An application needs to retrieve a subset of data from a large CSV file stored in an Amazon S3 bucket by using simple SQL expressions. The queries are made within Amazon S3 and must only return the needed data.
Which of the following actions should be taken?

• A.Perform an S3 Select operation based on the bucket's name.
• B.Perform an S3 Select operation based on the bucket's name and object's metadata.
• C.Perform an S3 Select operation based on the bucket's name and object tags.
• D.Perform an S3 Select operation based on the bucket's name and object's key.

Correct Answer: D

S3 Select enables applications to retrieve only a subset of data from an object by using simple SQL expressions. By using S3 Select to retrieve only the data needed by your application, you can achieve drastic performance increases.


Amazon S3 is composed of buckets, object keys, object metadata, object tags, and many other components as shown below:
An Amazon S3 bucket name is globally unique, and the namespace is shared by all AWS accounts.
An Amazon S3 object key refers to the key name, which uniquely identifies the object in the bucket.
An Amazon S3 object metadata is a name-value pair that provides information about the object.
An Amazon S3 object tag is a key-pair value used for object tagging to categorize storage.
You can perform S3 Select to query only the necessary data inside the CSV files based on the bucket's name and the object's key.
The following snippet below shows how it is done using boto3 ( AWS SDK for Python ):
client = boto3.client('s3')
resp = client.select_object_content(
Bucket='tdojo-bucket', # Bucket Name.
Key='s3-select/tutorialsdojofile.csv', # Object Key.
ExpressionType= 'SQL',
Expression = "select \ "Sample\ " from s3object s where s.\ "tutorialsdojofile\ " in ['A', 'B']" Hence, the correct answer is the option that says: Perform an S3 Select operation based on the bucket's name and object's key.
The option that says: Perform an S3 Select operation based on the bucket's name and object's metadata is incorrect because metadata is not needed when querying subsets of data in an object using S3 Select.
The option that says: Perform an S3 Select operation based on the bucket's name and object tags is incorrect because object tags just provide additional information to your object. This is not needed when querying with S3 Select although this can be useful for S3 Batch Operations. You can categorize objects based on tag values to provide S3 Batch Operations with a list of objects to operate on.
The option that says: Perform an S3 Select operation based on the bucket's name is incorrect because you need both the bucket's name and the object key to successfully perform an S3 Select operation.
References:
https://docs.aws.amazon.com/AmazonS3/latest/dev/s3-glacier-select-sql-reference-select.html
https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingObjects.html
Check out this Amazon S3 Cheat Sheet:
https://tutorialsdojo.com/amazon-s3/

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A government entity is conducting a population and housing census in the city. Each household information uploaded on their online portal is stored in encrypted files in Amazon S3. The government assigned its Solutions Architect to set compliance policies that verify data containing personally identifiable information (PII) in a manner that meets their compliance standards. They should also be alerted if there are potential policy violations with the privacy of their S3 buckets.
Which of the following should the Architect implement to satisfy this requirement?

• A.Set up and configure Amazon Polly to scan for usage patterns on Amazon S3 data
• B.Set up and configure Amazon Macie to monitor their Amazon S3 data.
• C.Set up and configure Amazon Kendra to monitor malicious activity on their Amazon S3 data
• D.Set up and configure Amazon Fraud Detector to send out alert notifications whenever a security violation is detected on their Amazon S3 data.

Correct Answer: B

Amazon Macie is an ML-powered security service that helps you prevent data loss by automatically discovering, classifying, and protecting sensitive data stored in Amazon S3. Amazon Macie uses machine learning to recognize sensitive data such as personally identifiable information (PII) or intellectual property, assigns a business value, and provides visibility into where this data is stored and how it is being used in your organization.


