SAP-C02 practice questions 441–450

1. Question 441 · 527 in bank

   A company needs to move some on-premises Oracle databases to AWS. The company has chosen to keep some of the databases on premises for business compliance reasons. The on-premises databases contain spatial data and run cron jobs for maintenance. The company needs to connect to the on-premises systems directly from AWS to query data as a foreign table. Which solution will meet these requirements?

   • ACreate Amazon DynamoDB global tables with auto scaling enabled. Use the AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) to move the data from on premises to DynamoDB. Create an AWS Lambda function to move the spatial data to Amazon S3. Query the data by using Amazon Athena. Use Amazon EventBridge to schedule jobs in DynamoDB for maintenance. Use Amazon API Gateway for foreign table support.
   • BCreate an Amazon RDS for Microsoft SQL Server DB instance. Use native replication to move the data from on premises to the DB instance. Use the AWS Schema Conversion Tool (AWS SCT) to modify the SQL Server schema as needed after replication. Move the spatial data to Amazon Redshift. Use stored procedures for system maintenance. Create AWS Glue crawlers to connect to the on-premises Oracle databases for foreign table support.
   • CLaunch Amazon EC2 instances to host the Oracle databases. Place the EC2 instances in an Auto Scaling group. Use AWS Application Migration Service to move the data from on premises to the EC2 instances and for real-time bidirectional change data capture (CDC) synchronization. Use Oracle native spatial data support. Create an AWS Lambda function to run maintenance jobs as part of an AWS Step Functions workflow. Create an internet gateway for foreign table support.
   • DCreate an Amazon RDS for PostgreSQL DB instance. Use the AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) to move the data from on premises to the DB instance. Use PostgreSQL native spatial data support. Run cron jobs on the DB instance for maintenance. Use AWS Direct Connect to connect the DB instance to the on-premises environment for foreign table support.

   Show answer

   Answer: D

   Explanation

   PostgreSQL natively supports spatial data through the PostGIS extension. This makes it well-suited for handling spatial data from the Oracle databases. AWS Schema Conversion Tool (SCT) and AWS Database Migration Service (DMS) are both effective tools for migrating schema and data from Oracle to Amazon RDS for PostgreSQL, ensuring minimal disruption during the migration process. Amazon RDS for PostgreSQL allows the use of cron jobs directly on the instance through extensions like pg_cron for scheduling maintenance tasks. AWS Direct Connect provides a dedicated, secure connection between the on-premises systems and the AWS environment. This low-latency link will allow querying data from on-premises Oracle databases as foreign tables in the PostgreSQL instance without going through the internet, which supports compliance and performance needs.

   Source: Adapted from community discussion — JoeTromundo

   References

   • AWS documentation

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2. Question 442 · 527 in bank

   Accompany runs an application on Amazon EC2 and AWS Lambda. The application stores temporary data in Amazon S3. The S3 objects are deleted after 24 hours. The company deploys new versions of the application by launching AWS CloudFormation stacks. The stacks create the required resources. After validating a new version, the company deletes the old stack. The deletion of an old development stack recently failed. A solutions architect needs to resolve this issue without major architecture changes. Which solution will meet these requirements?

   • ACreate a Lambda function to delete objects from an S3 bucket. Add the Lambda function as a custom resource in the CloudFormation stack with a DependsOn attribute that points to the S3 bucket resource.
   • BModify the CloudFormation stack to attach a DeletionPolicy attribute with a value of Delete to the S3 bucket.
   • CUpdate the CloudFormation stack to add a DeletionPolicy attribute with a value of Snapshot for the S3 bucket resource
   • DUpdate the CloudFormation template to create an Amazon Elastic File System (Amazon EFS) file system to store temporary files instead of Amazon S3. Configure the Lambda functions to run in the same VPC as the EFS file system.

