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Exam Number : SOA-C02
Exam Name : AWS Certified SysOps Administrator - Associate (SOA-C02)
Vendor Name : Amazon
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SOA-C02 test Format | SOA-C02 Course Contents | SOA-C02 Course Outline | SOA-C02 test Syllabus | SOA-C02 test Objectives

The AWS Certified SysOps Administrator – Associate (SOA-C02) test is intended for system administrators in a cloud operations role. The test validates a candidates ability to deploy, manage, and operate workloads on AWS.

- Support and maintain AWS workloads according to the AWS Well-Architected Framework

- Perform operations by using the AWS Management Console and the AWS CLI

- Implement security controls to meet compliance requirements

- Monitor, log, and troubleshoot systems

- Apply networking concepts (for example, DNS, TCP/IP, firewalls)

- Implement architectural requirements (for example, high availability, performance, capacity)

- Perform business continuity and disaster recovery procedures

- Identify, classify, and remediate incidents

Domains of exam;

Domain 1: Monitoring, Logging, and Remediation

Domain 2: Reliability and Business Continuity

Domain 3: Deployment, Provisioning, and Automation

Domain 4: Security and Compliance

Domain 5: Networking and Content Delivery

Domain 6: Cost and Performance Optimization

Domain 1: Monitoring, Logging, and Remediation

1.1 Implement metrics, alarms, and filters by using AWS monitoring and logging services

 Identify, collect, analyze, and export logs (for example, Amazon CloudWatch Logs, CloudWatch Logs Insights, AWS CloudTrail logs)

 Collect metrics and logs using the CloudWatch agent

 Create CloudWatch alarms

 Create metric filters

 Create CloudWatch dashboards

 Configure notifications (for example, Amazon Simple Notification Service [Amazon SNS], Service Quotas, CloudWatch alarms, AWS Health events)

1.2 Remediate issues based on monitoring and availability metrics

 Troubleshoot or take corrective actions based on notifications and alarms

 Configure Amazon EventBridge rules to trigger actions

 Use AWS Systems Manager Automation documents to take action based on AWS Config rules

Domain 2: Reliability and Business Continuity

2.1 Implement scalability and elasticity

 Create and maintain AWS Auto Scaling plans

 Implement caching

 Implement Amazon RDS replicas and Amazon Aurora Replicas

 Implement loosely coupled architectures

 Differentiate between horizontal scaling and vertical scaling

2.2 Implement high availability and resilient environments

 Configure Elastic Load Balancer and Amazon Route 53 health checks

 Differentiate between the use of a single Availability Zone and Multi-AZ deployments (for example, Amazon EC2 Auto Scaling groups, Elastic Load Balancing, Amazon FSx, Amazon RDS)

 Implement fault-tolerant workloads (for example, Amazon Elastic File System [Amazon EFS],Elastic IP addresses)

 Implement Route 53 routing policies (for example, failover, weighted, latency based)

2.3 Implement backup and restore strategies

 Automate snapshots and backups based on use cases (for example, RDS snapshots, AWS Backup, RTO and RPO, Amazon Data Lifecycle Manager, retention policy)

 Restore databases (for example, point-in-time restore, promote read replica)

 Implement versioning and lifecycle rules

 Configure Amazon S3 Cross-Region Replication

 Execute disaster recovery procedures

Domain 3: Deployment, Provisioning, and Automation

3.1 Provision and maintain cloud resources

 Create and manage AMIs (for example, EC2 Image Builder)

 Create, manage, and troubleshoot AWS CloudFormation

 Provision resources across multiple AWS Regions and accounts (for example, AWS Resource Access Manager, CloudFormation StackSets, IAM cross-account roles)

 Select deployment scenarios and services (for example, blue/green, rolling, canary)

 Identify and remediate deployment issues (for example, service quotas, subnet sizing, CloudFormation and AWS OpsWorks errors, permissions)

3.2 Automate manual or repeatable processes

 Use AWS services (for example, OpsWorks, Systems Manager, CloudFormation) to automate deployment processes

 Implement automated patch management

 Schedule automated tasks by using AWS services (for example, EventBridge, AWS Config)

Domain 4: Security and Compliance

4.1 Implement and manage security and compliance policies

 Implement IAM features (for example, password policies, MFA, roles, SAML, federated identity, resource policies, policy conditions)

