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Amazon SAP-C01 Practice Test - Questions Answers, Page 37

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Question 361

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Which of the following commands accepts binary data as parameters?

--user-data
--user-data
–cipher text-key
–cipher text-key
--aws-customer-key
--aws-customer-key
--describe-instances-user
--describe-instances-user
Suggested answer: A

Explanation:

For commands that take binary data as a parameter, specify that the data is binary content by using the fileb:// prefix. Commands that accept binary data include: aws ec2 run-instances --user-data parameter. aws s3api put-object --ssecustomer- key parameter. aws kms decrypt --ciphertext-blob parameter.

Reference: http://docs.aws.amazon.com/cli/latest/userguide/aws-cli.pdf

asked 16/09/2024
Sorin Craia
39 questions

Question 362

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A company runs a video processing platform. Files are uploaded by users who connect to a web server, which stores them on an Amazon EFS share. This web server is running on a single Amazon EC2 instance. A different group of instances, running in an Auto Scaling group, scans the EFS share directory structure for new files to process and generates new videos (thumbnails, different resolution, compression, etc.) according to the instructions file, which is uploaded along with the video files. A different application running on a group of instances managed by an Auto Scaling group processes the video files and then deletes them from the EFS share. The results are stored in an S3 bucket. Links to the processed video files are emailed to the customer.

The company has recently discovered that as they add more instances to the Auto Scaling Group, many files are processed twice, so image processing speed is not improved. The maximum size of these video files is 2GB. What should the Solutions Architect do to improve reliability and reduce the redundant processing of video files?

Modify the web application to upload the video files directly to Amazon S3. Use Amazon CloudWatch Events to trigger an AWS Lambda function every time a file is uploaded, and have this Lambda function put a message into an Amazon SQS queue. Modify the video processing application to read from SQS queue for new files and use the queue depth metric to scale instances in the video processing Auto Scaling group.
Modify the web application to upload the video files directly to Amazon S3. Use Amazon CloudWatch Events to trigger an AWS Lambda function every time a file is uploaded, and have this Lambda function put a message into an Amazon SQS queue. Modify the video processing application to read from SQS queue for new files and use the queue depth metric to scale instances in the video processing Auto Scaling group.
Set up a cron job on the web server instance to synchronize the contents of the EFS share into Amazon S3. Trigger an AWS Lambda function every time a file is uploaded to process the video file and store the results in Amazon S3. Using Amazon CloudWatch Events, trigger an Amazon SES job to send an email to the customer containing the link to the processed file.
Set up a cron job on the web server instance to synchronize the contents of the EFS share into Amazon S3. Trigger an AWS Lambda function every time a file is uploaded to process the video file and store the results in Amazon S3. Using Amazon CloudWatch Events, trigger an Amazon SES job to send an email to the customer containing the link to the processed file.
Rewrite the web application to run directly from Amazon S3 and use Amazon API Gateway to upload the video files to an S3 bucket. Use an S3 trigger to run an AWS Lambda function each time a file is uploaded to process and store new video files in a different bucket. Using CloudWatch Events, trigger an SES job to send an email to the customer containing the link to the processed file.
Rewrite the web application to run directly from Amazon S3 and use Amazon API Gateway to upload the video files to an S3 bucket. Use an S3 trigger to run an AWS Lambda function each time a file is uploaded to process and store new video files in a different bucket. Using CloudWatch Events, trigger an SES job to send an email to the customer containing the link to the processed file.
Rewrite the web application to run from Amazon S3 and upload the video files to an S3 bucket. Each time a new file is uploaded, trigger an AWS Lambda function to put a message in an SQS queue containing the link and the instructions. Modify the video processing application to read from the SQS queue and the S3 bucket. Use the queue depth metric to adjust the size of the Auto Scaling group for video processing instances.
Rewrite the web application to run from Amazon S3 and upload the video files to an S3 bucket. Each time a new file is uploaded, trigger an AWS Lambda function to put a message in an SQS queue containing the link and the instructions. Modify the video processing application to read from the SQS queue and the S3 bucket. Use the queue depth metric to adjust the size of the Auto Scaling group for video processing instances.
Suggested answer: B

Explanation:

Reference:

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/FIFO-queues.html

asked 16/09/2024
Ricardo de Sá Carvalho
38 questions

Question 363

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A company is running a commercial Apache Hadoop cluster on Amazon EC2. This cluster is being used daily to query large files on Amazon S3. The data on Amazon S3 has been curated and does not require any additional transformations steps.