   Show answer

   Answer: A

   Explanation

   A, for sure. DeletionPolicy: Delete: The DeletionPolicy attribute in CloudFormation is used to specify what should happen to a resource when the stack is deleted. The value Delete indicates that CloudFormation should delete the resource (in this case, the S3 bucket) when the stack is deleted. Non-Empty Buckets: The problem with this approach is that CloudFormation cannot delete an S3 bucket if it contains any objects. The DeletionPolicy: Delete does not change this behavior; it only specifies that the bucket should be deleted, which will still fail if the bucket is not empty.

   Source: Adapted from community discussion — gfhbox0083

   References

   • repost.aws documentation

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3. Question 443 · 527 in bank

   Select 2

   A company has an application that stores user-uploaded videos in an Amazon S3 bucket that uses S3 Standard storage. Users access the videos frequently in the first 180 days after the videos are uploaded. Access after 180 days is rare. Named users and anonymous users access the videos. Most of the videos are more than 100 MB in size. Users often have poor internet connectivity when they upload videos, resulting in failed uploads. The company uses multipart uploads for the videos. A solutions architect needs to optimize the S3 costs of the application. Which combination of actions will meet these requirements? (Choose two.)

   • AConfigure the S3 bucket to be a Requester Pays bucket.
   • BUse S3 Transfer Acceleration to upload the videos to the S3 bucket.
   • CCreate an S3 Lifecycle configuration o expire incomplete multipart uploads 7 days after initiation.
   • DCreate an S3 Lifecycle configuration to transition objects to S3 Glacier Instant Retrieval after 1 day.
   • ECreate an S3 Lifecycle configuration to transition objects to S3 Standard-infrequent Access (S3 Standard- IA) after 180 days.

   Show answer

   Answer: CE

   Explanation

   C: Incomplete multipart uploads consume unnecessary storage and increase costs, especially with large files like videos. Setting a lifecycle policy to automatically delete incomplete uploads after 7 days helps reduce unnecessary storage costs without manual intervention. E: The videos are frequently accessed in the first 180 days and rarely accessed after that. Transitioning the videos to S3 Standard-IA after 180 days reduces costs while still providing immediate retrieval when needed. S3 Standard-IA is designed for infrequently accessed data and offers lower storage costs than S3 Standard while maintaining fast access times. Why not B? S3 Transfer Acceleration improves upload speeds for users with poor internet connectivity, but it INCURS ADDITIONAL COSTS. While this can help with uploads, it does NOT directly optimize STORAGE COSTS, which is the main goal here.

   Source: Adapted from community discussion — JoeTromundo

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4. Question 444 · 527 in bank

   A company uses a service to collect metadata from applications that the company hosts on premises. Consumer devices such as TVs and internet radios access the applications. Many older devices do not support certain HTTP headers and exhibit errors when these headers are present in responses. The company has configured an on-premises load balancer to remove the unsupported headers from responses sent to older devices, which the company identified by the User-Agent headers. The company wants to migrate the service to AWS, adopt serverless technologies, and retain the ability to support the older devices. The company has already migrated the applications into a set of AWS Lambda functions. Which solution will meet these requirements?

   • ACreate an Amazon CloudFront distribution for the metadata service. Create an Application Load Balancer (ALB). Configure the CloudFront distribution to forward requests to the ALB. Configure the ALB to invoke the correct Lambda function for each type of request. Create a CloudFront function to remove the problematic headers based on the value of the User-Agent header.
   • BCreate an Amazon API Gateway REST API for the metadata service. Configure API Gateway to invoke the correct Lambda function for each type of request. Modify the default gateway responses to remove the problematic headers based on the value of the User-Agent header.
   • CCreate an Amazon API Gateway HTTP API for the metadata service. Configure API Gateway to invoke the correct Lambda function for each type of request. Create a response mapping template to remove the problematic headers based on the value of the User-Agent. Associate the response data mapping with the HTTP API.
   • DCreate an Amazon CloudFront distribution for the metadata service. Create an Application Load Balancer (ALB). Configure the CloudFront distribution to forward requests to the ALB. Configure the ALB to invoke the correct Lambda function for each type of request. Create a Lambda@Edge function that will remove the problematic headers in response to viewer requests based on the value of the User-Agent header.