 Troubleshoot and audit access issues by using AWS services (for example, CloudTrail, IAM Access Analyzer, IAM policy simulator)

 Validate service control policies and permissions boundaries

 Review AWS Trusted Advisor security checks

 Validate AWS Region and service selections based on compliance requirements

 Implement secure multi-account strategies (for example, AWS Control Tower, AWS Organizations)

4.2 Implement data and infrastructure protection strategies

 Enforce a data classification scheme

 Create, manage, and protect encryption keys

 Implement encryption at rest (for example, AWS Key Management Service [AWS KMS])

 Implement encryption in transit (for example, AWS Certificate Manager, VPN)

 Securely store secrets by using AWS services (for example, AWS Secrets Manager, Systems Manager Parameter Store)

 Review reports or findings (for example, AWS Security Hub, Amazon GuardDuty, AWS Config, Amazon Inspector)

Domain 5: Networking and Content Delivery

5.1 Implement networking features and connectivity

 Configure a VPC (for example, subnets, route tables, network ACLs, security groups, NAT gateway, internet gateway)
 Configure private connectivity (for example, Systems Manager Session Manager, VPC endpoints, VPC peering, VPN)
 Configure AWS network protection services (for example, AWS WAF, AWS Shield)
5.2 Configure domains, DNS services, and content delivery
 Configure Route 53 hosted zones and records
 Implement Route 53 routing policies (for example, geolocation, geoproximity)
 Configure DNS (for example, Route 53 Resolver)

 Configure Amazon CloudFront and S3 origin access identity (OAI)

 Configure S3 static website hosting

5.3 Troubleshoot network connectivity issues

 Interpret VPC configurations (for example, subnets, route tables, network ACLs, security groups)

 Collect and interpret logs (for example, VPC Flow Logs, Elastic Load Balancer access logs, AWS WAF web ACL logs, CloudFront logs)

 Identify and remediate CloudFront caching issues

 Troubleshoot hybrid and private connectivity issues

Domain 6: Cost and Performance Optimization

6.1 Implement cost optimization strategies

 Implement cost allocation tags

 Identify and remediate underutilized or unused resources by using AWS services and tools (for example, Trusted Advisor, AWS Compute Optimizer, Cost Explorer)

 Configure AWS Budgets and billing alarms

 Assess resource usage patterns to qualify workloads for EC2 Spot Instances

 Identify opportunities to use managed services (for example, Amazon RDS, AWS Fargate, EFS)

6.2 Implement performance optimization strategies

 Recommend compute resources based on performance metrics

 Monitor Amazon EBS metrics and modify configuration to increase performance efficiency

 Implement S3 performance features (for example, S3 Transfer Acceleration, multipart uploads)

 Monitor RDS metrics and modify the configuration to increase performance efficiency (for example, Performance Insights, RDS Proxy)

 Enable enhanced EC2 capabilities (for example, enhanced network adapter, instance store, placement groups)

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AWS HealthScribe Uses Generative AI And Real-Time Doctor-Patient Conversations To Update Medical Charts


Generative AI (GenAI) has demonstrated profound usefulness in almost every industry segment. Amazon Web Services recently announced several AI-driven products, which I will cover in future articles. In this article, I focus on an AWS application called HealthScribe, a HIPAA-eligible service that combines speech recognition and generative AI. AWS is now releasing HeathScribe as a preview product in the eastern U.S., and there is no information yet on its general availability.

HealthScribe’s target market is large healthcare organizations and related software vendors. It automatically creates preliminary clinical notes using natural language processing and generative AI to analyze conversations between patients and clinicians.

HealthScribe can Boost the quality, efficiency and accuracy of medical charting, a tedious and predominantly manual process that documents detailed information about each patient visit. Charting is an unpopular and time-consuming task in medical practices, in part because it reduces face-to-face time with patients. Nevertheless, it is essential for capturing historical patient interactions and administering quality programs and insurance billing.

Benefits of HealthScribe for medical charting

An illustrative example of the application experience that healthcare developers can provide users ... [+] with AWS HealthScribe.


