Modern technology now relies heavily on machine learning (ML), which allows algorithms to learn from data, spot trends, and make judgments with little assistance from humans. ML is changing sectors all around the world, from automating complicated procedures to personalizing user experiences.

ML services in the AWS ecosystem are made to make implementation easier by providing strong tools that call for little to no data science knowledge. It is crucial for applicants to comprehend the main machine learning services and their real-world uses in order to pass the AWS Certified Solutions Architect – Associate (SAA-C03) exam.

The ML services examined in the examination, their applications, and methods to help you master ML-related problems are all covered in this article.


Overview of AWS ML Services Relevant to SAA-C03

AWS offers a range of ML services, from fully managed AI tools to customizable platforms for building your own models. Below is an overview of the most relevant services for the SAA-C03 exam:

A powerful service for analyzing images and videos, Rekognition can detect faces, objects, and inappropriate content. It’s widely used for security, media analysis, and compliance.

Converts text into lifelike speech in multiple languages and voices, enhancing accessibility and enabling voice-driven applications.

A conversational AI service for building chatbots and voice assistants. Lex integrates seamlessly with other AWS services, simplifying customer engagement.

Provides automatic speech recognition (ASR) for converting audio files into text. It's commonly used in call centers, video transcription, and accessibility solutions.

Offers natural language processing (NLP) capabilities for tasks such as sentiment analysis, language detection, and entity recognition, making it ideal for customer insights.

A comprehensive platform for building, training, and deploying custom ML models. It’s perfect for organizations requiring bespoke AI solutions.

Extracts text, tables, and other structured data from scanned documents. Textract is widely used in finance and healthcare for automating form processing and compliance reporting.

AWS ML services are designed to address diverse business needs:

Identity verification for user onboarding.

Content moderation for user-generated images and videos.

Adding text-to-speech capabilities for accessibility.

Creating lifelike voice narrations for e-learning content.

Building interactive customer support chatbots.

Developing voice interfaces for IoT devices.

Transcribing customer calls for sentiment analysis.

Creating searchable video content through automatic subtitles.

Analyzing customer feedback to extract actionable insights.

Categorizing documents based on topics and entities.

Building recommendation systems for e-commerce platforms.

Forecasting business metrics like demand or inventory needs.

Automating the extraction of data from invoices and receipts.

Digitizing patient records in healthcare systems.

In the SAA-C03 exam, ML services are tested through scenario-based questions(Well, almost the whole exam is scenario based so...). Here’s what to expect:


Identify the appropriate ML service for a given business problem. For instance, selecting Textract to automate invoice processing or Rekognition for content moderation.


Understand how ML services interact with other AWS components, such as using Transcribe with S3 for storing audio files or Lambda for processing results.


Choose cost-effective and scalable solutions, like leveraging SageMaker for training custom models on-demand instead of using static EC2 instances.

How AWS ML Services Interact with Other AWS Services

As we have said, look out for how the ML services interact with other AWS services. Here's a few examples that you might want to take note of:

:
Store images and videos in S3, and use Rekognition to analyze the content directly from the bucket. For example, you can detect inappropriate content in user-uploaded media.

:
Trigger Lambda functions when new documents are uploaded to an S3 bucket. Use Textract to extract data from these documents automatically and forward the output for further processing.

:
Stream live audio data to Kinesis Data Streams, and use Transcribe to convert the audio into text in real-time for transcription or analytics.

:
Integrate Polly with IoT Core to add voice responses to IoT devices, enabling natural-sounding interactions for end-users.

:
Use Comprehend to analyze social media data for sentiment and trigger alerts or notifications via SNS based on specific insights.

:
Coordinate ML workflows with Step Functions, from data preprocessing to model training and deployment, creating a fully automated pipeline.

A financial institution receives thousands of scanned loan applications daily. The institution needs to extract applicant information, such as name, address, and loan amount, and store it in a database. The solution must minimize manual effort and integrate seamlessly with existing AWS services.
What should a solutions architect recommend?

A. Use Amazon Comprehend to extract the text and entities. Store the results in Amazon RDS.

B. Use Amazon Rekognition to detect text in the scanned documents. Store the results in Amazon DynamoDB.

C. Use Amazon Textract to extract text and form data. Use AWS Lambda to store the extracted data in Amazon RDS.

D. Use Amazon SageMaker to train a custom ML model to process the documents. Use Amazon S3 to store the results.


Amazon Textract is designed for extracting text, tables, and structured data from scanned documents. Lambda can automate the process of storing the results in Amazon RDS, reducing manual effort and simplifying integration.

