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Amazon AWS Certified Solutions Architect Associate
AWS Certified Solutions Architect - Associate
Amazon AWS Certified Solutions Architect Associate
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Question 1
A 3-tier e-commerce web application is current deployed on-premises and will be migrated to AWS for greater scalability and elasticity The web server currently shares read-only data using a network distributed file system The app server tier uses a clustering mechanism for discovery and shared session state that depends on IP multicast The database tier uses shared-storage clustering to provide database fall over capability, and uses several read slaves for scaling Data on all servers and the distributed file system directory is backed up weekly to off-site tapes
Which AWS storage and database architecture meets the requirements of the application?
Options:
A. Web servers: store read-only data in S3, and copy from S3 to root volume at boot time. App servers:
share state using a combination of DynamoDB and IP unicast. Database: use RDS with multi-AZ deployment and one or more read replicas. Backup: web servers, app servers, and database backed
up weekly to Glacier using snapshots.
B. Web servers: store read-only data in an EC2 NFS server, mount to each web server at boot time. App
servers: share state using a combination of DynamoDB and IP multicast. Database: use RDS with multi-AZ deployment and one or more Read Replicas. Backup: web and app servers backed up weekly
via AMIs, database backed up via DB snapshots.
C. Web servers: store read-only data in S3, and copy from S3 to root volume at boot time. App servers:
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share state using a combination of DynamoDB and IP unicast. Database: use RDS with multi-AZ deployment and one or more Read Replicas. Backup: web and app servers backed up weekly via AMIs, database backed up via DB snapshots.
D. Web servers: store read-only data in S3, and copy from S3 to root volume at boot time. App servers:
share state using a combination of DynamoDB and IP unicast. Database: use RDS with multi-AZ deployment. Backup: web and app servers backed up weekly via AMIs, database backed up via DB
snapshots.
Answer: C
Explanation:
Explanation:
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.
Benefits
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 up-to-date standby. Compare this to a Single-AZ deployment: in case of a Single-AZ database failure, a user-initiated point-in-time-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.
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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.
No Administrative Intervention
DB Instance failover is fully automatic and requires no administrative intervention. Amazon RDS monitors the health of your primary and standbys, and initiates a failover automatically in response to a variety of failure conditions.
Failover conditions
Amazon RDS detects and automatically recovers from the most common failure scenarios for Multi-AZ deployments so that you can resume database operations as quickly as possible without administrative intervention. Amazon RDS automatically performs a failover in the event of any of the following:
Loss of availability in primary Availability Zone
Loss of network connectivity to primary
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  Compute unit failure on primary
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 Storage failure on primary
Note: When operations such as DB Instance scaling or system upgrades like OS patching are initiated for Multi-AZ deployments, for enhanced availability, they are applied first on the standby prior to an automatic failover. As a result, your availability impact is limited only to the time required for automatic failover to complete. Note that Amazon RDS Multi-AZ deployments do not failover automatically in response to
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database operations such as long running queries, deadlocks or database corruption errors.
Question 2
Your customer wishes to deploy an enterprise application to AWS which will consist of several web servers, several application servers and a small (50GB) Oracle database information is stored, both in the database and the file systems of the various servers. The backup system must support database recovery whole server and whole disk restores, and individual file restores with a recovery time of no more than two hours. They have chosen to use RDS Oracle as the database.
Which backup architecture will meet these requirements?
Options:
A. Backup RDS using automated daily DB backups Backup the EC2 instances using AMIs and
supplement with file-level backup to S3 using traditional enterprise backup software to provide file level
restore
B. Backup RDS using a Multi-AZ Deployment Backup the EC2 instances using Amis, and supplement by
copying file system data to S3 to provide file level restore.
C. Backup RDS using automated daily DB backups Backup the EC2 instances using EBS snapshots and
supplement with file-level backups to Amazon Glacier using traditional enterprise backup software to
provide file level restore
D. Backup RDS database to S3 using Oracle RMAN Backup the EC2 instances using Amis, and
supplement with EBS snapshots for individual volume restore.
Answer: A
Explanation:
Point-In-Time Recovery
In addition to the daily automated backup, Amazon RDS archives database change logs. This enables you to recover your database to any point in time during the backup retention period, up to the last five minutes of database usage.
Amazon RDS stores multiple copies of your data, but for Single-AZ DB instances these copies are stored in a single availability zone. If for any reason a Single-AZ DB instance becomes unusable, you can use point-in-time recovery to launch a new DB instance with the latest restorable data. For more information on working with point-in-time recovery, go to Restoring a DB Instance to a Specified Time.
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Note
Multi-AZ deployments store copies of your data in different Availability Zones for greater levels of data durability. For more information on Multi-AZ deployments, see High Availability (Multi-AZ).
Question 3
Company B is launching a new game app for mobile devices. Users will log into the game using their existing social media account to streamline data capture. Company B would like to directly save player data and scoring information from the mobile app to a DynamoDS table named Score Data When a user saves their game the progress data will be stored to the Game state S3 bucket. What is the best approach for storing data to DynamoDB and S3?