HealthScribe uses generative AI techniques to manage and transform healthcare data. Considering HealthScribe’s potential to replace a massive number of manual charting hours across the industry, it could be one of the most promising and useful medical applications this year for various healthcare providers, payers, IT vendors and software developers. Its use in medical practices could enhance data-driven decision making, increase collaboration, reduce administrative labor and decrease the cost of care.

The graphic above shows how HealthScribe summarizes clinical notes from a conversation between a patient and a clinician. It also separates summaries into sections such as chief complaint, history of present illness, test and treatment plan.

A helpful feature is its ability to extract specific medical terms, medical conditions, medications and treatments from the conversation. Medical terms that suggest studying Topics can be included in a drop-down form for custom applications if desired.

The future of HealthScribe

Large medical organizations with thousands of medical practitioners will see significant productivity gains by using HealthScribe. Healthcare software vendors such as 3M, ScribeEMR and Babylon are already putting AWS HealthScribe to use in their clinical applications.

  • 3M Health Information Systems plans to leverage HealthScribe in clinical applications where more than 300,000 clinicians will use it. The company is positioning HealthScribe as a foundational technology to expedite, refine and scale the delivery of 3M’s ambient clinical documentation and virtual assistant solutions.
  • ScribeEMR is a leading provider of virtual medical scribing, virtual medical coding and virtual medical office services for hundreds of medical practices, hospitals and health systems. It wants to use AWS HealthScribe in healthcare documentation by capturing and interpreting patient visits more effectively, thereby optimizing EMR workflows, coding and reimbursement processes.
  • As interest in GenAI grows, software vendors that build high-performance applications for clinical settings—things like telehealth applications or centralized electronic health record (EHR) platforms—are considering how best to leverage HealthScribe. Integrating HealthScribe into clinical software will also help software vendors use AI responsibly. HealthScribe references each original patient transcript to its AI-generated sentence in the clinical notes, making it easier for users to validate the accuracy of summaries.

    Wrapping up

    AWS HealthScribe can potentially reduce the number of human hours associated with charting patients’ visits. Besides saving the healthcare industry a staggering amount of time in charting, its automated features promise to streamline patient care and Boost clinical workflows for medical practitioners.

    As technology advances and more healthcare software vendors integrate HealthScribe into packaged solutions, it should have a positive impact on individual medical practices and the healthcare system as a whole. Tedious documentation tasks will be minimized and physicians will be able to focus their attention where it matters most—caring for patients.

    Here are the overall benefits of HealthScribe as I see them:

  • Reduces time spent on clinical documentation, thereby freeing up more time with patients
  • Improves accuracy and readability of clinical notes
  • Simplifies review of patient histories via summary sections
  • Automatically provides sources for medical terms and conditions
  • Seamlessly integrates into EHR systems and clinical workflows
  • Enables development of customized voice-based clinical apps using its API
  • Lowers costs by reducing charting errors
  • Reduces time to extract coding and billing information
  • Provides reference transcripts to validate AI-generated notes
  • Frees up time for complex patient needs
  • Improves ability to collect data analytics
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    AWS’ head of health AI on Amazon’s new clinical documentation play

    Amazon Web Services generated significant buzz earlier this week when it announced HealthScribe, its own clinical documentation tool that uses generative artificial intelligence to summarize doctor-patient visits.

    With the launch, Amazon joined a number of other tech companies including Microsoft in the space, jockeying for a share of the market as providers look for ways to cut documentation burden on physicians.

    In an interview on the sidelines of the AWS Summit on Wednesday, AWS Head of Health AI Tehsin Syed told Healthcare Dive what differentiates HealthScribe from other medical notetakers, how the technology is expected to drive client demand and how Amazon is approaching pernicious accuracy, ethics and liability issues around generative AI in healthcare.

    Editor’s note: This interview has been edited for clarity and brevity.

    HEALTHCARE DIVE: Explain HealthScribe — how does the service work?

    TEHSIN SYED: HealthScribe transcribes the conversation between a doctor and a patient. It identifies the speakers and segments the transcript. If you start with some chitchat, it’ll recognize, okay, this is not relevant to the clinical note. It extracts specific medical information — things like medications, tests and references to anatomy — to take those as structured elements. This is where the generative AI capability comes in. That information, and the large language model we’ve trained, is used to generate summary documentation, in the shape of chief complaint, history of present illness, test and plan.