An e-commerce company wants to build a chatbot for its website to assist users with order tracking and answering common product queries. The chatbot must integrate with existing backend APIs hosted on AWS.
Which service combination should the company use?


Amazon Lex is purpose-built for creating conversational AI interfaces, such as chatbots. Lambda can be used to invoke backend APIs for processing user requests dynamically.

A company needs to analyze customer feedback from product reviews stored in an Amazon S3 bucket. The analysis should provide insights into sentiment, key phrases, and entities mentioned in the reviews.
What is the most efficient solution?

A. Use Amazon SageMaker to train a custom sentiment analysis model and process the reviews.

B. Use Amazon Comprehend to analyze sentiment, key phrases, and entities in the text.

C. Use Amazon Rekognition to detect text in the reviews and analyze it.

D. Use AWS Glue to extract insights from the reviews.


Amazon Comprehend is designed for natural language processing (NLP) tasks, including sentiment analysis, key phrase extraction, and entity recognition. It can analyze text data efficiently with minimal setup.

A video streaming platform wants to provide closed captions for its live broadcasts in real time. The platform needs to ensure scalability and low latency.
Which AWS service should the platform use to meet these requirements?

A. Amazon Transcribe and Amazon Kinesis Data Streams


Amazon Transcribe can perform real-time speech-to-text conversion, which is ideal for generating live captions. Integrating with Kinesis Data Streams ensures scalable and low-latency data processing for live broadcasts.

A healthcare provider wants to automate the processing of patient records to extract medical conditions and medications mentioned in clinical notes. The extracted data must be stored in a database for further analysis.
Which solution should the provider implement?

A. Use Amazon Textract to extract medical conditions and medications. Store the results in Amazon DynamoDB.

B. Use Amazon SageMaker to train a model for extracting medical information. Store the results in Amazon RDS.

C. Use Amazon Comprehend Medical to extract medical conditions and medications. Use AWS Lambda to store the results in Amazon RDS.

D. Use Amazon Lex to analyze the clinical notes and extract relevant data.


Amazon Comprehend Medical is specifically designed for extracting structured medical information, such as conditions and medications, from unstructured clinical notes. Lambda can automate storing the results in Amazon RDS.

Dive into our programming articles for insights and tips. Covering a range of topics, they're perfect for learners at any level eager to expand their knowledge.

Dive into our programming articles for insights and tips.

Articles

Machine Learning Services for the AWS SAA-C03 Exam

By now, we all know that data drives decision-making, they say data is the new oil, right? The ability to process and analyze information in real time is no longer a luxury—it’s a necessity! From tracking user interactions to monitoring IoT devices, organizations need scalable and reliable solutions to handle data streams effectively.

Amazon Kinesis provides a suite of services tailored for real-time data ingestion, processing, and delivery. With its architecture and seamless integration with other AWS services, Kinesis empowers businesses to harness the full potential of their data streams.

Amazon Kinesis is designed to simplify the challenges of managing real-time data. Its modular services cater to different aspects of the data lifecycle:

Handles real-time ingestion of large volumes of data.

Ideal for applications requiring low-latency processing, such as fraud detection or live user analytics.

Offers fine-grained control with data sharding for scalability.

Simplifies the process of delivering streaming data to storage and analytics services like Amazon S3, Redshift, and Elasticsearch.

Fully managed, with automatic scaling and the ability to transform data on the fly using AWS Lambda.

Enables real-time processing of streaming data using standard SQL queries.

Allows organizations to derive insights without needing complex infrastructure or custom applications.

Processes and stores video streams for real-time or batch analysis.

Commonly used in smart surveillance systems, machine learning applications, or live broadcasting.

Amazon Kinesis supports a wide variety of use cases that require efficient handling of large data volumes:

Massive volumes of log data are frequently produced by organizations from servers, systems, and applications. While Kinesis Firehose sends logs to storage or analytical tools like Amazon Elasticsearch for indexing and visualization, Kinesis Data Streams may ingest logs in real time.


Devices in Internet of Things applications continuously produce streams of sensor data. In settings like manufacturing or smart cities, Amazon Kinesis makes sure that this data is gathered, processed, and evaluated in real-time, enabling adaptive control and predictive maintenance.

