A company wants to experiment with individual AWS accounts for its engineer team. The company wants to be notified as soon as the Amazon EC2 instance usage for a given month exceeds a specific threshold for each account.
What should a solutions architect do to meet this requirement MOST cost-effectively?
A. Use Cost Explorer to create a daily report of costs by service. Filter the report by EC2 instances. Configure Cost Explorer to send an Amazon Simple Email Service (Amazon SES) notification when a threshold is exceeded. B. Use Cost Explorer to create a monthly report of costs by service. Filter the report by EC2 instances. Configure Cost Explorer to send an Amazon Simple Email Service (Amazon SES) notification when a threshold is exceeded. C. Use AWS Budgets to create a cost budget for each account. Set the period to monthly. Set the scope to EC2 instances. Set an alert threshold for the budget. Configure an Amazon Simple Notification Service (Amazon SNS) topic to receive a notification when a threshold is exceeded. D. Use AWS Cost and Usage Reports to create a report with hourly granularity. Integrate the report data with Amazon Athena. Use Amazon EventBridge to schedule an Athena query. Configure an Amazon Simple Notification Service (Amazon SNS) topic to receive a notification when a threshold is exceeded.
C. Use AWS Budgets to create a cost budget for each account. Set the period to monthly. Set the scope to EC2 instances. Set an alert threshold for the budget. Configure an Amazon Simple Notification Service (Amazon SNS) topic to receive a notification when a threshold is exceeded.
Question 172:
A company stores sensitive data in Amazon S3. A solutions architect needs to create an encryption solution. The company needs to fully control the ability of users to create, rotate, and disable encryption keys with minimal effort for any data that must be encrypted.
Which solution will meet these requirements?
A. Use default server-side encryption with Amazon S3 managed encryption keys (SSE-S3) to store the sensitive data. B. Create a customer managed key by using AWS Key Management Service (AWS KMS). Use the new key to encrypt the S3 objects by using server-side encryption with AWS KMS keys (SSE-KMS). C. Create an AWS managed key by using AWS Key Management Service (AWS KMS). Use the new key to encrypt the S3 objects by using server-side encryption with AWS KMS keys (SSE-KMS). D. Download S3 objects to an Amazon EC2 instance. Encrypt the objects by using customer managed keys. Upload the encrypted objects back into Amazon S3.
B. Create a customer managed key by using AWS Key Management Service (AWS KMS). Use the new key to encrypt the S3 objects by using server-side encryption with AWS KMS keys (SSE-KMS).
Question 173:
A company is developing a mobile game that streams score updates to a backend processor and then posts results on a leaderboard. A solutions architect needs to design a solution that can handle large traffic spikes, process the mobile game updates in order of receipt, and store the processed updates in a highly available database. The company also wants to minimize the management overhead required to maintain the solution.
What should the solutions architect do to meet these requirements?
A. Push score updates to Amazon Kinesis Data Streams. Process the updates in Kinesis Data Streams with AWS Lambda. Store the processed updates in Amazon DynamoDB. B. Push score updates to Amazon Kinesis Data Streams. Process the updates with a fleet of Amazon EC2 instances set up for Auto Scaling. Store the processed updates in Amazon Redshift. C. Push score updates to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe an AWS Lambda function to the SNS topic to process the updates. Store the processed updates in a SQL database running on Amazon EC2. D. Push score updates to an Amazon Simple Queue Service (Amazon SQS) queue. Use a fleet of Amazon EC2 instances with Auto Scaling to process the updates in the SQS queue. Store the processed updates in an Amazon RDS Multi-AZ DB instance.
A. Push score updates to Amazon Kinesis Data Streams. Process the updates in Kinesis Data Streams with AWS Lambda. Store the processed updates in Amazon DynamoDB.
Question 174:
A company is building a serverless application to process orders from an e-commerce site. The application needs to handle bursts of traffic during peak usage hours and to maintain high availability. The orders must be processed asynchronously in the order the application receives them.
Which solution will meet these requirements?.
A. Use an Amazon Simple Notification Service (Amazon SNS) topic to receive orders. Use an AWS Lambda function to process the orders. B. Use an Amazon Simple Queue Service (Amazon SQS) FIFO queue to receive orders. Use an AWS Lambda function to process the orders. C. Use an Amazon Simple Queue Service (Amazon SQS) standard queue to receive orders. Use AWS Batch jobs to process the orders. D. Use an Amazon Simple Notification Service (Amazon SNS) topic to receive orders. Use AWS Batch jobs to process the orders.
B. Use an Amazon Simple Queue Service (Amazon SQS) FIFO queue to receive orders. Use an AWS Lambda function to process the orders.
Explanation
Key Requirements:
Serverless architecture.
Handle traffic bursts with high availability.
Process orders asynchronouslyin the order they are received.
