questions. All this to say, don’t do what I did. The test was hard and I was convinced that I failed until I got the results 3 hours later. submitted by /u/Ducky_doo1 [link] [comments]

Just passed my exam. Yay! But what the hell? I did Stephane Maareks Udemy course and studied like crazy for a 2 days after finishing. I still felt like there were like 15 questions with answers I couldn’t have known from the course or even using ChatGPT and free practice exams. Like 3 really detailed questions about the pillars. A question about a service called nitro hypervisor that I’m pretty sure i had never even heard of before. I I am lucky I passed honestly. I was expecting the exam to be much easier. I must have just guessed correctly on enough of them to pass. I heard it was really easy but the CCP is no joke. R submitted by /u/reddithoggscripts [link] [comments]

Hi, I am at Reinvent 2024, and I see lots of people with lots of nice 'aws certified' bag tag, they seems to be well done, colourful with a metal ring ... Do you know where they come from ? There was a store for aws certified merch in the past at aws web, but doesn't exist anymore. Thanks. submitted by /u/CyrilDevOps [link] [comments]

Hi everyone, I haven't taken an exam since I took and passed the AWS Solutions Architect Professional about a year ago. I've been self-studying and decided to go for the AWS DevOps Professional. When I went to the portal to sign up, there's some kind of "Authorization" that's needed in order to register and sign up for the exam. Does anyone know what this is? How long does it take to get an authorization? The reason why I'm concerned is because I'm going out of the country next Wednesday and planned to take this exam on Monday or Tuesday. I will be gone for a prolonged period of time and hoped to have this under my belt before leaving. :-/ I'm editing this to add that I have the Cloud Practitioner, SAA, SAP, Sysops, and Developer already. Thanks! submitted by /u/koffeebrown [link] [comments]

I am scheduled to take the CCP exam on December 6 (which is tomorrow in my timezone). I will not be able to take it since I haven’t finished studying the course material I am watching. I wasn’t able to reschedule it days before, and now I couldn’t cancel or reschedule it (since it’s less than 24 hrs now). Anyone had the same experience before? Please help me out. submitted by /u/hanicinq [link] [comments]

I don’t work in AWS daily if even weekly. I’m an engineer on the front end Microsoft side for my company and our entire backend cloud is AWS. I know nothing about AWS. I studied and passed the CCP a few years ago and had to recertify as it was expiring. Took the Cloud Quest and it was stupid easy. Took me 2.5 days on and off and was done. If you need to renew your CCP, do it, it’s easy. submitted by /u/Illnasty2 [link] [comments]

I have received a voucher for a foundational exam, but I’m undecided between choosing the Cloud Practitioner or AI Practitioner certification. Which one should I pursue first? For context: I am a Computer Science student majoring in Data Science. I plan to work primarily in the Data Science and Machine Learning sectors. However, the challenge is that my country has very few entry-level job opportunities in these fields. As a result, I might need to work for 1–2 years as a Software Engineer, specifically in backend development, before transitioning to my desired role. submitted by /u/Shuvouwu [link] [comments]

So I’ve got free access to ACG/PluralSight. I’ve used it to pass my CCP and currently using it for SAA. I see a lot of negative options on it. Do you think using their material + practice exams I’ll be able to actually pass the real exam? Been prepping using ACG for a few months now… submitted by /u/Wessyvert [link] [comments]

Ive heard that a lot of people just memorize exam content but can't do hands on stuff in aws and that projects are the most important thing. Certifications also cover a broad range of topics at a surface level but don't make you an expert in any particular one. Why don't we just do projects and save ourselves the hassle of certs? submitted by /u/Holiday_Ad9679 [link] [comments]

I would really like to thank this community for the support: mainly answering my questions and calming me when in doubt. ☺️ Thank you to Stephen Maarek and Jon Bonso (TD) for the resources and practice exams. 🎉 As you can see in the screenshot, my scores were not high to boost my confidence but I was able to pass my actual exam (shaking and sometimes thinking about failing WHILE answering it, no joke). To all the passers, congratulations to us! ☺️ To those who are still studying, good luck!👍🏻 and dont give up ☺️ submitted by /u/Joi_trades [link] [comments]