Businesses can record, process, and examine video inputs to derive useful insights with Kinesis Video Streams. Applications include retail customer behavior analysis and manufacturing line anomaly detection.


Event streaming is used by media and e-commerce companies to monitor user actions including clicks, page views, and transactions. Businesses may monitor system performance, develop suggestions, and improve user experiences by analyzing these events in real time.

Amazon Kinesis becomes even more powerful when integrated with other AWS services, enabling seamless workflows for data processing and analysis. Here are some common integrations:


Automatically trigger functions in response to new data in Kinesis Data Streams or Firehose. For example, Lambda can process incoming data, generate alerts, or invoke downstream systems.


Use Kinesis Data Firehose to deliver streaming data directly to S3 for long-term storage or further processing with services like AWS Glue.


Stream data into Redshift for near real-time analytics and business intelligence. This is particularly useful for creating dashboards or running ad hoc queries.


Store processed data in DynamoDB for use in real-time applications, such as leaderboards, content recommendations, or session management.


Prepare and catalog data stored in S3 or Redshift for querying and analytics, ensuring compatibility with tools like Amazon Athena.


Monitor the health and performance of Kinesis streams with CloudWatch metrics, logs, and alarms.


Optimize the use of Kinesis Data Streams by properly partitioning data across shards. Assign partition keys based on your application's requirements to balance the load and reduce latency.


Ensure reliable data processing with mechanisms like retries and dead-letter queues. Use Lambda or a custom application to reprocess records in case of errors.


Protect your data with encryption in transit (TLS) and at rest using AWS Key Management Service (KMS). Implement fine-grained IAM policies to control access to Kinesis resources.


Monitor shard usage and adjust shard count dynamically to balance performance and cost. For applications with unpredictable workloads, consider Kinesis On-Demand mode.

Smart City IoT Data Processing with Amazon Kinesis

Imagine a smart city project that uses IoT sensors to monitor air quality, traffic, and energy consumption in real time. Here's how Amazon Kinesis enables this solution:


Sensors send data to Kinesis Data Streams, where it's partitioned and stored for processing.


Use Kinesis Data Analytics to calculate metrics like average air quality index or traffic congestion levels. Processed insights are sent to a dashboard for city administrators.


Raw and processed data is delivered to S3 via Kinesis Data Firehose. Insights are further analyzed and visualized using Amazon QuickSight.


Lambda functions are triggered to control traffic lights during high congestion periods or send alerts when air quality exceeds safe thresholds.

This architecture ensures scalability, low latency, and real-time insights, enhancing the city's operational efficiency and livability.

Amazon Kinesis gives businesses the ability to instantly handle and analyze enormous amounts of data. It is a key component of contemporary data architectures due to its adaptability and smooth integration with the AWS ecosystem, whether it is handling IoT sensor streams, processing application logs, or providing actionable insights from video feeds. Hope you got the gist of it and will be able to apply this knowledge as you do your AWS SAA exam!

A financial services company collects real-time market data and needs to build a solution to process and analyze this data for actionable insights. The data is ingested at a high rate and needs to be retained for 24 hours before being processed by downstream systems. The company also wants to minimize operational overhead.

Which solution will meet these requirements?

A. Use Amazon Kinesis Data Streams to ingest the data and store it for 24 hours. Configure an AWS Lambda function to process the data.
B. Use Amazon Kinesis Data Firehose to ingest the data and deliver it to Amazon S3. Use AWS Glue for processing.
C. Deploy an Apache Kafka cluster on Amazon EC2 instances to manage the ingestion and processing of the data.
D. Use Amazon SQS to store messages and process them with an Amazon EC2-based application.

: A
Kinesis Data Streams is ideal for ingesting high-rate data and provides the capability to retain data for up to 7 days. It integrates easily with Lambda for processing, reducing operational overhead.

An online retail company needs to analyze clickstream data to track user behavior in real time. The company wants a solution that can stream data, perform real-time analytics using SQL, and support storing the results in Amazon S3 for long-term analysis.

A. Use Amazon Kinesis Data Streams to ingest the clickstream data and AWS Lambda to perform real-time analytics.
B. Use Amazon Kinesis Data Firehose to ingest the clickstream data and transform it before storing it in Amazon S3.
C. Use Amazon Kinesis Data Streams to ingest the data and Amazon Kinesis Data Analytics to perform real-time SQL analytics. Store the results in Amazon S3.
D. Use Amazon SQS to store clickstream events and Amazon EMR to analyze the data in batches.

