SAP-C02 Premium Exam Questions

AWS Certified Solutions Architect - Professional Questions and Answers

Question 33

A flood monitoring agency has deployed more than 10.000 water-level monitoring sensors. Sensors send continuous data updates, and each update is less than 1 MB in size. The agency has a fleet of on-premises application servers. These servers receive upda.es ' on the sensors, convert the raw data into a human readable format, and write the results loan on-premises relational database server. Data analysts then use simple SOL queries to monitor the data.

The agency wants to increase overall application availability and reduce the effort that is required to perform maintenance tasks These maintenance tasks, which include updates and patches to the application servers, cause downtime. While an application server is down, data is lost from sensors because the remaining servers cannot handle the entire workload.

The agency wants a solution that optimizes operational overhead and costs. A solutions architect recommends the use of AWS loT Core to collect the sensor data.

What else should the solutions architect recommend to meet these requirements?

Options:

Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to .csv format, and insert it into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.

Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to Apache Parquet format and save it to an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.

Send the sensor data to an Amazon Managed Service for Apache Flink {previously known as Amazon Kinesis Data Analytics) application to convert the data to .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.

Send the sensor data to an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to convert the data to Apache Parquet format and store it in an Amazon S3 bucket Instruct the data analysis to query the data by using Amazon Athena.

Question 34

A company is using AWS Organizations to manage multiple accounts Due to regulatory requirements, the company wants to restrict specific member accounts to certain AWS Regions, where they are permitted to deploy resources The resources in the accounts must be tagged enforced based on a group standard and centrally managed with minimal configuration.

What should a solutions architect do to meet these requirements ' ?

Options:

Create an AWS Config rule in the specific member accounts to limit Regions and apply a tag policy.

From the AWS Billing and Cost Management console in the management account, disable Regions for the specific member accounts and apply a tag policy on the root.

Associate the specific member accounts with the root Apply a tag policy and an SCP using conditions to limit Regions.

Associate the specific member accounts with a new OU. Apply a tag policy and an SCP using conditions to limit Regions.

Question 35

A company that provisions job boards for a seasonal workforce is seeing an increase in traffic and usage. The backend services run on a pair of Amazon EC2 instances behind an Application Load Balancer with Amazon DynamoDB as the datastore. Application read and write traffic is slow during peak seasons.

Which option provides a scalable application architecture to handle peak seasons with the LEAST development effort?

Options:

Migrate the backend services to AWS Lambda. Increase the read and write capacity of DynamoDB.

Migrate the backend services to AWS Lambda. Configure DynamoDB to use global tables.

Use Auto Scaling groups for the backend services. Use DynamoDB auto scaling.

Use Auto Scaling groups for the backend services. Use Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB.

Answer:

Explanation:

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

Question 36

A company is running an application in the AWS Cloud. The application uses AWS Lambda functions and Amazon Elastic Container Service (Amazon ECS) containers that run with AWS Fargate technology as its primary compute. The load on the application is irregular. The application experiences long periods of no usage, followed by sudden and significant increases and decreases in traffic. The application is write-heavy and stores data in an Amazon Aurora MySQL database. The database runs on an Amazon RDS memory optimized DB instance that is not able to handle the load.

What is the MOST cost-effective way for the company to handle the sudden and significant changes in traffic?

Options:

Add additional read replicas to the database. Purchase Instance Savings Plans and RDS Reserved Instances.

Migrate the database to an Aurora multi-master DB cluster. Purchase Instance Savings Plans.

Migrate the database to an Aurora global database. Purchase Compute Savings Plans and RDS Reserved Instances.

Migrate the database to Aurora Serverless v1. Purchase Compute Savings Plans.

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AWS Certified Solutions Architect - Professional Questions and Answers

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CompTIA

Fortinet

Microsoft

Salesforce