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Achieving the Amazon SAA-C03 Certification demonstrates that an individual has the skills and expertise needed to design and deploy scalable, highly available, and fault-tolerant systems on AWS. It is a valuable credential for IT professionals who want to advance their careers and take on more challenging roles in cloud computing.

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Amazon SAA-C03 Certification Exam is a highly sought-after certification for professionals who want to validate their skills in designing and deploying scalable, highly available, and fault-tolerant systems on Amazon Web Services (AWS). The SAA-C03 exam is the updated version of the previous SAA-C02 exam and is designed to test a candidate's ability to design and implement solutions that meet customer requirements using AWS services and technologies.

The SAA-C03 exam consists of multiple-choice and multiple-response questions, and it covers a range of topics related to AWS services, including compute, storage, database, networking, security, and AWS cost management. SAA-C03 Exam is intended for individuals who have at least one year of hands-on experience designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Associate certification is highly valued by organizations that use AWS as their cloud platform and by professionals who want to advance their careers in the field of cloud computing.

Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q875-Q880):

NEW QUESTION # 875
A mining company is using Amazon S3 as its data lake. The company wants to analyze the data collected by the sensors in its mines. A data pipeline is being built to capture data from the sensors, ingest the data into an S3 bucket, and convert the data to Apache Parquet format. The data pipeline must be processed in near-real time. The data will be used for on-demand queries with Amazon Athena. Which solution will meet these requirements?

Answer: D

Explanation:
Amazon Kinesis Data Firehose (now known as Amazon Data Firehose) supports near-real-time streaming, with built-in support to:
Invoke AWS Lambda functions for transformation.
Store data directly into Amazon S3 in desired formats like Apache Parquet.
"You can use Data Firehose to transform the data before delivering it, by invoking a Lambda function. You can convert to formats such as JSON or Apache Parquet before storing it into S3." Why not the others?
B: Kinesis Data Streams requires more operational overhead than Firehose.
C: DataSync is for file movement--not stream processing.
D: SQS isn't designed for streaming and transformation pipelines.


NEW QUESTION # 876
The customers of a finance company request appointments with financial advisors by sending text messages. A web application that runs on Amazon EC2 instances accepts the appointment requests. The text messages are published to an Amazon Simple Queue Service (Amazon SQS) queue through the web application. Another application that runs on EC2 instances then sends meeting invitations and meeting confirmation email messages to the customers. After successful scheduling, this application stores the meeting information in an Amazon DynamoDB database.
As the company expands, customers report that their meeting invitations are taking longer to arrive. What should a solutions architect recommend to resolve this issue?

Answer: B

Explanation:
For SQS-backed worker architectures, AWS recommends scaling consumers based on queue metrics (e.g., ApproximateNumberOfMessagesVisible, AgeOfOldestMessage). EC2 Auto Scaling can "scale out based on Amazon CloudWatch alarms" tied to SQS backlog, increasing worker capacity to reduce latency as demand grows. DAX (A) accelerates DynamoDB reads, not message processing. API Gateway (B) and CloudFront (C) optimize request ingress and content delivery, not the asynchronous email-sending application. The bottleneck is consumer throughput; scaling workers with an SQS- driven policy restores timely processing without downtime and follows Well-Architected patterns for decoupled, elastic systems.


NEW QUESTION # 877
The media company that you are working for has a video transcoding application running on Amazon EC2. Each EC2 instance polls a queue to find out which video should be transcoded, and then runs a transcoding process. If this process is interrupted, the video will be transcoded by another instance based on the queuing system. This application has a large backlog of videos which need to be transcoded. Your manager would like to reduce this backlog by adding more EC2 instances, however, these instances are only needed until the backlog is reduced.
In this scenario, which type of Amazon EC2 instance is the most cost-effective type to use?

Answer: C

Explanation:
You require an instance that will be used not as a primary server but as a spare compute resource to augment the transcoding process of your application. These instances should also be terminated once the backlog has been significantly reduced. In addition, the scenario mentions that if the current process is interrupted, the video can be transcoded by another instance based on the queuing system. This means that the application can gracefully handle an unexpected termination of an EC2 instance, like in the event of a Spot instance termination when the Spot price is greater than your set maximum price.
Hence, an Amazon EC2 Spot instance is the best and cost-effective option for this scenario.

Amazon EC2 Spot instances are spare compute capacity in the AWS cloud available to you at steep discounts compared to On-Demand prices. EC2 Spot enables you to optimize your costs on the AWS cloud and scale your application's throughput up to 10X for the same budget. By simply selecting Spot when launching EC2 instances, you can save up-to 90% on On-Demand prices. The only difference between On-Demand instances and Spot Instances is that Spot instances can be interrupted by EC2 with two minutes of notification when the EC2 needs the capacity back.
You can specify whether Amazon EC2 should hibernate, stop, or terminate Spot Instances when they are interrupted. You can choose the interruption behavior that meets your needs.
Take note that there is no "bid price" anymore for Spot EC2 instances since March 2018. You simply have to set your maximum price instead.
Reserved instances and Dedicated instances are incorrect as both do not act as spare compute capacity.
On-demand instances is a valid option but a Spot instance is much cheaper than On-Demand.
References:
https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-interruptions.html
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/how-spot-instances-work.html
https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing
Check out this Amazon EC2 Cheat Sheet:
https://tutorialsdojo.com/amazon-elastic-compute-cloud-amazon-ec2/


NEW QUESTION # 878
A company has developed a non-production application that is composed of multiple microservices for each of the company's business units. A single development team maintains all the microservices.
The current architecture uses a static web frontend and a Java-based backend that contains the application logic. The architecture also uses a MySQL database that the company hosts on an Amazon EC2 instance.
The company needs to ensure that the application is secure and available globally.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
This solution offers the least operational overhead while meeting the security and global availability requirements:
* Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.
* AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.
* RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).
* Option A and D involve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.
* Option C suggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.
AWS References:
* Amazon S3 and CloudFront Integration
* AWS Lambda with API Gateway
* Amazon RDS for MySQL


NEW QUESTION # 879
A company needs to move data from an Amazon EC2 instance to an Amazon S3 bucket. The company must ensure that no API calls and no data are routed through public internet routes. Only the EC2 instance can have access to upload data to the S3 bucket.
Which solution will meet these requirements?

Answer: B

Explanation:
Explanation
(https://aws.amazon.com/blogs/security/how-to-restrict-amazon-s3-bucket-access-to-a-specific-iam-role/)


NEW QUESTION # 880
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