I took two of Stephane Maarek's practice tests for SAA-C03 exam. I did not do Stephane's course but followed Mark Wilkin and Chad Smith's course in O'Reilly so I think I'm not biased by the content. However, I was only able to get about 64% in two sample tests I have taken. Would the real test be harder or easier in your experience? I have my test coming up in 2 days. Should I postpone? submitted by /u/geeky_vin [link] [comments]

Hi there. I want to tackle the Associate challenge and try to get certified by the end of the year with an Associate certification. I greatly prefer on-site to online because online has many pitfalls and problems (consider the many posts we get in this sub about online problems). However my on-site center of choice is very inconsistent in putting up dates and I have to constantly (daily/weekly) log into AWS Certifications to see if they have any spots available. Normally they only have 1-2 spots available, and only within the next 1-2 weeks (which makes planning ahead really hard, you know). Anyway, it's also December and Christmas is around the corner. I might want to book an on-site center, but if the day arrives and I'm not ready, I would like to reschedule. I'm afraid that with Christmas, my center might have zero dates available for me when I try to reschedule. Is it then possible to reschedule to an online option? And viceversa? Can I reschedule from online to on-site if I see that the center suddenly has an available date? An extra question: the Associate challenge implies that you will take the exam before the end of 2024. However I feel I might be cutting it tight in regards to preparation (been really busy). If I postpone into 2025 do I lose the discount? Or do I keep the discount? submitted by /u/GlosuuLang [link] [comments]

Hello everyone I am looking for some adv, 2 months ago i got my aws practitioner cert. i am currently studying Active Directory. I would like to be a sysadmin, but i've heard it would be best to go for SA and than Sysops administrator. please i need some great adv here. thank you in advance submitted by /u/Accurate_Evening_390 [link] [comments]

Today, AWS Partner Central announces the preview of Partner Connections, a new feature allowing AWS Partners to discover and connect with other Partners for collaboration on shared customer opportunities. With Partner Connections, Partners can co-sell joint solutions, accelerate deal progression, and expand their reach by teaming with other AWS Partners. At the core of Partner Connections are two key capabilities: connections discovery and multi-partner opportunities. The connections discovery feature uses AI-powered recommendations to streamline Partner matchmaking, making it easier for Partners to find suitable collaborators and add them to their network. With multi-partner opportunities, Partners can work together seamlessly to create and manage joint customer opportunities in APN Customer Engagements (ACE). This integrated approach allows Partners to work seamlessly with AWS and other Partners on shared opportunities, reducing the operational overhead of managing multi-partner opportunities. Partners can also create, update, and share multi-partner opportunities using the Partner Central API for Selling. This allows Partners to collaborate with other Partners and AWS on joint sales opportunities from their own customer relationship management (CRM) system. Partner Connections (Preview) is available to all eligible AWS Partners who have signed the ACE Terms and Conditions and have linked their AWS account to their Partner Central account. To get started, log in to AWS Partner Central and review the ACE user guide for more information. To see how Partner Connections works, read the blog.

Introducing a new AI Security category in the AWS Security competency to help customers easily identify AWS Partners with deep experience securing AI environments, and defending AI workloads against advanced threats and attacks. Partners in this new category are validated for their capabilities in areas like prevention of sensitive data disclosure, prevention of injection attacks, security posture management, implementing responsible AI filtering, and more. The rapid adoption of AI, and especially generative AI is transforming how customers build applications, but also introduces new security risks that require specialized expertise. Customers need solutions that can secure AI models, tools, datasets, and other deployment resources used in these applications. Unlock the power of AI, while keeping your AI applications, and data safe with validated partner solutions. Learn more about the AWS Security competency and explore validated partners with customer success in the new AI Security category.  