   Show answer

   Answer: A

   Explanation

   A. The only difference between A and D is CloudFront function vs Lambda@Edge. In this case the CloudFront function can remove the response header based on request header and much faster/light-weight.

   Source: Adapted from community discussion — EricZhang

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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5. Question 445 · 527 in bank

   A company wants to migrate its data analytics environment from on premises to AWS. The environment consists of two simple Node.js applications. One of the applications collects sensor data and loads it into a MySQL database. The other application aggregates the data into reports. When the aggregation jobs run, some of the load jobs fail to run correctly. The company must resolve the data loading issue. The company also needs the migration to occur without interruptions or changes for the company’s customers. What should a solutions architect do to meet these requirements?

   • ASet up an Amazon Aurora MySQL database as a replication target for the on-premises database. Create an Aurora Replica for the Aurora MySQL database, and move the aggregation jobs to run against the Aurora Replica. Set up collection endpoints as AWS Lambda functions behind a Network Load Balancer (NLB), and use Amazon RDS Proxy to write to the Aurora MySQL database. When the databases are synced, disable the replication job and restart the Aurora Replica as the primary instance. Point the collector DNS record to the NLB.
   • BSet up an Amazon Aurora MySQL database. Use AWS Database Migration Service (AWS DMS) to perform continuous data replication from the on-premises database to Aurora. Move the aggregation jobs to run against the Aurora MySQL database. Set up collection endpoints behind an Application Load Balancer (ALB) as Amazon EC2 instances in an Auto Scaling group. When the databases are synced, point the collector DNS record to the ALDisable the AWS DMS sync task after the cutover from on premises to AWS.
   • CSet up an Amazon Aurora MySQL database. Use AWS Database Migration Service (AWS DMS) to perform continuous data replication from the on-premises database to Aurora. Create an Aurora Replica for the Aurora MySQL database, and move the aggregation jobs to run against the Aurora Replica. Set up collection endpoints as AWS Lambda functions behind an Application Load Balancer (ALB), and use Amazon RDS Proxy to write to the Aurora MySQL database. When the databases are synced, point the collector DNS record to the ALB. Disable the AWS DMS sync task after the cutover from on premises to AWS.
   • DSet up an Amazon Aurora MySQL database. Create an Aurora Replica for the Aurora MySQL database, and move the aggregation jobs to run against the Aurora Replica. Set up collection endpoints as an Amazon Kinesis data stream. Use Amazon Kinesis Data Firehose to replicate the data to the Aurora MySQL database. When the databases are synced, disable the replication job and restart the Aurora Replica as the primary instance. Point the collector DNS record to the Kinesis data stream.

   Show answer

   Answer: C

   Explanation

   Option A, B and D have some similarities with Option C but also have some key differences: Option A uses a Network Load Balancer (NLB) instead of an Application Load Balancer (ALB) and does not use AWS Database Migration Service (AWS DMS) for continuous data replication. Instead, it sets up the Aurora MySQL database as a replication target for the on-premises database. Option B does use AWS DMS for continuous data replication and sets up collection endpoints behind an ALB as Amazon EC2 instances in an Auto Scaling group. However, it does not create an Aurora Replica for the Aurora MySQL database or use Amazon RDS Proxy to write to the Aurora MySQL database. Option D does not use AWS DMS for continuous data replication or set up collection endpoints behind an ALB. Instead, it sets up collection endpoints as an Amazon Kinesis data stream and uses Amazon Kinesis Data Firehose to replicate the data to the Aurora MySQL database.

   Source: Adapted from community discussion — OCHT

   References

   • AWS documentation
   • AWS documentation

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6. Question 446 · 527 in bank

   A company runs an ecommerce web application on AWS. The web application is hosted as a static website on Amazon S3 with Amazon CloudFront for content delivery. An Amazon API Gateway API invokes AWS Lambda functions to handle user requests and order processing for the web application The Lambda functions store data in an Amazon ROS for MySQL DB cluster that uses On-Demand instances. The DB cluster usage has been consistent in the past 12 months. Recently, the website has experienced SQL injection and web exploit attempts. Customers also report that order processing time has increased during periods of peak usage. During these periods, the Lambda functions often have cold starts. As the company grows, the company needs to ensure scalability and low-latency access during traffic peaks. The company also must optimize the database costs and add protection against the SQL injection and web exploit attempts. Which solution will meet these requirements?

