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The SAA-C03 Certification Exam is a multiple-choice exam that consists of 65 questions. Candidates have 130 minutes to complete the exam. SAA-C03 exam covers a range of topics including AWS services, architecture, and best practices. Candidates are required to demonstrate their knowledge of AWS services and their ability to design and deploy scalable, highly available, and fault-tolerant systems on AWS.
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Amazon SAA-C03 Exam consists of multiple-choice and multiple-response questions. SAA-C03 exam covers a wide variety of topics, including AWS core services, security, database, networking, compute, and storage. Candidates are required to have a deep understanding of AWS services and their integration with each other to create solutions that meet specific business needs. In addition, candidates are expected to have experience with AWS best practices and the ability to identify and resolve common issues that arise when working with AWS.
Amazon SAA-C03 Certification Exam is a highly sought-after certification that validates the skills and knowledge of professionals who work with Amazon Web Services (AWS). It is designed for individuals who have experience in designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Associate certification exam tests the candidate's ability to architect and deploy secure and robust applications on AWS, and it covers a wide range of topics, including AWS core services, security, database technologies, and networking.
NEW QUESTION # 464
A company is migrating its databases to Amazon RDS for PostgreSQL. The company is migrating its applications to Amazon EC2 instances. The company wants to optimize costs for long-running workloads. Which solution will meet this requirement MOST cost-effectively?
Answer: C
NEW QUESTION # 465
A company is deploying a critical application by using Amazon RDS for MySQL. The application must be highly available and must recover automatically. The company needs to support interactive users (transactional queries) and batch reporting (analytical queries) with no more than a 4-hour lag. The analytical queries must not affect the performance of the transactional queries.
Which solution will meet these requirements?
Answer: C
Explanation:
The requirement has three key elements: high availability with automatic recovery, separation of transactional and analytical workloads, and acceptable reporting lag up to 4 hours. The clean AWS-native pattern for isolating heavy read/reporting traffic from transactional writes in RDS for MySQL is to use read replicas and direct reporting queries to the replica endpoint(s).
Option C meets these requirements well. The primary RDS for MySQL instance handles transactional traffic (reads/writes). One or more RDS read replicas asynchronously replicate data from the primary. Because replication is asynchronous, some lag is expected; the requirement explicitly tolerates up to a 4-hour lag, which fits the read replica model. Directing batch reporting and analytical queries to a replica prevents those expensive queries from consuming CPU, memory, and I/O on the primary, thereby protecting interactive user performance. Deploying replicas across multiple Availability Zones also improves availability of the reporting tier and reduces the risk that an AZ issue prevents reporting access.
Option A is incorrect because a standard Multi-AZ DB instance uses a synchronous standby that is not readable and is intended for failover, not for serving analytical queries. Option B describes a Multi-AZ DB cluster deployment, which does provide readable standbys in some RDS engines; however, for the classic RDS MySQL exam pattern, the most direct and widely used method to isolate analytics is read replicas.
Option D creates a nightly snapshot-based read-only copy; this increases operational complexity, can result in stale data for most of the day, and introduces provisioning delays, which is unnecessary when replicas provide continuous near-real-time copies.
Therefore, C is the best fit because it provides HA for the primary, isolates reporting reads to replicas, and meets the allowed replication lag requirement.
NEW QUESTION # 466
A company runs an application on Amazon EC2 instances that have instance store volumes attached. The application uses Amazon Elastic File System (Amazon EFS) to store files that are shared across a cluster of Linux servers. The shared files are at least 1 GB in size.
The company accesses the files often for the first 7 days after creation. The files must remain readily available after the first 7 days.
The company wants to optimize costs for the application.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven't been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).
"Amazon EFS Lifecycle Management automatically moves files that haven't been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files."
- Amazon EFS Documentation
Key Points:
* EFS IA is ideal for files larger than 128 KB and accessed less frequently.
* It's seamless - no code or tools needed.
* Meets requirement for cost optimization and high availability.
Incorrect Options:
* A: File Gateway adds unnecessary complexity and does not use EFS.
* B: Storing files locally breaks shared access and resiliency.
* D: Glacier Deep Archive is cold storage - not "readily available."
References:
EFS Lifecycle Management
EFS IA Storage Class
NEW QUESTION # 467
A company's application integrates with multiple software-as-a-service (SaaS) sources for data collection. The company runs Amazon EC2 instances to receive the data and to upload the data to an Amazon S3 bucket for analysis. The same EC2 instance that receives and uploads the data also sends a notification to the user when an upload is complete. The company has noticed slow application performance and wants to improve the performance as much as possible.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Explanation
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, SAP, Zendesk, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. https://aws.amazon.com/appflow/
NEW QUESTION # 468
A popular augmented reality (AR) mobile game is heavily using a RESTful API which is hosted in AWS. The API uses Amazon API Gateway and a DynamoDB table with a preconfigured read and write capacity. Based on your systems monitoring, the DynamoDB table begins to throttle requests during high peak loads which causes the slow performance of the game.
Which of the following can you do to improve the performance of your app?
Answer: B
Explanation:
DynamoDB auto scaling uses the AWS Application Auto Scaling service to dynamically adjust provisioned throughput capacity on your behalf, in response to actual traffic patterns. This enables a table or a global secondary index to increase its provisioned read and write capacity to handle sudden increases in traffic, without throttling. When the workload decreases, Application Auto Scaling decreases the throughput so that you don't pay for unused provisioned capacity.
Using DynamoDB Auto Scaling is the best answer. DynamoDB Auto Scaling uses the AWS Application Auto Scaling service to dynamically adjust provisioned throughput capacity on your behalf.
Integrating an Application Load Balancer with your DynamoDB table is incorrect because an Application Load Balancer is not suitable to be used with DynamoDB and in addition, this will not increase the throughput of your DynamoDB table.
Adding the DynamoDB table to an Auto Scaling Group is incorrect because you usually put EC2 instances on an Auto Scaling Group, and not a DynamoDB table.
Creating an SQS queue in front of the DynamoDB table is incorrect because this is not a design principle for high throughput DynamoDB table. Using SQS is for handling queuing and polling the request. This will not increase the throughput of DynamoDB which is required in this situation.
Reference:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html Check out this Amazon DynamoDB Cheat Sheet: https://tutorialsdojo.com/amazon-dynamodb/ Amazon DynamoDB Overview:
https://www.youtube.com/watch?v=3ZOyUNIeorU
NEW QUESTION # 469
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