SAA-C03 practice questions 1,011–1,017

1. Question 1011 · 1017 in bank

   A solutions architect is creating an application that will handle batch processing of large amounts of data. The input data will be held in Amazon S3 and the output data will be stored in a different S3 bucket. For processing, the application will transfer the data over the network between multiple Amazon EC2 instances. What should the solutions architect do to reduce the overall data transfer costs?

   • APlace all the EC2 instances in an Auto Scaling group.
   • BPlace all the EC2 instances in the same AWS Region.
   • CPlace all the EC2 instances in the same Availability Zone.
   • DPlace all the EC2 instances in private subnets in multiple Availability Zones.

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   Answer: C

   Explanation

   K. After research it seems C make sense since it is saying data transfer between EC2 and data transfter between two EC2 in AZ is free but not between AZ's. No cost within AZ: Data transfer between EC2 instances in the same AZ is free. Cross-AZ charges: If you transfer data between instances in different Availability Zones, you will be charged for the inter-zone data transfer. Region-based pricing: While data transfer within an AZ is free, data transfer across different regions may also incur charges

   Source: Adapted from community discussion — ckhemani

   References

   • AWS documentation

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2. Question 1012 · 1017 in bank

   A company hosts a multi-tier web application that uses an Amazon Aurora MySQL DB cluster for storage. The application tier is hosted on Amazon EC2 instances. The company's IT security guidelines mandate that the database credentials be encrypted and rotated every 14 days. What should a solutions architect do to meet this requirement with the LEAST operational effort?

   • ACreate a new AWS Key Management Service (AWS KMS) encryption key. Use AWS Secrets Manager to create a new secret that uses the KMS key with the appropriate credentials. Associate the secret with the Aurora DB cluster. Configure a custom rotation period of 14 days.
   • BCreate two parameters in AWS Systems Manager Parameter Store: one for the user name as a string parameter and one that uses the SecureString type for the password. Select AWS Key Management Service (AWS KMS) encryption for the password parameter, and load these parameters in the application tier. Implement an AWS Lambda function that rotates the password every 14 days.
   • CStore a file that contains the credentials in an AWS Key Management Service (AWS KMS) encrypted Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system in all EC2 instances of the application tier. Restrict the access to the file on the file system so that the application can read the file and that only super users can modify the file. Implement an AWS Lambda function that rotates the key in Aurora every 14 days and writes new credentials into the file.
   • DStore a file that contains the credentials in an AWS Key Management Service (AWS KMS) encrypted Amazon S3 bucket that the application uses to load the credentials. Download the file to the application regularly to ensure that the correct credentials are used. Implement an AWS Lambda function that rotates the Aurora credentials every 14 days and uploads these credentials to the file in the S3 bucket.

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   Answer: A

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3. Question 1013 · 1017 in bank

   A streaming media company is rebuilding its infrastructure to accommodate increasing demand for video content that users consume daily. The company needs to process terabyte-sized videos to block some content in the videos. Video processing can take up to 20 minutes. The company needs a solution that will scale with demand and remain cost-effective. Which solution will meet these requirements?

   • AUse AWS Lambda functions to process videos. Store video metadata in Amazon DynamoDB. Store video content in Amazon S3 Intelligent-Tiering.
   • BUse Amazon Elastic Container Service (Amazon ECS) and AWS Fargate to implement microservices to process videos. Store video metadata in Amazon Aurora. Store video content in Amazon S3 Intelligent-Tiering.
   • CUse Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB) to process videos. Store video content in Amazon S3 Standard. Use Amazon Simple Queue Service (Amazon SQS) for queuing and to decouple processing tasks.
   • DDeploy a containerized video processing application on Amazon Elastic Kubernetes Service (Amazon EKS) on Amazon EC2. Store video metadata in Amazon RDS in a single Availability Zone. Store video content in Amazon S3 Glacier Deep Archive.

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   Answer: B

   Explanation

   A - Video processing can take up to 20 minutes. So lambda is not suitable here. B - Fargate allows running containers without managing EC2 instances, scaling automatically based on demand. Besides, S3 Intelligent-Tiering is cost-effective for storing large video files. C - I gotta say, the SQS is a really good choice here! It's just that S3 Standard is more expensive than S3 Intelligent-Tiering for large files, and that EC2 (even with Auto Scaling) requires managing the underlying infrastructure. D - EKS is more complex to set up but the real issue is that RDS in a single Availability Zone introduces a single point of failure, so scalability and reliability can't be met. Also, S3 Glacier Deep Archive is designed for long-term archival storage with high latency and not suitable for frequently accessed video content.

   Source: Adapted from community discussion — LeonSauveterre

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4. Question 1014 · 1017 in bank

   A company runs an on-premises application on a Kubernetes cluster. The company recently added millions of new customers. The company's existing on-premises infrastructure is unable to handle the large number of new customers. The company needs to migrate the on-premises application to the AWS Cloud. The company will migrate to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The company does not want to manage the underlying compute infrastructure for the new architecture on AWS. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse a self-managed node to supply compute capacity. Deploy the application to the new EKS cluster.
   • BUse managed node groups to supply compute capacity. Deploy the application to the new EKS cluster.
   • CUse AWS Fargate to supply compute capacity. Create a Fargate profile. Use the Fargate profile to deploy the application.
   • DUse managed node groups with Karpenter to supply compute capacity. Deploy the application to the new EKS cluster.