: C
Kinesis Data Streams supports real-time data ingestion, and Kinesis Data Analytics allows for SQL-based real-time analytics. Storing results in S3 fulfills the long-term storage requirement.

A gaming company uses telemetry data from its games to detect cheating behavior in real time. The company requires a highly scalable and managed solution for streaming and analyzing the telemetry data with minimal latency.

A. Use Amazon Kinesis Data Firehose to ingest telemetry data and store it in Amazon Redshift for analysis.
B. Use Amazon Kinesis Data Streams to ingest the telemetry data and Amazon Kinesis Data Analytics to process the data in real time.
C. Use Amazon S3 to store the telemetry data and run AWS Glue to analyze it.
D. Use Amazon SQS to collect messages and process them with an EC2-based application.

: B
Kinesis Data Streams is designed for real-time data ingestion, and Kinesis Data Analytics enables low-latency, real-time processing, which is essential for detecting cheating in gaming telemetry.

A video streaming service needs to process and analyze live video streams for content moderation. The solution must be fully managed and scalable, supporting machine learning models for real-time analysis.

A. Use Amazon Kinesis Data Streams to ingest video streams and process them with AWS Lambda.
B. Use Amazon Kinesis Video Streams to ingest video streams and process them with Amazon Rekognition Video.
C. Use Amazon S3 to store video streams and Amazon SageMaker to analyze them.
D. Use an Amazon MQ broker to handle video ingestion and Amazon EC2 for processing.

: B
Kinesis Video Streams is tailored for ingesting and processing video streams, and it integrates directly with Rekognition Video for machine learning-based content moderation in real time.

A data analytics company needs to process large datasets that arrive in batch format once every hour. The company wants to use a managed service to transform and store the data in Amazon Redshift for reporting.

A. Use Amazon Kinesis Data Streams to ingest and process the data. Store it in Amazon Redshift.
B. Use Amazon Kinesis Data Firehose to ingest the data, transform it with an AWS Lambda function, and store it in Amazon Redshift.
C. Use AWS Glue to transform the data and load it into Amazon Redshift.
D. Use an Amazon EMR cluster to process the data and store it in Amazon Redshift.

: C
AWS Glue is designed for ETL operations on batch data and integrates with Amazon Redshift for seamless data loading and transformation. This minimizes operational overhead for batch processing.

Real-Time Data Processing with Amazon Kinesis

Any IT strategy must include disaster recovery (DR), which makes sure that in the case of failures—such as outages, hardware malfunctions, or natural disasters—systems can promptly recover and data is still accessible. Because of its scalability, worldwide architecture, and high availability and fault tolerance services, AWS offers a strong platform for DR implementation.
Different business needs for Recovery Time Objective (RTO) and Recovery Point Objective (RPO) are addressed by important DR solutions as Pilot Light, Warm Standby, and Multi-Site Active-Active. Organisations with on-premise resources are often advised to have cloud based backup for DR.

Disaster recovery is the process of restoring systems, applications, and data to normal operations after a disruptive event, ensuring business continuity and minimizing downtime.

There are terms that you'll often hear being mentioned in relation to DR such as:

 The maximum acceptable time to restore operations after a disruption.

 The maximum acceptable amount of data loss measured in time.

Let's use an example to explain RTO & RPO. A service might have an RTO of 1 hour and and RPO of 15 mins. That means that 1 hour is the maximum acceptable out-of-service time without causing too much loss to the users/clients of the service (RTO). The service should also be able to restore all the data to the point it was 15 minutes before the outage(RTO).

AWS offers services that cater to different RTO and RPO requirements, making it easy to balance cost and recovery needs.

 Automate disaster recovery testing, and monitor recovery performance.

 Design systems with failover in mind, leveraging multiple Availability Zones (AZs) and Regions.

 Automates the provisioning of recovery environments.

 Facilitates real-time replication of workloads.

 Centralized backup service that ensures data durability.

Pilot Light ensures that critical systems are always ready for recovery. A minimal version of the infrastructure is continuously running, and additional resources are scaled up during recovery. Databases and object storage, among other resources, are constantly available to allow data backup and replication. Other components, such application servers, are "switched off" and only utilized when testing or disaster recovery failover is triggered. They are loaded with application code and configurations.

 A small database server is continuously replicating production data, while compute instances are dormant but pre-configured.