Today, AWS announces that Amazon Bedrock now supports prompt caching. Prompt caching is a new capability that can reduce costs by up to 90% and latency by up to 85% for supported models by caching frequently used prompts across multiple API calls. It allows you to cache repetitive inputs and avoid reprocessing context, such as long system prompts and common examples that help guide the model’s response. When cache is used, fewer computing resources are needed to generate output. As a result, not only can we process your request faster, but we can also pass along the cost savings from using fewer resources. Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI capabilities built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while providing tools to build customer trust and data governance. Prompt caching is now available on Claude 3.5 Haiku and Claude 3.5 Sonnet v2 in US West (Oregon) and US East (N. Virginia) via cross-region inference, and Nova Micro, Nova Lite, and Nova Pro models in US East (N. Virginia). At launch, only a select number of customers will have access to this feature. To learn more about participating in the preview, see this page. To learn more about prompt caching, see our documentation and blog.

Amazon announces a five-year commitment of cloud technology and technical support for organizations creating digital learning solutions that expand access for underserved learners worldwide through the AWS Education Equity Initiative. While the use of educational technologies continues to rise, many organizations lack access to cloud computing and AI resources needed to accelerate and scale their work to reach more learners in need. Amazon is committing up to $100 million in AWS credits and technical advising to support socially-minded organizations build and scale learning solutions that utilize cloud and AI technologies. This will help reduce initial financial barriers and provide guidance on building and scaling AI-powered education solutions using AWS technologies. Eligible recipients, including socially-minded edtechs, social enterprises, non-profits, governments, and corporate social responsibility teams, must demonstrate how their solution will benefit students from underserved communities. The initiative is now accepting applications. To learn more and how to apply, visit the AWS Education Equity Initiative page.

Amazon Kendra is an AI-powered search service enabling organizations to build intelligent search experiences and retrieval augmented generation (RAG) systems to power generative AI applications. Starting today, AWS customers can use a new index - the GenAI Index for RAG and intelligent search. With the Kendra GenAI Index, customers get high out-of-the-box search accuracy powered by the latest information retrieval technologies and semantic models. Kendra GenAI Index supports mobility across AWS generative AI services like Amazon Bedrock Knowledge Base and Amazon Q Business, giving customers the flexibility to use their indexed content across different use cases. It is available as a managed retriever in Bedrock Knowledge Bases, enabling customers to create a Knowledge Base powered by the Kendra GenAI Index. Customers can also integrate such Knowledge Bases with other Bedrock Services like Guardrails, Prompt Flows, and Agents to build advanced generative AI applications. The GenAI Index supports connectors for 43 different data sources, enabling customers to easily ingest content from a variety of sources. Kendra GenAI Index is available in the US East (N. Virginia) and US West (Oregon) regions. To learn more, see Kendra GenAI Index in the Amazon Kendra Developer Guide. For pricing, please refer to Kendra pricing page.

Today, we are announcing the support of GraphRAG, a new capability in Amazon Bedrock Knowledge Bases that enhances Generative AI applications by providing more comprehensive, relevant and explainable responses using RAG techniques combined with graph data. Amazon Bedrock Knowledge Bases offers fully-managed, end-to-end Retrieval-Augmented Generation (RAG) workflows to create highly accurate, low latency, and custom Generative AI applications by incorporating contextual information from your company's data sources. Amazon Bedrock Knowledge Bases now offers a fully-managed GraphRAG capability with Amazon Neptune Analytics. Previously, customers faced challenges in conducting exhaustive, multi-step searches across disparate content. By identifying key entities across documents, GraphRAG delivers insights that leverage relationships within the data, enabling improved responses to end users. For example, users can ask a travel application for family-friendly beach destinations with direct flights and good seafood restaurants. Developers building Generative AI applications can enable GraphRAG in just a few clicks by specifying their data sources and choosing Amazon Neptune Analytics as their vector store when creating a knowledge base. This will automatically generate and store vector embeddings in Amazon Neptune Analytics, along with a graph representation of entities and their relationships. GraphRAG with Amazon Neptune is built right into Amazon Bedrock Knowledge Bases, offering an integrated experience with no additional setup or additional charges beyond the underlying services. GraphRAG is available in AWS Regions where Amazon Bedrock Knowledge Bases and Amazon Neptune Analytics are both available (see current list of supported regions). To learn more, visit the Amazon Bedrock User Guide.