Analysis of Options:
Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.
Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once.
AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently.
This satisfies all requirements.
Option C:Amazon SQS standard queues do not guarantee message order and have " at-least-once " delivery, making them unsuitable for the FIFO requirement.
Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.
References:
Amazon SQS FIFO Queues
AWS Lambda and SQS Integration
Question 175:
An ecommerce company is redesigning a web application to run on the AWS Cloud. The application needs to store static website content and must use a Microsoft SQL Server database to store customer data. The company needs to deploy the application in a resilient way across multiple Availability Zones.
Which solution will meet these requirements?
A. Use an Amazon S3 bucket to store static content. Deploy an Amazon RDS Custom for SQL Server DB instance for the database. B. Use an Amazon S3 bucket to store static content. Create an Amazon RDS for SQL Server Multi-AZ deployment for the database. C. Create an Amazon Elastic Block Store (Amazon EBS) Multi-Attach volume to store static content. Deploy an Amazon RDS for SQL Server DB instance for the database. D. Create an Amazon Elastic Block Store (Amazon EBS) Multi-Attach volume to store static content. Deploy SQL Server on two Amazon EC2 instances in separate Availability Zones.
B. Use an Amazon S3 bucket to store static content. Create an Amazon RDS for SQL Server Multi-AZ deployment for the database.
Explanation
To achieve resilience across multiple Availability Zones, the architecture should use services that natively provide Multi-AZ durability and failover with minimal manual intervention. For static website content, Amazon S3 is the natural choice because it stores objects durably and is designed for high availability; it also integrates well with common web delivery patterns. For the database, the requirement is explicitly Microsoft SQL Server and resilience across AZs.
An Amazon RDS for SQL Server Multi-AZ deployment (Option B) is designed to provide high availability by maintaining a standby replica in a different Availability Zone and performing automated failover in certain failure scenarios. This meets the Multi-AZ resiliency requirement while reducing operational overhead compared to self-managed database clustering on EC2.
Option A uses RDS Custom for SQL Server, which is intended for scenarios where you need OS-level and database-level access/customization. That typically increases operational responsibility and is not the simplest "resilient Multi-AZ" answer unless the scenario demands deep customization (it does not here).
Options C and D propose using EBS Multi-Attach for static content, which is not a good fit for static website assets and adds complexity; Multi-Attach is limited and does not replace object storage semantics. Option
D also adds substantial operational overhead by running and managing SQL Server across EC2 instances in multiple AZs, including patching, backups, and HA configuration.
Therefore, B best meets all requirements: S3 for static object content and RDS for SQL Server Multi-AZ for resilient, managed database high availability across Availability Zones.
Question 176:
An analytics application runs on multiple Amazon EC2 Linux instances that use Amazon Elastic File System (Amazon EFS) Standard storage. Files are accessed infrequently after 30 days, but some older files are occasionally retrieved for reporting.
The company wants to reduce storage costs and allow throughput to scale based on file system size. The company will use the EFS lifecycle policy to transition files to Infrequent Access (IA) after 30 days.
Which solution will meet these requirements?
A. Configure files to transition back to Standard storage on access. Specify provisioned throughput mode. B. Specify the provisioned throughput mode only. C. Configure files to transition back to Standard storage on access. Specify bursting throughput mode. D. Specify the bursting throughput mode only.
C. Configure files to transition back to Standard storage on access. Specify bursting throughput mode.
Explanation
The requirements focus on cost optimization, automatic lifecycle management, and throughput scaling based on file system size. Amazon EFS provides two throughput modes: bursting and provisioned.
Bursting throughput automatically scales with the size of the file system, which directly aligns with the company's needs.
Option C is correct because it combines two essential features. First, configuring lifecycle management to transition files from Standard to Infrequent Access (IA) after 30 days reduces storage costs for infrequently accessed data. Second, enabling automatic transition back to Standard storage upon access ensures that performance-sensitive reporting workloads are not negatively affected when older files are read.
Using bursting throughput mode allows throughput to grow naturally as the file system size increases, eliminating the need to manually manage or pay for fixed throughput levels. This is ideal for workloads with variable access patterns.
Option A incorrectly specifies provisioned throughput, which is designed for predictable, high-throughput workloads and incurs additional cost.
Option B ignores lifecycle transitions.
Option D does not account for automatic return to Standard storage, which could lead to suboptimal performance for accessed files.
Therefore, C provides the best balance of performance, cost efficiency, and minimal operational overhead.
Question 177:
A company has an application that processes customer orders. The company hosts the application on an Amazon EC2 instance that saves the orders to an Amazon Aurora database. Occasionally when traffic is high the workload does not process orders fast enough.
What should a solutions architect do to write the orders reliably to the database as quickly as possible?