 For maintaining a small database replica.

 For quick provisioning of additional resources.

Warm Standby maintains a scaled-down, fully functional copy of the production environment. It offers faster recovery times than Pilot Light by having a partially operational environment ready.

 E-commerce platforms needing quicker recovery to minimize downtime.

Automatically scale up the environment to handle full production workloads.

 Replicates data across multiple AZs for low-latency DR.

 Ensures configurations are consistent during scaling.

 method to operate your workload concurrently in several regions. While hot standby simply serves traffic from one area and uses the other region or regions only for disaster recovery, multi-site active/active serves traffic from all regions to which it is deployed.

 Mission-critical financial applications requiring zero downtime.

 Configured for geo-routing and failover.

 Ensures low latency by routing traffic to the nearest Region.

 Allows a single database to span multiple Regions for real-time replication.

Here DR is achieved by duplicating data to additional AWS Regions to reduce the lack of redundancy for workloads deployed to a single Availability Zone or to protect against a regional disaster. You need to redeploy the application code, configuration, and infrastructure in the recovery region in addition to the data. Infrastructure should always be deployed using infrastructure as code (IaC) utilizing services like AWS CloudFormation or the AWS Cloud Development Kit (AWS CDK) to allow for rapid and error-free redeployment.

 - A startup with budget constraints runs its workload in a single Availability Zone but backs up its databases and application files nightly to Amazon S3 with Cross-Region Replication enabled.

 Centralized backup for databases, file systems, and more.


 Cost-effective storage for backup data with cross-region replication.


 Enables quick, automated infrastructure provisioning.


 Automates the deployment of application code.


 Utilizes AMIs to recreate instances in recovery Regions.

A thorough understanding of disaster recovery techniques, trade-offs, and how AWS services support various recovery objectives is necessary for preparing for the AWS Solutions Architect Associate (SAA) exam. Pay attention to important ideas like RTO and RPO, and understand which AWS products and architectures fit different situations, such cost-effective backup and restore or cross-region failover.

From Multi-Site Active-Active architectures for important systems to Pilot Light configurations for lighter workloads, AWS provides scalable, adaptable, and reasonably priced disaster recovery options. Gaining proficiency in these techniques not only gets you ready for test success but also enables you to create solid, trustworthy solutions for real-world problems. To guarantee preparedness, effectiveness, and compliance with AWS best practices, test and improve your disaster recovery strategies on a regular basis. Good luck!

Your company hosts a mission-critical e-commerce application in the us-east-1 Region. To ensure high availability and quick disaster recovery, you want to implement cross-region failover to us-west-2. Which AWS services would you use to synchronize application data and route traffic to the secondary Region during a failure?

a) Amazon S3 with Lifecycle Policies and Elastic Load Balancing (ELB)
b) Aurora Global Database and Route 53 Failover Routing Policy
c) Amazon RDS Multi-AZ and Application Load Balancer (ALB)
d) DynamoDB Streams and S3 Cross-Region Replication


Aurora Global Database synchronizes data across Regions with low latency, and Route 53 Failover Routing Policy ensures traffic is redirected to the secondary Region during outages.

Your company wants a disaster recovery strategy for a SaaS application with a 12-hour RTO but must minimize costs. Which DR strategy and services would you recommend?
a) Pilot Light with Amazon RDS and Elastic Load Balancing
b) Multi-Site Active-Active with Route 53 and Aurora Global Database
c) Backup and Restore with Amazon S3 and AWS Backup
d) Warm Standby with Auto Scaling and CloudFront

 
Backup and Restore is the most cost-effective strategy, suitable for a 12-hour RTO. Amazon S3 and AWS Backup enable secure, cost-efficient storage and recovery.

Your application hosted in a single AZ has experienced a hardware failure. You need to restore operations using the Backup and Restore strategy. Which sequence of actions will restore your application with minimal downtime?

a) Use Amazon S3 for backup retrieval, AWS CloudFormation for infrastructure, and AWS CodePipeline for application deployment.
b) Retrieve backups from Amazon RDS snapshots, manually configure infrastructure, and deploy applications from source code.
c) Enable Multi-AZ for databases, replicate data to a secondary AZ, and use Elastic Beanstalk for infrastructure setup.
d) Use S3 Cross-Region Replication, create infrastructure manually, and deploy applications with AWS CodeDeploy.