The company is using a commercial business intelligence (BI) tool on Amazon EC2 to run queries against the Hadoop cluster and visualize the data. The company wants to reduce or eliminate the overhead costs associated with managing the Hadoop cluster and the BI tool. The company would like to move to a more cost-effective solution with minimal effort. The visualization is simple and requires performing some basic aggregation steps only. Which option will meet the company’s requirements?

Launch a transient Amazon EMR cluster daily and develop an Apache Hive script to analyze the files on Amazon S3. Shut down the Amazon EMR cluster when the job is complete. Then use Amazon QuickSight to connect to Amazon EMR and perform the visualization.
Launch a transient Amazon EMR cluster daily and develop an Apache Hive script to analyze the files on Amazon S3. Shut down the Amazon EMR cluster when the job is complete. Then use Amazon QuickSight to connect to Amazon EMR and perform the visualization.
Develop a stored procedure invoked from a MySQL database running on Amazon EC2 to analyze the files in Amazon S3. Then use a fast in-memory BI tool running on Amazon EC2 to visualize the data.
Develop a stored procedure invoked from a MySQL database running on Amazon EC2 to analyze the files in Amazon S3. Then use a fast in-memory BI tool running on Amazon EC2 to visualize the data.
Develop a script that uses Amazon Athena to query and analyze the files on Amazon S3. Then use Amazon QuickSight to connect to Athena and perform the visualization.
Develop a script that uses Amazon Athena to query and analyze the files on Amazon S3. Then use Amazon QuickSight to connect to Athena and perform the visualization.
Use a commercial extract, transform, load (ETL) tool that runs on Amazon EC2 to prepare the data for processing. Then switch to a faster and cheaper BI tool that runs on Amazon EC2 to visualize the data from Amazon S3.
Use a commercial extract, transform, load (ETL) tool that runs on Amazon EC2 to prepare the data for processing. Then switch to a faster and cheaper BI tool that runs on Amazon EC2 to visualize the data from Amazon S3.
Suggested answer: C

Explanation:

Reference: https://docs.aws.amazon.com/quicksight/latest/user/create-a-data-set-athena.html

https://aws.amazon.com/athena/

asked 16/09/2024
Sriharsha Janga
43 questions

Question 364

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You currently operate a web application. In the AWS US-East region. The application runs on an auto-scaled layer of EC2 instances and an RDS Multi-AZ database. Your IT security compliance officer has tasked you to develop a reliable and durable logging solution to track changes made to your EC2.IAM And RDS resources. The solution must ensure the integrity and confidentiality of your log data. Which of these solutions would you recommend?

Create a new CloudTrail trail with one new S3 bucket to store the logs and with the global services option selected. Use IAM roles S3 bucket policies and Multi Factor Authentication (MFA) Delete on the S3 bucket that stores your logs.
Create a new CloudTrail trail with one new S3 bucket to store the logs and with the global services option selected. Use IAM roles S3 bucket policies and Multi Factor Authentication (MFA) Delete on the S3 bucket that stores your logs.
Create a new CloudTrail with one new S3 bucket to store the logs Configure SNS to send log file delivery notifications to your management system. Use IAM roles and S3 bucket policies on the S3 bucket mat stores your logs.
Create a new CloudTrail with one new S3 bucket to store the logs Configure SNS to send log file delivery notifications to your management system. Use IAM roles and S3 bucket policies on the S3 bucket mat stores your logs.
Create a new CloudTrail trail with an existing S3 bucket to store the logs and with the global services option selected. Use S3 ACLs and Multi Factor Authentication (MFA). Delete on the S3 bucket that stores your logs.
Create a new CloudTrail trail with an existing S3 bucket to store the logs and with the global services option selected. Use S3 ACLs and Multi Factor Authentication (MFA). Delete on the S3 bucket that stores your logs.
Create three new CloudTrail trails with three new S3 buckets to store the logs one for the AWS Management console, one for AWS SDKs and one for command line tools. Use IAM roles and S3 bucket policies on the S3 buckets that store your logs.
Create three new CloudTrail trails with three new S3 buckets to store the logs one for the AWS Management console, one for AWS SDKs and one for command line tools. Use IAM roles and S3 bucket policies on the S3 buckets that store your logs.
Suggested answer: A
asked 16/09/2024
Andre Passos
35 questions

Question 365

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How does in-memory caching improve the performance of applications in ElastiCache?