Amazon Macie generates two categories of findings: policy findings and sensitive data findings. A policy finding is a detailed report of a potential policy violation or issue with the security or privacy of an Amazon S3 bucket. Macie generates these findings as part of its ongoing monitoring activities for your Amazon S3 data. A sensitive data finding is a detailed report of sensitive data in an S3 object. Macie generates these findings when it discovers sensitive data in S3 objects that you configure a sensitive data discovery job to analyze.
Hence, the correct answer is: Set up and configure Amazon Macie to monitor their Amazon S3 data. The option that says: Set up and configure Amazon Polly to scan for usage patterns on Amazon S3 data is incorrect because Amazon Polly is simply a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Polly can't be used to scane usage patterns on your S3 data.
The option that says: Set up and configure Amazon Kendra to monitor malicious activity on their Amazon S3 data is incorrect Amazon Kendra is just an enterprise search service that allows developers to add search capabilities to their applications. This enables their end users to discover information stored within the vast amount of content spread across their company, but not monitor malcious activity on their S3 buckets.
The option that says: Set up and configure Amazon Fraud Detector to send out alert notifications whenever a security violation is detected on their Amazon S3 data is incorrect because the Amazon Fraud Detector is only a fully managed service for identifying potentially fraudulent activities and for catching more online fraud faster. It does not check any S3 data containing personally identifiable information (PII), unlike Amazon Macie.
References:
https://docs.aws.amazon.com/macie/latest/userguide/what-is-macie.html
https://aws.amazon.com/macie/faq/
https://docs.aws.amazon.com/macie/index.html
Check out this Amazon Macie Cheat Sheet:
https://tutorialsdojo.com/amazon-macie/
AWS Security Services Overview - Secrets Manager, ACM, Macie:
https://youtu.be/ogVamzF2Dzk

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An advertising company is currently working on a proof of concept project that automatically provides SEO analytics for its clients. Your company has a VPC in AWS that operates in a dual-stack mode in which IPv4 and IPv6 communication is allowed. You deployed the application to an Auto Scaling group of EC2 instances with an Application Load Balancer in front that evenly distributes the incoming traffic. You are ready to go live but you need to point your domain name (tutorialsdojo.com) to the Application Load Balancer.
In Route 53, which record types will you use to point the DNS name of the Application Load Balancer?
(Select TWO.)

• A.Alias with a type "A" record set
• B.Non-Alias with a type "A" record set
• C.Alias with a type of "MX" record set
• D.Alias with a type "AAAA" record set
• E.Alias with a type "CNAME" record set

Correct Answer: A,D

The correct answers are: Alias with a type "AAAA" record set and Alias with a type "A" record set. To route domain traffic to an ELB load balancer, use Amazon Route 53 to create an alias record that points to your load balancer. An alias record is a Route 53 extension to DNS. It's similar to a CNAME record, but you can create an alias record both for the root domain, such as tutorialsdojo.com, and for subdomains, such as portal.tutorialsdojo.com. (You can create CNAME records only for subdomains.) To enable IPv6 resolution, you would need to create a second resource record, tutorialsdojo.com ALIAS AAAA -> myelb.us-west-2.elb.amazonnaws.com, this is assuming your Elastic Load Balancer has IPv6 support.

Non-Alias with a type "A" record set is incorrect because you only use Non-Alias with a type "A" record set for IP addresses.
Alias with a type "CNAME" record set is incorrect because you can't create a CNAME record at the zone apex. For example, if you register the DNS name tutorialsdojo.com, the zone apex is tutorialsdojo.com.
Alias with a type of "MX" record set is incorrect because an MX record is primarily used for mail servers.
It includes a priority number and a domain name, for example: 10 mailserver.tutorialsdojo.com.
Explanation:
Reference:
https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-to-elb-load-balancer.html
https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/resource-record-sets-choosing-alias-non- alias.html Check out this Amazon Route 53 Cheat Sheet:
https://tutorialsdojo.com/amazon-route-53/

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A major TV network has a web application running on eight Amazon T3 EC2 instances. The number of requests that the application processes are consistent and do not experience spikes. To ensure that eight instances are running at all times, the Solutions Architect should create an Auto Scaling group and distribute the load evenly between all instances.
Which of the following options can satisfy the given requirements?

• A.Deploy two EC2 instances with Auto Scaling in four regions behind an Amazon Elastic Load Balancer.
• B.Deploy eight EC2 instances with Auto Scaling in one Availability Zone behind an Amazon Elastic Load Balancer.
• C.Deploy four EC2 instances with Auto Scaling in one region and four in another region behind an Amazon Elastic Load Balancer.
• D.Deploy four EC2 instances with Auto Scaling in one Availability Zone and four in another availability zone in the same region behind an Amazon Elastic Load Balancer.

Correct Answer: D

The best option to take is to deploy four EC2 instances in one Availability Zone and four in another availability zone in the same region behind an Amazon Elastic Load Balancer. In this way, if one availability zone goes down, there is still another available zone that can accommodate traffic.