   • AConfigure the Lambda functions to have an increased timeout value during peak periods. Use RDS Reserved Instances for the database. Use CloudFront and subscribe to AWS Shield Advanced to protect against the SQL injection and web exploit attempts.
   • BIncrease the memory of the Lambda functions, Transition to Amazon Redshift for the database. Integrate Amazon Inspector with CloudFront to protect against the SQL injection and web exploit attempts.
   • CUse Lambda functions with provisioned concurrency for compute during peak periods, Transition to Amazon Aurora Serverless for the database. Use CloudFront and subscribe to AWS Shield Advanced to protect against the SQL injection and web exploit attempts.
   • DUse Lambda functions with provisioned concurrency for compute during peak periods. Use RDS Reserved Instances for the database. Integrate AWS WAF with CloudFront to protect against the SQL injection and web exploit attempts.

   Show answer

   Answer: D

   Explanation

   It's D: Provisioned Concurrency ensures that Lambda functions are pre-warmed and ready to handle requests instantly, which reduces the "cold start" problem. RDS Reserved Instances for Amazon RDS will help reduce the database cost. Since the workload has been consistent over the past 12 months, Reserved Instances provide a cost-effective solution by offering significant discounts compared to On-Demand pricing. AWS WAF protects the application from web exploits such as SQL injection and cross-site scripting (XSS).

   Source: Adapted from community discussion — JoeTromundo

   References

   • AWS documentation

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7. Question 447 · 527 in bank

   A company runs a web application on a single Amazon EC2 instance. End users experience slow application performance during times of peak usage, when CPU utilization is consistently more than 95%. A user data script installs required custom packages on the EC2 instance. The process of launching the instance takes several minutes. The company is creating an Auto Scaling group that has mixed instance groups, varied CPUs, and a maximum capacity limit. The Auto Scaling group will use a launch template for various configuration options. The company needs to decrease application latency when new instances are launched during auto scaling. Which solution will meet these requirements?

   • AUse a predictive scaling policy. Use an instance maintenance policy to run the user data script. Set the default instance warmup time to 0 seconds.
   • BUse a dynamic scaling policy. Use lifecycle hooks to run the user data script. Set the default instance warmup time to 0 seconds.
   • CUse a predictive scaling policy. Enable warm pools for the Auto Scaling group. Use an instance maintenance policy to run the user data script.
   • DUse a dynamic scaling policy. Enable warm pools for the Auto Scaling group. Use lifecycle hooks to run the user data script.

   Show answer

   Answer: D

   Explanation

   Agree with D There is no mention of predictable peak period. Since there's a known metric where user experience skpwness, dynamic scaling should be used. https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scale-based-on-demand.html#:~:text=A%20dynamic%20scaling%20policy%20instructs,CloudWatch%20alarm%20is%20in%20ALARM. Use warm pool to reduce latency and cost of unnecessary standby instance. https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-warm-pools.html Use lifecycle hook due to the need to install custom packages https://docs.aws.amazon.com/autoscaling/ec2/userguide/lifecycle-hooks.html More reference: https://aws.amazon.com/blogs/compute/introducing-instance-maintenance-policy-for-amazon-ec2-auto-scaling/

   Source: Adapted from community discussion — Daniel76

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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8. Question 448 · 527 in bank

   Select 2

   A company needs to migrate its on-premises database fleet to Amazon RDS. The company is currently using a mixture of Microsoft SQL Server, MySQL, and Oracle databases. Some of the databases have custom schemas and stored procedures. Which combination of steps should the company take for the migration? (Choose two.)

   • AUse Migration Evaluator Quick Insights to analyze the source databases and to identify the stored procedures that need to be migrated.
   • BUse AWS Application Migration Service to analyze the source databases and to identify the stored procedures that need to be migrated.
   • CUse the AWS Schema Conversion Tool (AWS SCT) to analyze the source databases for changes that are required
   • DUse AWS Database Migration Service (AWS DMS) to migrate the source databases to Amazon RDS.
   • EUse AWS DataSync to migrate the data from the source databases to Amazon RDS.