Amazon Bedrock Knowledge Bases now enables developers to build generative AI applications that can analyze and leverage insights from both textual and visual data, such as images, charts, diagrams, and tables. Bedrock Knowledge Bases offers end-to-end managed Retrieval-Augmented Generation (RAG) workflow that enables customers to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual information from their own data sources. With this launch, Bedrock Knowledge Bases extracts content from both text and visual data, generates semantic embeddings using the selected embedding model, and stores them in the chosen vector store. This enables users to retrieve and generate answers to questions derived not only from text but also from visual data. Additionally, retrieved results now include source attribution for visual data, enhancing transparency and building trust in the generated outputs. To get started, customers can choose between: Amazon Bedrock Data Automation, a managed service that automatically extracts content from multimodal data (currently in Preview), or FMs such as Claude 3.5 Sonnet or Claude 3 Haiku, with the flexibility to customize the default prompt. Multimodal data processing with Bedrock Data Automation is available in the US West (Oregon) region in preview. FM-based parsing is supported in all regions where Bedrock Knowledge Bases is available. For details on pricing for using Bedrock Data Automation or FM as a parser, please refer to the pricing page. To learn more, visit Amazon Bedrock Knowledge Bases product documentation.

Amazon SageMaker HyperPod now provides you with centralized governance across all generative AI development tasks, such as training and inference. You have full visibility and control over compute resource allocation, ensuring the most critical tasks are prioritized and maximizing compute resource utilization, reducing model development costs by up to 40%. With HyperPod task governance, administrators can more easily define priorities for different tasks and set up limits for how many compute resources each team can use. At any given time, administrators can also monitor and audit the tasks that are running or waiting for compute resources through a visual dashboard. When data scientists create their tasks, HyperPod automatically runs them, adhering to the defined compute resource limits and priorities. For example, when training for a high-priority model needs to be completed as soon as possible but all compute resources are in use, HyperPod frees up resources from lower-priority tasks to support the training. HyperPod pauses the low-priority task, saves the checkpoint, and reallocates the freed-up compute resources. The preempted low-priority task will resume from the last saved checkpoint as resources become available again. And when a team is not fully using the resource limits the administrator has set up, HyperPod use those idle resources to accelerate another team’s tasks. Additionally, HyperPod is now integrated with Amazon SageMaker Studio, bringing task governance and other HyperPod capabilities into the Studio environment. Data scientists can now seamlessly interact with HyperPod clusters directly from Studio, allowing them to develop, submit, and monitor machine learning (ML) jobs on powerful accelerator-backed clusters. Task governance for HyperPod is available in all AWS Regions where HyperPod is available: US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and South America (São Paulo). To learn more, visit SageMaker HyperPod webpage, AWS News Blog, and SageMaker AI documentation.

Amazon SageMaker HyperPod recipes help you get started training and fine-tuning publicly available foundation models (FMs) in minutes with state-of-the-art performance. SageMaker HyperPod helps customers scale generative AI model development across hundreds or thousands of AI accelerators with built-in resiliency and performance optimizations, decreasing model training time by up to 40%. However, as FM sizes continue to grow to hundreds of billions of parameters, the process of customizing these models can take weeks of extensive experimenting and debugging. In addition, performing training optimizations to unlock better price performance is often unfeasible for customers, as they often require deep machine learning expertise that could cause further delays in time to market.  With SageMaker HyperPod recipes, customers of all skill sets can benefit from state-of-the-art performance while quickly getting started training and fine-tuning popular publicly available FMs, including Llama 3.1 405B, Mixtral 8x22B, and Mistral 7B. SageMaker HyperPod recipes include a training stack tested by AWS, removing weeks of tedious work experimenting with different model configurations. You can also quickly switch between GPU-based and AWS Trainium-based instances with a one-line recipe change and enable automated model checkpointing for improved training resiliency. Finally, you can run workloads in production on the SageMaker AI training service of your choice.  SageMaker HyperPod recipes are available in all AWS Regions where SageMaker HyperPod and SageMaker training jobs are supported. To learn more and get started, visit the SageMaker HyperPod page and blog.