    So they take a Titan model — that’s an Amazon first-party model — and they do what’s called grounding. They adapt the model to this specific purpose of learning how to generate this kind of documentation.

    Finally, they show the end user the transcript and the summary, and cite conclusions in the summary back to the dialogue. So it’s a really natural and easy review process for clinicians.

    What differentiates HealthScribe from other clinical documentation tools, like Suki or Microsoft-owned Nuance, that also use generative AI?

    SYED: HealthScribe is an API that Amazon is selling to its partners for them to provide an end user experience. That’s a very different business model. We’re saying, you don’t have to have all the machine learning expertise and all of the infrastructure that’s needed to manage a large language model.

    AWS head of health AI Tehsin Syed

    Permission granted by AWS

    AWS is going to do that part of the work, and maintain the security and privacy of the data. They provide this capability to anyone who wants to use it, and they have public pricing based on usage, so it’s clear what’s going on.

    What we’re trying to do is say, whoever you are, whatever software builder you are, you get to market much faster with this capability. You can concentrate on the end user experience.

    There’s also innovation in the technology itself. Some of the grounding techniques are patent-pending. Underlying HealthScribe itself — at a high level everyone’s using an LLM and whatever — but part of this is the expertise that they bring.

    3M, Babylon and ScribeEMR have already said they plan to integrate HealthScribe. How is AWS forecasting client demand moving forward?

    SYED: There’s a number of others who are interested. I absolutely anticipate new customers coming to the table who previously didn’t have the ability to do this [work] themselves. We’ve seen a lot of interest from software vendors, because we’re providing an API and you do have to have some expertise to build. When they talk to provider systems, they’re interested, and they usually ask who’s a good partner, and how do they work with them to incorporate this.

    Can you share any accuracy rates for HealthScribe?

    SYED: The best way to show accuracy and build trust in the system is the method of showing end users what in the summary came from the dialogue. I don’t think showing them an accuracy stat is going to help as much.

    Internally, obviously, they use a number of approaches. Some are about factual completeness — is what was in the dialogue reflected in the summary? Did the model come up with something that wasn’t in the dialogue? There’s a couple other metrics they track internally but it’s not shared externally.

    Will you ever share accuracy metrics externally?

    SYED: I think we’re directly addressing the concern about quality by showing the user every time why a decision was made.

    To play devil’s advocate, from the doctor’s perspective they have a visit with a patient and then a summary pops out of their computer. What if there’s an issue with the original voice-to-text software that’s reflected in the transcript after being run through AI? What if doctors review summaries at the end of the day, and don’t remember specifics of a patient visit so don’t catch errors? How is AWS thinking about staying responsible here?

    SYED: This is part of why we’re announcing in preview, and we’re working with their partners to understand what’s necessary to do here. I don’t have a definitive answer for you. There’s a lot of complexity to it, right? You have an ambient listening device, but maybe it’s too far away, or the air conditioner’s too loud. It’s not as simple as HealthScribe sharing accuracy numbers, because there could be an issue with the recorded conversation that we’re getting.

    I agree that there’s a lot of think about in doing this responsibly.

    What we’re saying is here’s what they can do, and as HealthScribe is deployed in the workflow there are other things that their customers have to do to ensure it’s the right level of accuracy. They are very clear that it’s generating the summary but it is not automating the documentation. The idea is, this is a tool, and then the tool is applied in a workflow. And the workflow has a lot of those checks and balances in it.

    What if there’s an issue with that workflow that later results in a patient getting improper care, resulting in a malpractice suit? What party is liable — AWS? AWS’ client? The physician? How is AWS approaching the liability issue here, given the use of generative AI in clinical settings is still very new?

    SYED: The way they look at that is, we’re providing a capability that has to be incorporated in an end-to-end workflow, and that workflow has to deal with those aspects you’re talking about. It’s the vendor who’s incorporating the capability that has to make sure that workflow is appropriate, and the clinician also has to make sure.

    I worked at Cerner a long time before this. The dictation thing has been around for a very long time. It’s the same thing, right? Is it accurate or not? So folks have mechanisms in place to handle the workflow around this. But across the board with AI, there’s lots of new issues.