A. Increase the instance size of the EC2 instance when traffic is high. Write orders to Amazon Simple Notification Service (Amazon SNS). Subscribe the database endpoint to the SNS topic. B. Write orders to an Amazon Simple Queue Service (Amazon SQS) queue. Use EC2 instances in an Auto Scaling group behind an Application Load Balancer to read from the SQS queue and process orders into the database. C. Write orders to Amazon Simple Notification Service (Amazon SNS). Subscribe the database endpoint to the SNS topic. Use EC2 instances in an Auto Scaling group behind an Application Load Balancer to read from the SNS topic. D. Write orders to an Amazon Simple Queue Service (Amazon SQS) queue when the EC2 instance reaches CPU threshold limits. Use scheduled scaling of EC2 instances in an Auto Scaling group behind an Application Load Balancer to read from the SQS queue and process orders into the database.
B. Write orders to an Amazon Simple Queue Service (Amazon SQS) queue. Use EC2 instances in an Auto Scaling group behind an Application Load Balancer to read from the SQS queue and process orders into the database.
Question 178:
A company is developing software that uses a PostgreSQL database schema. The company needs to configure development environments and test environments for its developers. Each developer at the company uses their own development environment, which includes a PostgreSQL database. On average, each development environment is used for an 8-hour workday. The test environments are used for load testing that can take up to 2 hours each day.
Which solution will meet these requirements MOST cost-effectively?
A. Configure the development environments and test environments with their own Amazon Aurora Serverless v2 PostgreSQL-compatible databases. B. For each development environment, configure an Amazon RDS for PostgreSQL Single-AZ DB instance. For the test environment, configure a single Amazon RDS for PostgreSQL Multi-AZ DB instance. C. Configure the development environments and test environments with their own Amazon Aurora PostgreSQL DB clusters. D. Configure an Amazon Aurora global database. Allow developers to connect to the database by using their own credentials.
A. Configure the development environments and test environments with their own Amazon Aurora Serverless v2 PostgreSQL-compatible databases.
You pay only for actual usage, making it ideal for development and test environments that are not used continuously. It supports PostgreSQL and is suitable for variable and short-duration workloads, minimizing costs when databases are idle.
AWS Documentation Extract:
"Amazon Aurora Serverless v2 automatically adjusts database capacity based on application needs, ideal for development and test environments with intermittent usage." (Source: Aurora Serverless v2 documentation)
Option B: RDS instances run and accrue charges even when idle. Option C, Option D: Aurora clusters/ global databases are overkill and incur higher costs for dev/test.
References:
AWS Certified Solutions Architect?Official Study Guide, Aurora Serverless for Cost Optimization.
Question 179:
A financial services company must retain log data for 1 year. The company stores log files in an Amazon
S3 bucket and wants to prevent any user from deleting or overwriting the log files during this period. The data must remain available for read-only requests.
Which solution will meet these requirements?
A. Enable S3 Versioning on the bucket. Use Object Lock in compliance mode with a 1-year retention period. B. Enable S3 Transfer Acceleration on the bucket. Create an S3 Lifecycle Configuration rule to move objects to Amazon S3 Glacier Flexible Retrieval after 1 year. C. Enable S3 Versioning on the bucket. Create an S3 Lifecycle Configuration rule to move objects to Amazon S3 Glacier Flexible Retrieval after 1 year. D. Create an AWS Lambda function to programmatically check the timestamp of S3 data and to move the data to Amazon S3 Glacier Deep Archive if the data is older than 1 year.
A. Enable S3 Versioning on the bucket. Use Object Lock in compliance mode with a 1-year retention period.
Explanation
To ensure that log files are immutable and cannot be deleted or overwritten for a specified retention period, Amazon S3 offers Object Lock in Compliance Mode. When enabled:
Compliance Mode: Ensures that a protected object version can ' t be overwritten or deleted by any user, including the root user in your AWS account, for the duration of the retention period. AWS Documentation
S3 Versioning: Must be enabled on the bucket to use Object Lock. This allows multiple versions of an object to be stored, ensuring that previous versions are preserved and protected. Amazon Web Services, Inc.
By enabling S3 Versioning and applying Object Lock in Compliance Mode with a 1-year retention period, the company can meet regulatory requirements to retain log data securely and prevent any modifications or deletions during that time.
Question 180:
A company wants to build a map of its IT infrastructure to identify and enforce policies on resources that pose security risks. The company's security team must be able to query data in the IT infrastructure map and quickly identify security risks.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon RDS to store the data. Use SQL to query the data to identify security risks. B. Use Amazon Neptune to store the data. Use SPARQL to query the data to identify security risks. C. Use Amazon Redshift to store the data. Use SQL to query the data to identify security risks. D. Use Amazon DynamoDB to store the data. Use PartiQL to query the data to identify security risks.
B. Use Amazon Neptune to store the data. Use SPARQL to query the data to identify security risks.
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