Amazon Bedrock Marketplace provides generative AI developers access to over 100 publicly available and proprietary foundation models (FMs), in addition to Amazon Bedrock’s industry-leading, serverless models. Customers deploy these models onto SageMaker endpoints where they can select their desired number of instances and instance types. Amazon Bedrock Marketplace models can be accessed through Bedrock’s unified APIs, and models which are compatible with Bedrock’s Converse APIs can be used with Amazon Bedrock’s tools such as Agents, Knowledge Bases, and Guardrails. Amazon Bedrock Marketplace empowers generative AI developers to rapidly test and incorporate a diverse array of emerging, popular, and leading FMs of various types and sizes. Customers can choose from a variety of models tailored to their unique requirements, which can help accelerate the time-to-market, improve the accuracy, or reduce the cost of their generative AI workflows. For example, customers can incorporate models highly-specialized for finance or healthcare, or language translation models for Asian languages, all from a single place. Amazon Bedrock Marketplace is supported in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and South America (São Paulo). For more information, please refer to Amazon Bedrock Marketplace's announcement blog or documentation.

Amazon Bedrock Knowledge Bases now supports natural language querying to retrieve structured data from your data sources. With this launch, Bedrock Knowledge Bases offers an end-to-end managed workflow for customers to build custom generative AI applications that can access and incorporate contextual information from a variety of structured and unstructured data sources. Using advanced natural language processing, Bedrock Knowledge Bases can transform natural language queries into SQL queries, allowing users to retrieve data directly from the source without the need to move or preprocess the data. Developers often face challenges integrating structured data into generative AI applications. This includes difficulties training large language models (LLMs) to convert natural language queries to SQL queries based on complex database schemas, as well as ensuring appropriate data governance and security controls are in place. Bedrock Knowledge Bases eliminates these hurdles by providing a managed natural language to SQL (NL2SQL) module. A retail analyst can now simply ask "What were my top 5 selling products last month?", and then Bedrock Knowledge Base automatically translates that query into SQL, execute the query against the database, and return the results - or even provide a summarized narrative response. To generate accurate SQL queries, Bedrock Knowledge Base leverages database schema, previous query history, and other contextual information that are provided about the data sources. Bedrock Knowledge Bases supports structured data retrieval from Amazon Redshift and Amazon Sagemaker Lakehouse at this time and is available in all commercial regions where Bedrock Knowledge Bases is supported. To learn more, visit here and here. For details on pricing, please refer here.

Amazon SageMaker HyperPod announces flexible training plans, a new capability that allows you to train generative AI models within your timelines and budgets. Gain predictable model training timelines and run training workloads within your budget requirements, while continuing to benefit from features of SageMaker HyperPod such as resiliency, performance-optimized distributed training, and enhanced observability and monitoring.  In a few quick steps, you can specify your preferred compute instances, desired amount of compute resources, duration of your workload, and preferred start date for your generative AI model training. SageMaker then helps you create the most cost-efficient training plans, reducing time to train your model by weeks. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the training workloads on these compute resources without requiring any manual intervention. SageMaker also automatically takes care of pausing and resuming training between gaps in compute availability, as the plan switches from one capacity block to another. If you wish to remove all the heavy lifting of infrastructure management, you can also create and run training plans using SageMaker fully managed training jobs.   SageMaker HyperPod flexible training plans are available in the US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS Regions. To learn more, visit: SageMaker HyperPod, documentation, and the announcement blog. 

Today, AWS Security Incident Response launches a new AWS Specialization with approved partners from the AWS Partner Network (APN). AWS customers today rely on various 3rd party tools and services to support their internal security incident response capabilities. To better help both customers and partners, AWS introduced AWS Security Incident Response, a new service that helps customers prepare for, respond to, and recover from security events. Alongside approved AWS Partners, AWS Security Incident Response monitors, investigates, and escalates triaged security findings from Amazon GuardDuty and other threat detection tools through AWS Security Hub. Security Incident Response identifies and escalates only high-priority incidents. Partners and customers can also leverage collaboration and communication features to streamline coordinated incident response for faster reaction and recovery. For example, service members can create  a predefined "Incident Response Team" that is automatically alerted whenever a security case is escalated. Alerted members, which includes customers and partners, can then communicate and collaborate in a centralized format, with native feature integrations such as in-console messaging, video conferencing, and quick and secure data transfer.  Customers can access the service alongside AWS Partners that have been vetted and approved to use Security Incident Response. Learn more and explore AWS Security Incident Response Partners with specialized expertise to help you respond when it matters most.

