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Amazon SAA-C03 certification exam is a challenging but rewarding certification that validates the candidate’s expertise in AWS. It is a valuable asset for professionals who are looking to advance their careers in the field of cloud computing and AWS. With the increasing demand for cloud computing professionals, the Amazon SAA-C03 certification is a great way to distinguish oneself in the job market and stay ahead of the competition.

The SAA-C03 exam is designed to validate a candidate's technical expertise in AWS services and their ability to design and deploy scalable and fault-tolerant systems on AWS. Candidates who pass SAA-C03 exam will demonstrate their ability to design and deploy robust and scalable systems on AWS, which is highly valued by employers.

Achieving the Amazon SAA-C03 Certification demonstrates a high level of expertise in designing and deploying applications on AWS. AWS Certified Solutions Architect - Associate certification is recognized by employers and industry experts as a valuable credential that can help users advance their careers in the cloud computing industry. AWS Certified Solutions Architect - Associate certification is also a prerequisite for other AWS certifications, such as the Professional and Specialty certifications.

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Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q397-Q402):

NEW QUESTION # 397
A company wants to improve the availability and performance of its hybrid application. The application consists of a stateful TCP-based workload hosted on Amazon EC2 instances in different AWS Regions and a stateless UDP-based workload hosted on premises.
Which combination of actions should a solutions architect take to improve availability and performance?
(Select TWO.)

A. Configure a Network Load Balancer in each Region to address the EC2 endpoints. Configure an Application Load Balancer in each Region that routes to the on-premises endpoints.
B. Configure a Network Load Balancer in each Region to address the EC2 endpoints. Configure a Network Load Balancer in each Region that routes to the on-premises endpoints.
C. Create an Amazon CloudFront distribution with an origin that uses Amazon Route 53 latency-based routing to route requests to the load balancers.
D. Create an accelerator using AWS Global Accelerator. Add the load balancers as endpoints.
E. Configure two Application Load Balancers in each Region. The first will route to the EC2 endpoints.
and the second will route lo the on-premises endpoints.

Answer: B,D

Explanation:
For improving availability and performance of the hybrid application, the following solutions are optimal:
AWS Global Accelerator (Option A): Global Accelerator provides high availability and improves performance by using the AWS global network to route user traffic to the nearest healthy endpoint (across AWS Regions). By adding the Network Load Balancers as endpoints, Global Accelerator ensures that traffic is routed efficiently to the closest endpoint, improving both availability and performance.
Network Load Balancer (Option D): The stateful TCP-based workload hosted on Amazon EC2 instances and the stateless UDP-based workload hosted on-premises are best served by Network Load Balancers (NLBs). NLBs are designed to handle TCP and UDP traffic with ultra-low latency and can route traffic to both EC2 and on-premises endpoints.
Option B (CloudFront and Route 53): CloudFront is better suited for HTTP/HTTPS workloads, not for TCP
/UDP-based applications.
Option C (ALB): Application Load Balancers do not support the stateless UDP-based workload, making NLBs the better choice for both TCP and UDP.
AWS References:
AWS Global Accelerator
Network Load Balancer

NEW QUESTION # 398
A company has registered its domain name with Amazon Route 53. The company uses Amazon API Gateway in the ca-central-1 Region as a public interface for its backend microservice APIs. Third-party services consume the APIs securely. The company wants to design its API Gateway URL with the company's domain name and corresponding certificate so that the third-party services can use HTTPS.
Which solution will meet these requirements?

A. Create a Regional API Gateway endpoint. Associate the API Gateway endpoint with the company's domain name. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the same Region. Attach the certificate to the API Gateway endpoint. Configure Route 53 to route traffic to the API Gateway endpoint.
B. Create stage variables in API Gateway with Name="Endpoint-URL" and Value="Company Domain Name" to overwrite the default URL. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM).
C. Create a Regional API Gateway endpoint. Associate the API Gateway endpoint with the company's domain name. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the us-east-1 Region. Attach the certificate to the API Gateway APIs. Create Route 53 DNS records with the company's domain name. Point an A record to the company's domain name.
D. Create Route 53 DNS records with the company's domain name. Point the alias record to the Regional API Gateway stage endpoint. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the us-east-1 Region.

Answer: C

NEW QUESTION # 399
A company has an Amazon S3 data lake The company needs a solution that transforms the data from the data lake and loads the data into a data warehouse every day The data warehouse must have massively parallel processing (MPP) capabilities.
Data analysts then need to create and train machine learning (ML) models by using SQL commands on the data The solution must use serverless AWS services wherever possible Which solution will meet these requirements?

A. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless Use Amazon Redshift ML to create and tram the ML models
B. Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless Use Amazon Aurora ML to create and train the ML models
C. Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift Use Amazon Redshift ML to create and train the ML models
D. Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables Use Amazon Athena ML to create and train the ML models

Answer: A

Explanation:
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue can automatically discover your data in Amazon S3 and catalog it, so you can query and search the data using SQL. AWS Glue can also run serverless ETL jobs using Apache Spark and Python to transform and load your data into various destinations, such as Amazon Redshift, Amazon Athena, or Amazon Aurora. AWS Glue is a serverless service, so you only pay for the resources consumed by the jobs, and you don't need to provision or manage any infrastructure.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables you to use standard SQL and your existing business intelligence (BI) tools to analyze your data. Amazon Redshift also supports massively parallel processing (MPP), which means it can distribute and execute queries across multiple nodes in parallel, delivering fast performance and scalability. Amazon Redshift Serverless is a new option that automatically scales query compute capacity based on the queries being run, so you don't need to manage clusters or capacity. You only pay for the query processing time and the storage consumed by your data.
Amazon Redshift ML is a feature that enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift ML can automatically discover the best model and hyperparameters for your data, and store the model in Amazon SageMaker, a fully managed service that provides a comprehensive set of tools for building, training, and deploying ML models. You can then use SQL functions to apply the model to your data in Amazon Redshift and generate predictions.
The combination of AWS Glue, Amazon Redshift Serverless, and Amazon Redshift ML meets the requirements of the question, as it provides a serverless, scalable, and SQL-based solution to transform, load, and analyze the data from the Amazon S3 data lake, and to create and train ML models on the data.
Option A is not correct, because Amazon EMR is not a serverless service. Amazon EMR is a managed service that simplifies running Apache Spark, Apache Hadoop, and other big data frameworks on AWS. Amazon EMR requires you to launch and configure clusters of EC2 instances to run your ETL jobs, which adds complexity and cost compared to AWS Glue.
Option B is not correct, because Amazon Aurora Serverless is not a data warehouse service, and it does not support MPP. Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a relational database service that is compatible with MySQL and PostgreSQL. Amazon Aurora Serverless can automatically adjust the database capacity based on the traffic, but it does not distribute the data and queries across multiple nodes like Amazon Redshift does. Amazon Aurora Serverless is more suitable for transactional workloads than analytical workloads.
Option D is not correct, because Amazon Athena is not a data warehouse service, and it does not support MPP.
Amazon Athena is an interactive query service that enables you to analyze data in Amazon S3 using standard SQL. Amazon Athena is serverless, so you only pay for the queries you run, and you don't need to load the data into a database. However, Amazon Athena does not store the data in a columnar format, compress the data, or optimize the query execution plan like Amazon Redshift does. Amazon Athena is more suitable for ad-hoc queries than complex analytics and ML.
References:
* AWS Glue
* Amazon Redshift
* Amazon Redshift Serverless
* Amazon Redshift ML
* Amazon EMR
* Amazon Aurora Serverless
* Amazon Athena

NEW QUESTION # 400
[Design Resilient Architectures]
A company runs a highly available web application on Amazon EC2 instances behind an Application Load Balancer The company uses Amazon CloudWatch metrics As the traffic to the web application Increases, some EC2 instances become overloaded with many outstanding requests The CloudWatch metrics show that the number of requests processed and the time to receive the responses from some EC2 instances are both higher compared to other EC2 instances The company does not want new requests to be forwarded to the EC2 instances that are already overloaded.
Which solution will meet these requirements?

A. Use the round robin routing algorithm based on the RequestCount and TargetResponseTime CloudWatch metrics.
B. Use the least outstanding requests algorithm based on the RequestCountPerTarget and ActiveConnectionCount CloudWatch metrics.
C. Use the round robin routing algorithm based on the RequestCountPerTarget and Active Connection Count CloudWatch metrics.
D. Use the least outstanding requests algorithm based on the RequestCount and TargetResponseTime CloudWatch metrics.

Answer: D

Explanation:
The least outstanding requests (LOR) algorithm is a load balancing algorithm that distributes incoming requests to the target with the fewest outstanding requests. This helps to avoid overloading any single target and improves the overall performance and availability of the web application. The LOR algorithmcan use the RequestCount and TargetResponseTime CloudWatch metrics to determine the number of outstanding requests and the response time of each target. These metrics measure the number of requests processed by each target and the time elapsed after the request leaves the load balancer until a response from the target is received by the load balancer, respectively. By using these metrics, the LOR algorithm can route new requests to the targets that are less busy and more responsive, and avoid sending requests to the targets that are already overloaded or slow. This solution meets the requirements of the company.
Reference:
Application Load Balancer now supports Least Outstanding Requests algorithm for load balancing requests Target groups for your Application Load Balancers Elastic Load Balancing - Application Load Balancers

NEW QUESTION # 401
A media company is evaluating the possibility ot moving rts systems to the AWS Cloud The company needs at least 10 TB of storage with the maximum possible I/O performance for video processing. 300 TB of very durable storage for storing media content, and 900 TB of storage to meet requirements for archival media that is not in use anymore Which set of services should a solutions architect recommend to meet these requirements?

A. Amazon EBS for maximum performance, Amazon EFS for durable data storage and Amazon S3 Glacier for archival storage
B. Amazon EBS for maximum performance, Amazon S3 for durable data storage, and Amazon S3 Glacier for archival storage
C. Amazon EC2 Instance store for maximum performance. Amazon S3 for durable data storage, and Amazon S3 Glacier for archival storage
D. Amazon EC2 instance store for maximum performance. Amazon EFS for durable data storage and Amazon S3 for archival storage

Answer: B

Explanation:
Explanation
https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html

NEW QUESTION # 402
......

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