Options:
A. Use an EC2 Instance that is launched with an EC2 role providing access to the Score Data DynamoDB
table and the GameState S3 bucket that communicates with the mobile app via web services.
B. Use temporary security credentials that assume a role providing access to the Score Data DynamoDB
table and the Game State S3 bucket using web identity federation.
C. Use Login with Amazon allowing users to sign in with an Amazon account providing the mobile app
with access to the Score Data DynamoDB table and the Game State S3 bucket.
D. Use an IAM user with access credentials assigned a role providing access to the Score Data
DynamoDB table and the Game State S3 bucket for distribution with the mobile app.
Answer: B
Explanation:
Web Identity Federation
Imagine that you are creating a mobile app that accesses AWS resources, such as a game that runs on a mobile device and stores player and score information using Amazon S3 and DynamoDB.
When you write such an app, you'll make requests to AWS services that must be signed with an AWS access key. However, we strongly recommend that you do not embed or distribute long-term AWS credentials with apps that a user downloads to a device, even in an encrypted store. Instead, build your app so that it requests temporary AWS security credentials dynamically when needed using web identity federation. The supplied temporary credentials map to an AWS role that has only the permissions needed to perform the tasks required by the mobile app.
With web identity federation, you don't need to create custom sign-in code or manage your own user
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identities. Instead, users of your app can sign in using a well-known identity provider (IdP) "such as Login with Amazon, Facebook, Google, or any other OpenID Connect (OIDC)-compatible IdP, receive an authentication token, and then exchange that token for temporary security credentials in AWS that map to an IAM role with permissions to use the resources in your AWS account. Using an IdP helps you keep your AWS account secure, because you don't have to embed and distribute long-term security credentials with your application.
For most scenarios, we recommend that you use Amazon Cognito because it acts as an identity broker and does much of the federation work for you. For details, see the following section, Using Amazon Cognito for Mobile Apps.
If you don't use Amazon Cognito, then you must write code that interacts with a web IdP (Login with Amazon, Facebook, Google, or any other OIDC-compatible IdP) and then calls the AssumeRoleWithWebIdentity API to trade the authentication token you get from those IdPs for AWS temporary security credentials. If you have already used this approach for existing apps, you can continue to use it.
Using Amazon Cognito for Mobile Apps
The preferred way to use web identity federation is to use Amazon Cognito. For example, Adele the developer is building a game for a mobile device where user data such as scores and profiles is stored in Amazon S3 and Amazon DynamoDB. Adele could also store this data locally on the device and use Amazon Cognito to keep it synchronized across devices. She knows that for security and maintenance reasons, long-term AWS security credentials should not be distributed with the game. She also knows that the game might have a large number of users. For all of these reasons, she does not want to create new user identities in IAM for each player. Instead, she builds the game so that users can sign in using an identity that they've already established with a well-known identity provider, such as Login with Amazon, Facebook, Google, or any OpenID Connect (OIDC)-compatible identity provider. Her game can take advantage of the authentication mechanism from one of these providers to validate the user's identity.
To enable the mobile app to access her AWS resources, Adele first registers for a developer ID with her chosen IdPs. She also configures the application with each of these providers. In her AWS account that contains the Amazon S3 bucket and DynamoDB table for the game, Adele uses Amazon Cognito to create IAM roles that precisely define permissions that the game needs. If she is using an OIDC IdP, she also creates an IAM OIDC identity provider entity to establish trust between her AWS account and the IdP.
In the app's code, Adele calls the sign-in interface for the IdP that she configured previously. The IdP handles all the details of letting the user sign in, and the app gets an OAuth access token or OIDC ID token from the provider. Adele's app can trade this authentication information for a set of temporary security credentials that consist of an AWS access key ID, a secret access key, and a session token. The app can then use these credentials to access web services offered by AWS. The app is limited to the permissions that are defined in the role that it assumes.
The following figure shows a simplified flow for how this might work, using Login with Amazon as the IdP. For Step 2, the app can also use Facebook, Google, or any OIDC-compatible identity provider, but that's not shown here.
Sample workflow using Amazon Cognito to federate users for a mobile application
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 A customer starts your app on a mobile device. The app asks the user to sign in.
The app uses Login with Amazon resources to accept the user's credentials.
The app uses Cognito APIs to exchange the Login with Amazon ID token for a Cognito token.
The app requests temporary security credentials from AWS STS, passing the Cognito token.
The temporary security credentials can be used by the app to access any AWS resources required by the app to operate. The role associated with the temporary security credentials and its assigned policies
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determines what can be accessed.
Use the following process to configure your app to use Amazon Cognito to authenticate users and give your app access to AWS resources. For specific steps to accomplish this scenario, consult the documentation for Amazon Cognito.
(Optional) Sign up as a developer with Login with Amazon, Facebook, Google, or any other OpenID Connect (OIDC)-compatible identity provider and configure one or more apps with the provider. This step is optional because Amazon Cognito also supports unauthenticated (guest) access for your users.