Starting today, you can build ML models using natural language with Amazon Q Developer, now available in Amazon SageMaker Canvas in preview. You can now get generative AI-powered assistance through the ML lifecycle, from data preparation to model deployment. With Amazon Q Developer, users of all skill levels can use natural language to access expert guidance to build high-quality ML models, accelerating innovation and time to market. Amazon Q Developer will break down your objective into specific ML tasks, define the appropriate ML problem type, and apply data preparation techniques to your data. Amazon Q Developer then guides you through the process of building, evaluating, and deploying custom ML models. ML models produced in SageMaker Canvas with Amazon Q Developer are production ready, can be registered in SageMaker Studio, and the code can be shared with data scientists for integration into downstream MLOps workflows. Amazon Q Developer is available in SageMaker Canvas in preview in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Paris), Asia Pacific (Tokyo), and Asia Pacific (Seoul). To learn more about using Amazon Q Developer with SageMaker Canvas, visit the website, read the AWS News blog, or view the technical documentation.

Today, AWS Marketplace announces Buy with AWS, a new feature that helps accelerate discovery and procurement on AWS Partners’ websites for products available in AWS Marketplace. Partners that sell or resell products in AWS Marketplace can now offer new experiences on their websites that are powered by AWS Marketplace. Customers can more quickly identify solutions from Partners that are available in AWS Marketplace and use their AWS accounts to access a streamlined purchasing experience. Customers browsing on Partner websites can explore products that are “Available in AWS Marketplace” and request demos, access free trials, and request custom pricing. Customers can conveniently and securely make purchases by clicking the Buy with AWS button and completing transactions by logging in to their AWS accounts. All purchases made through Buy with AWS are transacted and managed within AWS Marketplace, allowing customers to take advantage of benefits such as consolidated AWS billing, centralized subscriptions management, and access to cost optimization tools. For AWS Partners, Buy with AWS provides a new way to engage website visitors and accelerate the path-to-purchase for customers. By adding Buy with AWS buttons to Partner websites, Partners can give website visitors the ability to subscribe to free trials, make purchases, and access custom pricing using their AWS accounts. Partners can complete an optional integration and build new experiences on websites that allow customers to search curated product listings and filter products from the AWS Marketplace catalog. Learn more about making purchases using Buy with AWS. Learn how AWS Partners can start selling using Buy with AWS.

Today, we are announcing the preview launch of Amazon Bedrock Data Automation (BDA), a new feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. These insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. Developers can also customize BDA’s output to generate specific insights in consistent formats required by their systems and applications. By leveraging BDA, developers can reduce development time and effort, making it easier to build intelligent document processing, media analysis, and other multimodal data-centric automation solutions. BDA offers high accuracy at lower cost than alternative solutions, along with features such as visual grounding with confidence scores for explainability and built-in hallucination mitigation. This ensures accurate insights from unstructured, multi-modal data content. Developers can get started with BDA on the Bedrock console, where they can configure and customize output using their sample data. They can then integrate BDA’s unified multi-modal inference API into their applications to process their unstructured content at scale with high accuracy and consistency. BDA is also integrated with Bedrock Knowledge Bases, making it easier for developers to generate meaningful information from their unstructured multi-modal content to provide more relevant responses for retrieval augmented generation (RAG). Bedrock Data Automation is available in preview in US West (Oregon) AWS Region. To learn more, visit the Bedrock Data Automation page.