When the first AZ goes down, the second AZ will only have an initial 4 EC2 instances. This will eventually be scaled up to 8 instances since the solution is using Auto Scaling.
The 110% compute capacity for the 4 servers might cause some degradation of the service, but not a total outage since there are still some instances that handle the requests. Depending on your scale-up configuration in your Auto Scaling group, the additional 4 EC2 instances can be launched in a matter of minutes.
T3 instances also have a Burstable Performance capability to burst or go beyond the current compute capacity of the instance to higher performance as required by your workload. So your 4 servers will be able to manage 110% compute capacity for a short period of time. This is the power of cloud computing versus our on-premises network architecture. It provides elasticity and unparalleled scalability.
Take note that Auto Scaling will launch additional EC2 instances to the remaining Availability Zone/s in the event of an Availability Zone outage in the region. Hence, the correct answer is the option that says:
Deploy four EC2 instances with Auto Scaling in one Availability Zone and four in another availability zone in the same region behind an Amazon Elastic Load Balancer.
The option that says: Deploy eight EC2 instances with Auto Scaling in one Availability Zone behind an Amazon Elastic Load Balancer is incorrect because this architecture is not highly available. If that Availability Zone goes down then your web application will be unreachable.
The options that say: Deploy four EC2 instances with Auto Scaling in one region and four in another region behind an Amazon Elastic Load Balancer and Deploy two EC2 instances with Auto Scaling in four regions behind an Amazon Elastic Load Balancer are incorrect because the ELB is designed to only run in one region and not across multiple regions.
References:
https://aws.amazon.com/elasticloadbalancing/
https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-increase-availability.html AWS Elastic Load Balancing Overview:
https://youtu.be/UBl5dw59DO8
Check out this AWS Elastic Load Balancing (ELB) Cheat Sheet:
https://tutorialsdojo.com/aws-elastic-load-balancing-elb/

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A Solutions Architect is migrating several Windows-based applications to AWS that require a scalable file system storage for high-performance computing (HPC). The storage service must have full support for the SMB protocol and Windows NTFS, Active Directory (AD) integration, and Distributed File System (DFS).
Which of the following is the MOST suitable storage service that the Architect should use to fulfill this scenario?

• A.Amazon S3 Glacier Deep Archive
• B.AWS DataSync
• C.Amazon FSx for Lustre
• D.Amazon FSx for Windows File Server

Correct Answer: D

Amazon FSx provides fully managed third-party file systems. Amazon FSx provides you with the native compatibility of third-party file systems with feature sets for workloads such as Windows-based storage, high-performance computing (HPC), machine learning, and electronic design automation (EDA). You don't have to worry about managing file servers and storage, as Amazon FSx automates the time- consuming administration tasks such as hardware provisioning, software configuration, patching, and backups. Amazon FSx integrates the file systems with cloud-native AWS services, making them even more useful for a broader set of workloads.
Amazon FSx provides you with two file systems to choose from: Amazon FSx for Windows File Server for Windows-based applications and Amazon FSx for Lustre for compute-intensive workloads.

For Windows-based applications, Amazon FSx provides fully managed Windows file servers with features and performance optimized for "lift-and-shift" business-critical application workloads including home directories (user shares), media workflows, and ERP applications. It is accessible from Windows and Linux instances via the SMB protocol. If you have Linux-based applications, Amazon EFS is a cloud- native fully managed file system that provides simple, scalable, elastic file storage accessible from Linux instances via the NFS protocol.
For compute-intensive and fast processing workloads, like high-performance computing (HPC), machine learning, EDA, and media processing, Amazon FSx for Lustre, provides a file system that's optimized for performance, with input and output stored on Amazon S3.
Hence, the correct answer is: Amazon FSx for Windows File Server.
Amazon S3 Glacier Deep Archive is incorrect because this service is primarily used as a secure, durable, and extremely low-cost cloud storage for data archiving and long-term backup.
AWS DataSync is incorrect because this service simply provides a fast way to move large amounts of data online between on-premises storage and Amazon S3 or Amazon Elastic File System (Amazon EFS).
Amazon FSx for Lustre is incorrect because this service doesn't support the Windows-based applications as well as Windows servers.
References:
https://aws.amazon.com/fsx/
https://aws.amazon.com/getting-started/use-cases/hpc/3/
Check out this Amazon FSx Cheat Sheet:
https://tutorialsdojo.com/amazon-fsx/

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