    Is AWS concerned that generative AI might be being implemented too quickly in clinical settings?

    SYED: I don’t have an opinion on moving too quickly or not quickly enough. They work with their customers on very specific use cases. With HealthScribe, we’re iterating during this preview period to make sure it is actually fit for its purpose, and it meets criteria that would result in real use in a responsible way.

    AWS is ready to power AI agents that can handle busywork instead of just chatting

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  • Tech/
  • Amazon
  • AWS is ready to power AI agents that can handle busywork instead of just chattingAWS is ready to power AI agents that can handle busywork instead of just chatting / Amazon’s cloud business AWS is leaning toward becoming a database of generative AI tools and will now add access to AI agents. Share this story
  • Swami Sivasubramanian standing on stage in front of a screen with the words “Enhancing AWS services with generative AI”AWS VP Swami Sivasubramanian at the AWS Summit event. Image: Amazon

    During the AWS Summit in New York on Wednesday, Amazon launched Agents for Bedrock, which will let companies build AI apps that can automatically do tasks on their own, like booking a flight for users instead of just telling them about it. AI agents are the assistant that actually gets a restaurant reservation instead of just giving suggestions on where to eat.

    “I believe this will supercharge developers who wanted an easier way to build agents and at the same time customize the data the models read,” Swami Sivasubramanian, vice president of data and machine learning at AWS, tells The Verge. “Building agents took so much time even with how advanced generative AI is now, but we’re making it so developers can access exactly the models they need.”

    Generative AI models like GPT-4 or Llama 2 are powerful but do not actually automate some tasks for users without extra help, like plug-ins. With agents, a travel company could use generative AI to suggest trips, then build one agent that will take in a user’s travel history and interests, another agent that finds flight schedules, and finally, an agent that books the chosen flight.

    Amazon, along with other AI companies like Meta, Google, and OpenAI, signed an agreement with the White House last week pledging to develop responsible AI. 

    Amazon itself hasn’t been a big player in the generative AI arms race in the way Meta, Google, OpenAI, and Microsoft have been. Still, it has positioned itself, via AWS, as a key infrastructure provider to the space. StabilityAI and Anthropic released new versions of their large language models, Stable Diffusion XL 1.0 and the chatbot Claude 2, that are now available on Bedrock. 

    Agents for Bedrock is aimed at startups and enterprises, letting companies use their own data to teach foundational models, like image-to-text models or large language models, and then build additional apps to finish tasks. A developer can choose which foundational model to use, provide some instructions, and choose which data for the model to read.

    Other tech companies are also working on agents; Meta CEO Mark Zuckerberg told investors there’s an opportunity to bring AI agents “to billions of people in ways that will be useful and meaningful.” OpenAI CEO Sam Altman, in a wide-ranging interview with The Atlantic, ruminated on AI agents and how to approach their creation best. 

    Bedrock, a platform where organizations can access foundational generative AI models like Stable Diffusion, Claude, Jurassic, and Amazon’s own Titan language, was launched in April this year. 

    Other AWS announcements included bringing generative AI to healthcare through a new service called AWS HealthScribe and a partnership with Nvidia allowing AWS to handle larger amounts of memory and data by using Nvidia H100 Tensor Core GPUs.

    AWS HealthScribe automates clinical documentation, basically notes summarizing a patient’s complaint, and makes medical notes for diagnosis. AWS partnered with 3M Health Information System to deploy the tool. Sivasubramanian said using generative AI to create clinical documentation saves time for healthcare professionals and lets them interact with more patients. 

    Bringing AI to healthcare, though, will be fraught. Healthcare is one of the most regulated spaces in the US. AI companies face questions about privacy protection and security. Data privacy is one of the main focuses of the Federal Trade Commission’s investigation into OpenAI. AWS reiterated that HealthScribe is HIPAA-eligible and will not use results from HealthScribe to train the tool. 

    “Healthcare is one of the spaces where AI can dramatically reduce the burden on people,” Sivasubramanian said.

    AWS made its machine learning-powered analytics tool, Entity Resolution, that helps people analyze and match documents across applications generally available. AWS also made its code assistant CodeWhisperer available in Glue Studio — a platform that simplifies coding for data analysts — to allow access for data engineers and analysts. It gives code suggestions in real time. 

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