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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Solutions Architect - Associate |
| Exam Number: | SAA-C03 |
| Related Certifications: | AWS Certified Solutions Architect - Professional AWS Certified Developer - Associate AWS Certified SysOps Administrator - Associate AWS Certified Cloud Practitioner |
| Available Languages: | Korean, English, Japanese, Simplified Chinese |
| Passing Score: | 720/1000 |
| Real Exam Qty: | 65 |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 150 |
| Exam Duration: | 130 minutes |
| Exam Format: | Multiple Choice, Multiple Response |
| Sample Questions: | Amazon AWS-Solutions-Architect-Associate Sample Questions |
| Exam Way: | Pearson VUE or PSI testing center; online proctored exam available. |
| Pre Condition: | No formal prerequisite. AWS recommends at least 1 year of hands-on experience designing cloud solutions using AWS services. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-solutions-architect-associate/ |
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NEW QUESTION # 287
A manufacturing company runs its report generation application on AWS. The application generates each report in about 20 minutes. The application is built as a monolith that runs on a single Amazon EC2 instance.
The application requires frequent updates to its tightly coupled modules. The application becomes complex to maintain as the company adds new features.
Each time the company patches a software module, the application experiences downtime. Report generation must restart from the beginning after any interruptions. The company wants to redesign the application so that the application can be flexible, scalable, and gradually improved. The company wants to minimize application downtime.
Which solution will meet these requirements?
Answer: B
Explanation:
The solution that will meet the requirements is to run the application on Amazon Elastic Container Service (Amazon ECS) as microservices with service auto scaling. This solution will allow the application to be flexible, scalable, and gradually improved, as well as minimize application downtime. By breaking down the monolithic application into microservices, the company can decouple the modules and update them independently, without affecting the whole application. By running the microservices on Amazon ECS, the company can leverage the benefits of containerization, such as portability, efficiency, and isolation. By enabling service auto scaling, the company can adjust the number of containers running for each microservice based on demand, ensuring optimal performance and cost. Amazon ECS also supports various deployment strategies, such as rolling update or blue/green deployment, that can reduce or eliminate downtime during updates.
The other solutions are not as effective as the first one because they either do not meet the requirements or introduce new challenges. Running the application on AWS Lambda as a single function with maximum provisioned concurrency will not meet the requirements, as it will not break down the monolith into microservices, nor will it reduce the complexity of maintenance. Lambda functions are also limited by execution time (15 minutes), memory size (10 GB), and concurrency quotas, which may not be sufficient for the report generation application. Running the application on Amazon EC2 Spot Instances as microservices with a Spot Fleet default allocation strategy will not meet the requirements, as it will introduce the risk of interruptions due to spot price fluctuations. Spot Instances are not guaranteed to be available or stable, and may be reclaimed by AWS at any time with a two-minute warning. This may cause report generation to fail or restart from scratch. Running the application on AWS Elastic Beanstalk as a single application environment with an all-at-once deployment strategy will not meet the requirements, as it will not break down the monolith into microservices, nor will it minimize application downtime. The all-at-once deployment strategy will deploy updates to all instances simultaneously, causing a brief outage for the application.
References:
Amazon Elastic Container Service
Microservices on AWS
Service Auto Scaling - Amazon Elastic Container Service
AWS Lambda
Amazon EC2 Spot Instances
[AWS Elastic Beanstalk]
NEW QUESTION # 288
A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket A series ot data preparation jobs aggregate the data for reporting The data preparation jobs need to run at regular intervals in parallel A few jobs need to run in a specific order later The company wants to remove the operational overhead of job error handling retry logic, and state management Which solution will meet these requirements?
Answer: B
Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to easily clean, normalize, and transform your data without writing any code. You can create and run data preparation jobs on your data stored in Amazon S3, Amazon Redshift, or other data sources. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use Step Functions to orchestrate your DataBrew jobs, define the order and parallelism of execution, handle errors and retries, and monitor the state of your workflow. By using AWS Glue DataBrew and AWS Step Functions, you can meet the requirements of the company with minimal operational overhead, as you do not need to write any code, manage any servers, or deal with complex dependencies.
References:
AWS Glue DataBrew
AWS Step Functions
Orchestrate AWS Glue DataBrew jobs using AWS Step Functions
NEW QUESTION # 289
A company operates a website on Amazon EC2 Linux instances. Some of the instances are faring Troubleshooting points to insufficient swap space on the failed instances. The operations team lead needs a solution to monitor this.
What should a solutions architect recommend?
Answer: B
NEW QUESTION # 290
Is decreasing the storage size of a DB Instance permitted?
Answer: B
NEW QUESTION # 291
A 3-Ber e-commerce web application is currently deployed on-premises, and will be migrated to AWS for greater scalability and elasticity. The web tier currently shares read-only data using a network distributed file system. The app server tier uses a clustering mechanism for discovery and shared session state that depends on IP multicast. The database tier uses shared-storage clustering to provide database failover capability, and uses several read slaves for scaling. Data on all servers and the distributed file system directory is backed up weekly to off-site tapes.
Which AWS storage and database architecture meets the requirements of the application?
Answer: C
Explanation:
Amazon Glacier doesn't suit all storage situations. Listed following are a few storage needs for which you should consider other AWS storage options instead of Amazon Glacier.
Data that must be updated very frequently might be better served by a storage solution with lower read/write latencies, such as Amazon EBS, Amazon RDS, Amazon DynamoDB, or relational databases running on EC2.
https://d0.awsstatic.com/whitepapers/Storage/AWS%20Storage%20Services%20Whitepaper-v9.pdf
NEW QUESTION # 292
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