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   Answer: C

   Explanation

   "The company does not want to manage the underlying compute infrastructure for the new architecture on AWS" => Serverless => Fargate

   Source: Adapted from community discussion — Razvan_C

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5. Question 1015 · 1017 in bank

   A company is launching a new application that requires a structured database to store user profiles, application settings, and transactional data. The database must be scalable with application traffic and must offer backups. Which solution will meet these requirements MOST cost-effectively?

   • ADeploy a self-managed database on Amazon EC2 instances by using open source software. Use Spot Instances for cost optimization. Configure automated backups to Amazon S3.
   • BUse Amazon RDS. Use on-demand capacity mode for the database with General Purpose SSD storage. Configure automatic backups with a retention period of 7 days.
   • CUse Amazon Aurora Serverless for the database. Use serverless capacity scaling. Configure automated backups to Amazon S3.
   • DDeploy a self-managed NoSQL database on Amazon EC2 instances. Use Reserved Instances for cost optimization. Configure automated backups directly to Amazon S3 Glacier Flexible Retrieval.

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   Answer: C

   Explanation

   A - Spot Instance is never a good idea for databases. These instances can be terminated with little notice, while databases are almost always persistent workloads. B - On-Demand Capacity is suitable for steady or predictable workloads, but not as cost-efficient as Aurora Serverless for spiky or unpredictable workloads. C - Aurora Serverless is a fully managed relational database that scales automatically with traffic. Automated Backups is also integrated with Aurora. Good! D - We need a structured database, not NoSQL database.

   Source: Adapted from community discussion — LeonSauveterre

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6. Question 1016 · 1017 in bank

   A company runs its legacy web application on AWS. The web application server runs on an Amazon EC2 instance in the public subnet of a VPC. The web application server collects images from customers and stores the image files in a locally attached Amazon Elastic Block Store (Amazon EBS) volume. The image files are uploaded every night to an Amazon S3 bucket for backup. A solutions architect discovers that the image files are being uploaded to Amazon S3 through the public endpoint. The solutions architect needs to ensure that traffic to Amazon S3 does not use the public endpoint. Which solution will meet these requirements?

   • ACreate a gateway VPC endpoint for the S3 bucket that has the necessary permissions for the VPC. Configure the subnet route table to use the gateway VPC endpoint.
   • BMove the S3 bucket inside the VPC. Configure the subnet route table to access the S3 bucket through private IP addresses.
   • CCreate an Amazon S3 access point for the Amazon EC2 instance inside the VPConfigure the web application to upload by using the Amazon S3 access point.
   • DConfigure an AWS Direct Connect connection between the VPC that has the Amazon EC2 instance and Amazon S3 to provide a dedicated network path.

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   Answer: A

   Explanation

   A - Gateway VPC Endpoint provides private connectivity between resources in a VPC and AWS services like S3 without requiring an internet gateway, NAT gateway, or public IP address. B - This is just bull. Amazon S3 is a global service that cannot "move" into a VPC. C - This doesn't ensure private traffic between the EC2 instance and S3 at all. You still need VPC Endpoint. D - This may actually work but more expensive and complex compared to option A.

   Source: Adapted from community discussion — LeonSauveterre

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7. Question 1017 · 1017 in bank

   A company is creating a prototype of an ecommerce website on AWS. The website consists of an Application Load Balancer, an Auto Scaling group of Amazon EC2 instances for web servers, and an Amazon RDS for MySQL DB instance that runs with the Single-AZ configuration. The website is slow to respond during searches of the product catalog. The product catalog is a group of tables in the MySQL database that the company does not update frequently. A solutions architect has determined that the CPU utilization on the DB instance is high when product catalog searches occur. What should the solutions architect recommend to improve the performance of the website during searches of the product catalog?

   • AMigrate the product catalog to an Amazon Redshift database. Use the COPY command to load the product catalog tables.
   • BImplement an Amazon ElastiCache for Redis cluster to cache the product catalog. Use lazy loading to populate the cache.
   • CAdd an additional scaling policy to the Auto Scaling group to launch additional EC2 instances when database response is slow.
   • DTurn on the Multi-AZ configuration for the DB instance. Configure the EC2 instances to throttle the product catalog queries that are sent to the database.

   Show answer

   Answer: B

   Explanation

   A - For transactional or low-latency queries like product catalog searches in an ecommerce website, Redshift is not suitable because it's a data warehouse solution optimized for analytical queries. B - ElastiCache for Redis is a highly performant, in-memory caching service that can significantly reduce database load by caching frequent queries. C - Adding more web servers can't help alleviate the load on the database. The database remains the bottleneck for product catalog queries. D - If you turn on throttle queries, customers may gradually ask for a refund :) Let's get rid of the bad things in database instead of user experience. BTW, what Lazy Loading means: Data is added to the cache only when requested. If not found in the cache, it is fetched from the database and added to the cache for subsequent requests.

   Source: Adapted from community discussion — LeonSauveterre

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SAA-C03 practice, question 1011. 1017 questions available.