   Show answer

   Answer: CD

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9. Question 449 · 527 in bank

   Select 2

   A company is migrating its blog platform to AWS. The company's on-premises servers connect to AWS through an AWS Site-to-Site VPN connection. The blog content is updated several times a day by multiple authors and is served from a file share on a network-attached storage (NAS) server. The company needs to migrate the blog platform without delaying the content updates. The company has deployed Amazon EC2 instances across multiple Availability Zones to run the blog platform behind an Application Load Balancer. The company also needs to move 200 TB of archival data from its on-premises servers to Amazon S3 as soon as possible. Which combination of stops will meet these requirements? (Choose two.)

   • ACreate a weekly cron job in Amazon EventBridge. Use the cron job to invoke an AWS Lambda function to update the EC2 instances from the NAS server.
   • BConfigure an Amazon Elastic Block Store (Amazon EBS) Multi-Attach volume for the EC2 instances to share for content access. Write code to synchronize the EBS volume with the NAS server weekly.
   • CMount an Amazon Elastic File System (Amazon EFS) file system to the on-premises servers to act as the NAS server. Copy the blog data to the EFS file system. Mount the EFS file system to the C2 instances to serve the content.
   • DOrder an AWS Snowball Edge Storage Optimized device. Copy the static data artifacts to the device. Ship the device to AWS.
   • EOrder an AWS Snowcons SSD device. Copy the static data artifacts to the device. Ship the device to AWS.

   Show answer

   Answer: CD

   Explanation

   Answer is CD C. EFS provides a scalable, shared file storage that can be accessed by both on-premises servers and EC2 instances. This ensures that updates to the content are immediately available to the blog platform without the need for synchronization. D. Snowball Edge is designed for large-scale data transfers, providing an efficient way to move 200 TB of data to Amazon S3 quickly and securely.

   Source: Adapted from community discussion — AhmedSalem

   References

   • AWS documentation

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10. Question 450 · 527 in bank

    A company plans to migrate a legacy on-premises application to AWS. The application is a Java web application that runs on Apache Tomcat with a PostgreSQL database. The company does not have access to the source code but can deploy the application Java Archive (JAR) files. The application has increased traffic at the end of each month. Which solution will meet these requirements with the LEAST operational overhead?

    • ALaunch Amazon EC2 instances in multiple Availability Zones. Deploy Tomcat and PostgreSQL to all the instances by using Amazon Elastic File System (Amazon EFS) mount points. Use AWS Step Functions to deploy additional EC2 instances to scale for increased traffic.
    • BProvision Amazon Elastic Kubernetes Service (Amazon EKS) in an Auto Scaling group across multiple AWS Regions. Deploy Tomcat and PostgreSQL in the container images. Use a Network Load Balancer to scale for increased traffic.
    • CRefactor the Java application into Python-based containers. Use AWS Lambda functions for the application logic. Store application data in Amazon DynamoDB global tables. Use AWS Storage Gateway and Lambda concurrency to scale for increased traffic.
    • DUse AWS Elastic Beanstalk to deploy the Tomcat servers with auto scaling in multiple Availability Zones. Store application data in an Amazon RDS for PostgreSQL database. Deploy Amazon CloudFront and an Application Load Balancer to scale for increased traffic.

    Show answer

    Answer: D

    Explanation

    Answer D Elastic Beanstalk: Provides an easy and managed way to deploy and scale web applications. It handles the deployment, capacity provisioning, load balancing, and auto-scaling automatically. Amazon RDS for PostgreSQL: Manages the database operations, providing automated backups, patching, and scaling, which reduces operational overhead. CloudFront and Application Load Balancer: Ensure that the application can handle increased traffic efficiently, distributing the load across multiple Availability Zones and providing low latency.

    Source: Adapted from community discussion — AhmedSalem

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SAP-C02 practice, question 441. 527 questions available.