It improves application performance by deleting the requests that do not contain frequently accessed data.
It improves application performance by deleting the requests that do not contain frequently accessed data.
It improves application performance by implementing good database indexing strategies.
It improves application performance by implementing good database indexing strategies.
It improves application performance by using a part of instance RAM for caching important data.
It improves application performance by using a part of instance RAM for caching important data.
It improves application performance by storing critical pieces of data in memory for low-latency access.
It improves application performance by storing critical pieces of data in memory for low-latency access.
Suggested answer: D

Explanation:

In Amazon ElastiCache, in-memory caching improves application performance by storing critical pieces of data in memory for low-latency access. Cached information may include the results of I/Ointensive database queries or the results of computationally intensive calculations.

Reference: http://aws.amazon.com/elasticache/faqs/#g4

asked 16/09/2024
Jack de Cort
42 questions

Question 366

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You want to mount an Amazon EFS file system on an Amazon EC2 instance using DNS names. Which of the following generic form of a mount target's DNS name must you use to mount the file system?

availability-zone.file-system-id.efs.aws-region.amazonaws.com
availability-zone.file-system-id.efs.aws-region.amazonaws.com
efs-system-id.availability-zone.file-aws-region.amazonaws.com
efs-system-id.availability-zone.file-aws-region.amazonaws.com
$file-system-id.$availability-zone.$efs.aws-region.$amazonaws.com
$file-system-id.$availability-zone.$efs.aws-region.$amazonaws.com
#aws-region.#availability-zone.#file-system-id.#efs.#amazonaws.com
#aws-region.#availability-zone.#file-system-id.#efs.#amazonaws.com
Suggested answer: A

Explanation:

An Amazon EFS file system can be mounted on an Amazon EC2 instance using DNS names. This can be done with either a DNS name for the file system or a DNS name for the mount target. To construct the mount target's DNS name, use the following generic form: availability-zone.file-system-id.efs.aws-region.amazonaws.com

Reference: http://docs.aws.amazon.com/efs/latest/ug/mounting-fs.html#mounting-fs-install-nfsclient

asked 16/09/2024
Dimitri Alvarez Ruiz
41 questions

Question 367

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You want to use AWS CodeDeploy to deploy an application to Amazon EC2 instances running within an Amazon Virtual Private Cloud (VPC). What criterion must be met for this to be possible?

The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access only the public AWS CodeDeploy endpoint.
The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access only the public AWS CodeDeploy endpoint.
The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access only the public Amazon S3 service endpoint.
The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access only the public Amazon S3 service endpoint.
The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access the public AWS CodeDeploy and Amazon S3 service endpoints.
The AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access the public AWS CodeDeploy and Amazon S3 service endpoints.
It is not currently possible to use AWS CodeDeploy to deploy an application to Amazon EC2 instances running within an Amazon Virtual Private Cloud (VPC.)
It is not currently possible to use AWS CodeDeploy to deploy an application to Amazon EC2 instances running within an Amazon Virtual Private Cloud (VPC.)
Suggested answer: C

Explanation:

You can use AWS CodeDeploy to deploy an application to Amazon EC2 instances running within an Amazon Virtual Private Cloud (VPC). However, the AWS CodeDeploy agent installed on the Amazon EC2 instances must be able to access the public AWS CodeDeploy and Amazon S3 service endpoints. Reference: http://aws.amazon.com/codedeploy/faqs/

asked 16/09/2024
Gokul Kalaiselvi Loganathan
47 questions

Question 368

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An AWS customer runs a public blogging website. The site users upload two million blog entries a month. The average blog entry size is 200 KB. The access rate to blog entries drops to negligible 6 months after publication and users rarely access a blog entry 1 year after publication. Additionally, blog entries have a high update rate during the first 3 months following publication, this drops to no updates after 6 months. The customer wants to use CloudFront to improve his user's load times.

Which of the following recommendations would you make to the customer?