Today Amazon Web Services, Inc. (AWS) announced the general availability of Amazon SageMaker partner AI apps, a new capability that enables customers to easily discover, deploy, and use best-in-class machine learning (ML) and generative AI (GenAI) development applications from leading app providers privately and securely, all without leaving Amazon SageMaker AI so they can develop performant AI models faster. Until today, integrating purpose-built GenAI and ML development applications that provide specialized capabilities for a variety of model development tasks, required a considerable amount of effort. Beyond the need to invest time and effort in due diligence to evaluate existing offerings, customers had to perform undifferentiated heavy lifting in deploying, managing, upgrading and scaling these applications. Furthermore, to adhere to rigorous security and compliance protocols, organizations need their data to stay within the confines of their security boundaries without needing to move their data elsewhere, for example, to a Software as a Service (SaaS) application. Finally, the resulting developer experience is often fragmented, with developers having to switch back and forth between multiple disjointed interfaces. With SageMaker partner AI apps you can quickly subscribe to a partner solution and seamlessly integrate the app with your SageMaker development environment. SageMaker partner AI apps are fully managed and run privately and securely in your SageMaker environment reducing the risk of data and model exfiltration. At launch, you will be able to boost your team’s productivity and reduce time to market by enabling: Comet, to track, visualize, and manage experiments for AI model development; Deepchecks, to evaluate quality and compliance for AI models; Fiddler, to validate, monitor, analyze, and improve AI models in production; and, Lakera, to protect AI applications from security threats such as prompt attacks, data loss and inappropriate content. SageMaker partner AI apps is available in all currently supported regions except Gov Cloud. To learn more please visit SageMaker partner AI app’s developer guide.  

A new scenario analysis capability of Amazon Q in QuickSight is now available in preview. This new capability provides an AI-assisted data analysis experience that helps you make better decisions, faster. Amazon Q in QuickSight simplifies in-depth analysis with step-by-step guidance, saving hours of manual data manipulation and unlocking data-driven decision-making across your organization. Amazon Q in QuickSight helps business users perform complex scenario analysis up to 10x faster than spreadsheets. You can ask a question or state your goal in natural language and Amazon Q in QuickSight guides you through every step of advanced data analysis—suggesting analytical approaches, automatically analyzing data, surfacing relevant insights, and summarizing findings with suggested actions. This agentic approach breaks down data analysis into a series of easy-to-understand, executable steps, helping you find solutions to complex problems without specialized skills or tedious, error-prone data manipulation in spreadsheets. Working on an expansive analysis canvas, you can intuitively iterate your way to solutions by directly interacting with data, refining analysis steps, or exploring multiple analysis paths side-by-side. This scenario analysis capability is accessible from any Amazon QuickSight dashboard, so you can move seamlessly from visualizing data to modelling solutions. With Amazon Q in QuickSight, you can easily modify, extend, and reuse previous analyses, helping you quickly adapt to changing business needs. Amazon Q in QuickSight Pro users can use this new capability in preview in the following AWS regions: US East (N. Virginia) and US West (Oregon). To learn more, visit the Amazon Q in QuickSight documentation and read the AWS News Blog.

We are excited to announce the new Amazon Security Lake Ready Specialization, which recognizes AWS Partners who have technically validated their software solutions to integrate with Amazon Security Lake and demonstrated successful customer deployments. These solutions have been technically validated by AWS Partner Solutions Architects for their sound architecture and proven customer success. Security Lake Ready software solutions can either contribute data to the Security Lake or consume this data and provide analytics, delivering a cohesive security solution for AWS customers. Amazon Security Lake automates data management tasks for customers, reducing costs and consolidating security data that customers own. It uses the Open Cybersecurity Schema Framework (OCSF), an open standard that helps customers address the challenges of data normalization and schema mapping across multiple log sources. With Amazon Security Lake Ready software solutions, customers now have a single place with verified partner solutions where security data can be stored in an open-source format, ready for identifying potential threats and vulnerabilities, and for security investigations and analytics. Explore Amazon Security Lake Ready software solutions that can help your organization improve the protection of workloads, applications, and data by significantly reducing the operational overhead of managing security data. To learn more about how to become an Amazon Security Lake Ready Partner, visit the AWS Service Ready Program webpage.  