S3 ensures durable backup storage, CloudFormation automates infrastructure deployment, and CodePipeline redeploys application code, minimizing recovery time.

A financial institution requires a disaster recovery plan with a low RTO for its transaction system. Core functionality must be restored cost-effectively. Which implementation best supports the Pilot Light strategy?

a) Fully replicate all resources in a secondary Region using Multi-Site Active-Active.
b) Maintain a minimal environment with critical services running and scale up resources during a disaster.
c) Keep backups in Amazon S3 and redeploy everything manually during recovery.
d) Use Auto Scaling with Spot Instances for all critical services during recovery.

Pilot Light involves running a minimal environment (e.g., database replicas) and scaling up additional resources when needed to meet demand.

Your global streaming service requires zero downtime and high availability across multiple Regions. Which AWS services and configurations enable a Multi-Site Active-Active setup?
a) DynamoDB Streams and S3 Cross-Region Replication
b) Route 53 Geolocation Routing and Aurora Global Database
c) AWS Backup and Elastic Load Balancing
d) S3 Lifecycle Policies and AWS CloudFront

Route 53 distributes traffic based on user location, while Aurora Global Database ensures synchronized data across multiple Regions, supporting an active-active architecture.

Understanding Disaster Recovery in AWS for the AWS SAA exam

Caching is a technique used to store frequently accessed data in a temporary storage layer for quick retrieval. This reduces the need to access the primary storage or database repeatedly, thus improving performance and reducing latency. In AWS, caching can be implemented across various services to enhance application responsiveness.

Caching is crucial in improving application performance by:

: Cached data is stored closer to the application or user, reducing round-trip times for data access.

: By reducing the number of requests to backend databases or storage services, caching can reduce costs associated with data transfer and database operations.

: It helps applications scale by reducing the load on the backend infrastructure, enabling smoother operations during traffic spikes.

: Faster response times lead to a more seamless and efficient user experience.

Caching becomes especially important in scenarios such as:

: When applications frequently access the same data from a database.

: Caching API responses to reduce processing time.

: When delivering static content like images, videos, or large datasets to end users.

: When handling high traffic and spikes in demand, caching reduces strain on your resources.

: Storing user session data temporarily for fast access.

AWS provides multiple services that can be used for caching:

Amazon ElastiCache is a fully managed in-memory caching service. It supports two major engines:

: An in-memory key-value store that supports advanced data structures, pub/sub messaging, and persistence.

: A simpler key-value store suitable for caching frequently accessed data from databases or API requests.

: Automatically scales in response to demand.

: Distributes data across multiple nodes to improve performance.

: Allows the distribution of cache across multiple instances.

CloudFront is AWS's content delivery network (CDN), which caches content at edge locations globally, bringing static and dynamic content closer to users.

: Ideal for caching static content like images, CSS files, and JavaScript.

: Can cache dynamic content based on request headers.

: Data is cached at over 400 points of presence worldwide.

: Allows customization of what data is cached and for how long.

: Integrates with AWS Shield and AWS WAF for protection.

While not a traditional caching service, 

, which allows faster content delivery by caching S3 objects at edge locations.

: Improves latency by routing data to the nearest edge location.

: Objects stored in S3 can be cached to improve performance for global users.

This service is a global traffic manager that accelerates application performance by caching DNS resolutions and routing traffic through AWS’s global network.

 is designed for in-memory caching of data, while 

 focuses on caching content at edge locations for global distribution.

ElastiCache is used for database query caching, session storage, etc., while CloudFront is optimized for static and dynamic content delivery.

 offers data persistence, replication, and advanced data structures, making it suitable for complex caching and in-memory databases.

 is simpler and more lightweight, making it ideal for straightforward caching needs where high throughput is required but advanced features are not.

 is a CDN with edge caching, primarily for website assets and APIs, while 

aws Articles

Machine Learning Services for the AWS SAA-C03 Exam

Real-Time Data Processing with Amazon Kinesis

Understanding Disaster Recovery in AWS for the AWS SAA exam

AWS Caching guide for the AWS Solutions Architect Associate exam

Network Connections in AWS for the Solutions Architect associate Exam

VPC Peering in AWS

Amazon Elastic Block Store Guide For the AWS SAA

Amazon Machine Images (AMI) Comprehensive Guide for AWS SAA

Amazon FSx guide for the AWS Solutions Architect Associate Exam

Practice Frontend, Android and iOS by building real world projects.