Duplicate entries into two different buckets and create two separate CloudFront distributions where S3 access is restricted only to Cloud Front identity
Duplicate entries into two different buckets and create two separate CloudFront distributions where S3 access is restricted only to Cloud Front identity
Create a CloudFront distribution with “US Europe” price class for US/Europe users and a different CloudFront distribution with “All Edge Locations” for the remaining users.
Create a CloudFront distribution with “US Europe” price class for US/Europe users and a different CloudFront distribution with “All Edge Locations” for the remaining users.
Create a CloudFront distribution with S3 access restricted only to the CloudFront identity and partition the blog entry's location in S3 according to the month it was uploaded to be used with CloudFront behaviors.
Create a CloudFront distribution with S3 access restricted only to the CloudFront identity and partition the blog entry's location in S3 according to the month it was uploaded to be used with CloudFront behaviors.
Create a CloudFront distribution with Restrict Viewer Access Forward Query string set to true and minimum TTL of 0.
Create a CloudFront distribution with Restrict Viewer Access Forward Query string set to true and minimum TTL of 0.
Suggested answer: C
asked 16/09/2024
Srecka Kovacevic
39 questions

Question 369

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A company is running multiple workloads in the AWS Cloud. The company has separate units for software development. The company uses AWS Organizations and federation with SAML to give permissions to developers to manage resources in their AWS accounts. The development units each deploy their production workloads into a common production account. Recently, an incident occurred in the production account in which members of a development unit terminated an EC2 instance that belonged to a different development unit. A solutions architect must create a solution that prevents a similar incident from happening in the future. The solution also must allow developers the possibility to manage the instances used for their workloads. Which strategy will meet these requirements?

Create separate OUs in AWS Organizations for each development unit. Assign the created OUs to the company AWS accounts. Create separate SCPs with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag that matches the development unit name. Assign the SCP to the corresponding OU.
Create separate OUs in AWS Organizations for each development unit. Assign the created OUs to the company AWS accounts. Create separate SCPs with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag that matches the development unit name. Assign the SCP to the corresponding OU.
Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Update the IAM policy for the developers’ assumed IAM role with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/DevelopmentUnit.
Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Update the IAM policy for the developers’ assumed IAM role with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/DevelopmentUnit.
Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Create an SCP with an allow action and a StringEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/DevelopmentUnit. Assign the SCP to the root OU.
Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Create an SCP with an allow action and a StringEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/DevelopmentUnit. Assign the SCP to the root OU.
Create separate IAM policies for each development unit. For every IAM policy, add an allow action and a StringEquals condition for the DevelopmentUnit resource tag and the development unit name. During SAML federation, use AWS Security Token Service (AWS STS) to assign the IAM policy and match the development unit name to the assumed IAM role.
Create separate IAM policies for each development unit. For every IAM policy, add an allow action and a StringEquals condition for the DevelopmentUnit resource tag and the development unit name. During SAML federation, use AWS Security Token Service (AWS STS) to assign the IAM policy and match the development unit name to the assumed IAM role.
Suggested answer: B
asked 16/09/2024
Padmanabhan Kudiarasu
48 questions

Question 370

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Your company runs a customer facing event registration site This site is built with a 3-tier architecture with web and application tier servers and a MySQL database The application requires 6 web tier servers and 6 application tier servers for normal operation, but can run on a minimum of 65% server capacity and a single MySQL database.

When deploying this application in a region with three availability zones (AZs) which architecture provides high availability?