Digital sovereignty has been a priority for AWS since its inception. AWS remains committed to offering customers the most advanced sovereignty controls and features in the cloud. With the increasing importance of digital sovereignty for public sector organizations and regulated industries, AWS is excited to announce the launch of the AWS Digital Sovereignty Competency. The AWS Digital Sovereignty Competency curates and validates a community of AWS Partners with advanced sovereignty capabilities and solutions, including deep experience in helping customers address sovereignty and compliance requirements. These partners can assist customers with residency control, access control, resilience, survivability, and self-sufficiency. Through this competency, customers can search for and engage with trusted local and global AWS Partners that have technically validated experience in addressing customers’ sovereignty requirements. Many partners have built sovereign solutions that leverage AWS innovations and built-in controls and security features. In addition to these offerings, AWS Digital Sovereignty Partners provide skills and knowledge of local compliance requirements and regulations, making it easier for customers to meet their digital sovereignty requirements while benefiting from the performance, agility, security, and scale of the AWS Cloud.

Amazon Bedrock Intelligent Prompt Routing routes prompts to different foundational models within a model family, helping you optimize for quality of responses and cost. Using advanced prompt matching and model understanding techniques, Intelligent Prompt Routing predicts the performance of each model for each request and dynamically routes each request to the model that it predicts is most likely to give the desired response at the lowest cost. Customers can choose from two prompt routers in preview that route requests either between Claude Sonnet 3.5 and Claude Haiku, or between Llama 3.1 8B and Llama 3.1 70B. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI capabilities built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance. With Intelligent Prompt Routing, Amazon Bedrock can help customers build cost-effective generative AI applications with a combination of foundation models to get better performance at lower cost than a single foundation model. During preview, customers are charged regular on-demand pricing for the models that requests are routed to. Learn more in our documentation and blog.

Organizations are increasingly using applications with multimodal data to drive business value, improve decision-making, and enhance customer experiences. Amazon Bedrock Guardrails now supports multimodal toxicity detection for image content, enabling organizations to apply content filters to images. This new capability with Guardrails, now in public preview, removes the heavy lifting required by customers to build their own safeguards for image data or spend cycles with manual evaluation that can be error-prone and tedious. Bedrock Guardrails helps customers build and scale their generative AI applications responsibly for a wide range of use cases across industry verticals including healthcare, manufacturing, financial services, media and advertising, transportation, marketing, education, and much more. With this new capability, Amazon Bedrock Guardrails offers a comprehensive solution, enabling the detection and filtration of undesirable and potentially harmful image content while retaining safe and relevant visuals. Customers can now use content filters for both text and image data in a single solution with configurable thresholds to detect and filter undesirable content across categories such as hate, insults, sexual, and violence, and build generative AI applications based on their responsible AI policies. This new capability in preview is available with all foundation models (FMs) on Amazon Bedrock that support images including fine-tuned FMs in 11 AWS regions globally: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo), and Asia Pacific (Mumbai), and AWS GovCloud (US-West). To learn more, visit the Amazon Bedrock Guardrails product page, read the News blog, and documentation.

Today, AWS announces the availability of new AWS AI Service Cards for Amazon Nova Reel; Amazon Canvas; Amazon Nova Micro, Lite, and Pro; Amazon Titan Image Generator; and Amazon Titan Text Embeddings. AI Service Cards are a resource designed to enhance transparency by providing customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and performance optimization best practices for AWS AI services. AWS AI Service Cards are part of our comprehensive development process to build services in a responsible way. They focus on key aspects of AI development and deployment, including fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. By offering these cards, AWS aims to empower customers with the knowledge they need to make informed decisions about using AI services in their applications and workflows. Our AI Service Cards will continue to evolve and expand as we engage with our customers and the broader community to gather feedback and continually iterate on our approach. For more information, see the AI Service Cards for Amazon Nova Reel  Amazon Nova Canvas  Amazon Nova Micro, Lite and Pro  Amazon Titan Image Generator  Amazon Titan Text Embeddings  To learn more about AI Service Cards, as well as our broader approach to building AI in a responsible way, see our Responsible AI webpage.

Hi all, I just finished the AWS Cloud Practitioner Recertification, now that I claimed to extend my certification for 3 more years, is there anything else that needs to be done on my end? Do I also get the Cloud Quest Badge, if so when do my credentials get updated? Also feel free to ask me about the experience of re-certifiying using Cloud Quest. submitted by /u/mtot961 [link] [comments]