A web tier deployed across 2 AZs with 3 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer), and an application tier deployed across 2 AZs with 3 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and one RDS (Relational Database Service) instance deployed with read replicas in the other AZ.
A web tier deployed across 2 AZs with 3 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer), and an application tier deployed across 2 AZs with 3 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and one RDS (Relational Database Service) instance deployed with read replicas in the other AZ.
A web tier deployed across 3 AZs with 2 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer) and an application tier deployed across 3 AZs with 2 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and one RDS (Relational Database Service) Instance deployed with read replicas in the two other AZs.
A web tier deployed across 3 AZs with 2 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer) and an application tier deployed across 3 AZs with 2 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and one RDS (Relational Database Service) Instance deployed with read replicas in the two other AZs.
A web tier deployed across 2 AZs with 3 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer) and an application tier deployed across 2 AZs with 3 EC2 instances m each AZ inside an Auto Scaling Group behind an ELS and a Multi-AZ RDS (Relational Database Service) deployment.
A web tier deployed across 2 AZs with 3 EC2 (Elastic Compute Cloud) instances in each AZ inside an Auto Scaling Group behind an ELB (elastic load balancer) and an application tier deployed across 2 AZs with 3 EC2 instances m each AZ inside an Auto Scaling Group behind an ELS and a Multi-AZ RDS (Relational Database Service) deployment.
A web tier deployed across 3 AZs with 2 EC2 (Elastic Compute Cloud) instances in each AZ Inside an Auto Scaling Group behind an ELB (elastic load balancer). And an application tier deployed across 3 AZs with 2 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and a Multi-AZ RDS (Relational Database services) deployment.
A web tier deployed across 3 AZs with 2 EC2 (Elastic Compute Cloud) instances in each AZ Inside an Auto Scaling Group behind an ELB (elastic load balancer). And an application tier deployed across 3 AZs with 2 EC2 instances in each AZ inside an Auto Scaling Group behind an ELB and a Multi-AZ RDS (Relational Database services) deployment.
Suggested answer: D

Explanation:

Amazon RDS Multi-AZ Deployments

Amazon RDS Multi-AZ deployments provide enhanced availability and durability for Database (DB) Instances, making them a natural fit for production database workloads. When you provision a Multi-AZ DB Instance, Amazon RDS automatically creates a primary DB Instance and synchronously replicates the data to a standby instance in a different Availability Zone (AZ). Each AZ runs on its own physically distinct, independent infrastructure, and is engineered to be highly reliable. In case of an infrastructure failure (for example, instance hardware failure, storage failure, or network disruption), Amazon RDS performs an automatic failover to the standby, so that you can resume database operations as soon as the failover is complete. Since the endpoint for your DB Instance remains the same after a failover, your application can resume database operation without the need for manual administrative intervention. Enhanced Durability

Multi-AZ deployments for the MySQL, Oracle, and PostgreSQL engines utilize synchronous physical replication to keep data on the standby up-to-date with the primary. Multi-AZ deployments for the SQL Server engine use synchronous logical replication to achieve the same result, employing SQL Server-native Mirroring technology. Both approaches safeguard your data in the event of a DB Instance failure or loss of an Availability Zone. If a storage volume on your primary fails in a Multi-AZ deployment, Amazon RDS automatically initiates a failover to the upto- date standby. Compare this to a Single-AZ deployment: in case of a Single-AZ database failure, a user-initiated point-intime- restore operation will be required. This operation can take several hours to complete, and any data updates that occurred after the latest restorable time (typically within the last five minutes) will not be available. Amazon Aurora employs a highly durable, SSD-backed virtualized storage layer purpose-built for database workloads. Amazon Aurora automatically replicates your volume six ways, across three Availability Zones. Amazon Aurora storage is fault-tolerant, transparently handling the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability. Amazon Aurora storage is also self-healing. Data blocks and disks are continuously scanned for errors and replaced automatically. Increased Availability

You also benefit from enhanced database availability when running Multi-AZ deployments. If an Availability Zone failure or DB Instance failure occurs, your availability impact is limited to the time automatic failover takes to complete:

typically under one minute for Amazon Aurora and one to two minutes for other database engines (see the RDS FAQ for details). The availability benefits of Multi-AZ deployments also extend to planned maintenance and backups. In the case of system upgrades like OS patching or DB Instance scaling, these operations are applied first on the standby, prior to the automatic failover. As a result, your availability impact is, again, only the time required for automatic failover to complete. Unlike Single-AZ deployments, I/O activity is not suspended on your primary during backup for Multi-AZ deployments for the MySQL, Oracle, and PostgreSQL engines, because the backup is taken from the standby. However, note that you may still experience elevated latencies for a few minutes during backups for Multi-AZ deployments.

On instance failure in Amazon Aurora deployments, Amazon RDS uses RDS Multi-AZ technology to automate failover to one of up to 15 Amazon Aurora Replicas you have created in any of three Availability Zones. If no Amazon Aurora Replicas have been provisioned, in the case of a failure, Amazon RDS will attempt to create a new Amazon Aurora DB instance for you automatically.

asked 16/09/2024
PATRICK ADUSEI
36 questions
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