Go to Amazon Cognito in the AWS Management Console. Use the Amazon Cognito wizard to create an identity pool, which is a container that Amazon Cognito uses to keep end user identities organized for your apps. You can share identity pools between apps. When you set up an identity pool, Amazon Cognito creates one or two IAM roles (one for authenticated identities, and one for unauthenticated "guest" identities) that define permissions for Amazon Cognito users.
Download and integrate the AWS SDK for iOS or the AWS SDK for Android with your app, and import the files required to use Amazon Cognito.
Create an instance of the Amazon Cognito credentials provider, passing the identity pool ID, your AWS account number, and the Amazon Resource Name (ARN) of the roles that you associated with the identity pool. The Amazon Cognito wizard in the AWS Management Console provides sample code to help you get started.
When your app accesses an AWS resource, pass the credentials provider instance to the client object, which passes temporary security credentials to the client. The permissions for the credentials are based on the role or roles that you defined earlier.
Question 4
Your company plans to host a large donation website on Amazon Web Services (AWS). You anticipate a large and undetermined amount of traffic that will create many database writes. To be certain that you do not drop any writes to a database hosted on AWS. Which service should you use?
Options:
A. Amazon RDS with provisioned IOPS up to the anticipated peak write throughput.
B. Amazon Simple Queue Service (SOS) for capturing the writes and draining the queue to write to the
database.
C. Amazon ElastiCache to store the writes until the writes are committed to the database.
D. Amazon DynamoDB with provisioned write throughput up to the anticipated peak write throughput.
Answer: B
Explanation:
Explanation:
Amazon Simple Queue Service (Amazon SQS) offers a reliable, highly scalable hosted queue for storing
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messages as they travel between computers. By using Amazon SQS, developers can simply move data between distributed application components performing different tasks, without losing messages or requiring each component to be always available. Amazon SQS makes it easy to build a distributed, decoupled application, working in close conjunction with the Amazon Elastic Compute Cloud (Amazon EC2) and the other AWS infrastructure web services.
What can I do with Amazon SQS?
Amazon SQS is a web service that gives you access to a message queue that can be used to store messages while waiting for a computer to process them. This allows you to quickly build message queuing applications that can be run on any computer on the internet. Since Amazon SQS is highly scalable and you only pay for what you use, you can start small and grow your application as you wish, with no compromise on performance or reliability. This lets you focus on building sophisticated message-based applications, without worrying about how the messages are stored and managed. You can use Amazon SQS with software applications in various ways. For example, you can:
Integrate Amazon SQS with other AWS infrastructure web services to make applications more reliable and flexible.
Use Amazon SQS to create a queue of work where each message is a task that needs to be completed by a process. One or many computers can read tasks from the queue and perform them.
Build a microservices architecture, using queues to connect your microservices.
Keep notifications of significant events in a business process in an Amazon SQS queue. Each event can have a corresponding message in a queue, and applications that need to be aware of the event can read and process the messages.
Question 5
Your company is in the process of developing a next generation pet collar that collects biometric information to assist families with promoting healthy lifestyles for their pets Each collar will push 30kb of biometric data In JSON format every 2 seconds to a collection platform that will process and analyze the data providing health trending information back to the pet owners and veterinarians via a web portal Management has tasked you to architect the collection platform ensuring the following requirements are met.
Provide the ability for real-time analytics of the inbound biometric data
Ensure processing of the biometric data is highly durable. Elastic and parallel
The results of the analytic processing should be persisted for data mining
Which architecture outlined below win meet the initial requirements for the collection platform?
Options:
A. Utilize S3 to collect the inbound sensor data analyze the data from S3 with a daily scheduled Data
Pipeline and save the results to a Redshift Cluster.
B. Utilize Amazon Kinesis to collect the inbound sensor data, analyze the data with Kinesis clients and
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save the results to a Redshift cluster using EMR.
C. Utilize SQS to collect the inbound sensor data analyze the data from SQS with Amazon Kinesis and
save the results to a Microsoft SQL Server RDS instance.
D. Utilize EMR to collect the inbound sensor data, analyze the data from EUR with Amazon Kinesis and
save me results to DynamoDB.
Answer: B
Explanation:
The POC solution is being scaled up by 1000, which means it will require 72TB of Storage to retain 24 months' worth of data. This rules out RDS as a possible DB solution which leaves you with RedShift. I believe DynamoDB is a more cost effective and scales better for ingest rather than using EC2 in an auto scaling group. Also, this example solution from AWS is somewhat similar for reference. http://media.amazonwebservices.com/architecturecenter/AWS_ac_ra_timeseriesprocessing_16.pdf
Question 6
You need a persistent and durable storage to trace call activity of an IVR (Interactive Voice Response) system. Call duration is mostly in the 2-3 minutes timeframe. Each traced call can be either active or terminated. An external application needs to know each minute the list of currently active calls, which are usually a few calls/second. Put once per month there is a periodic peak up to 1000 calls/second for a few hours. The system is open 24/7 and any downtime should be avoided. Historical data is periodically archived to files. Cost saving is a priority for this project.
What database implementation would better fit this scenario, keeping costs as low as possible?
Options:
A. Use RDS Multi-AZ with two tables, one for -Active calls" and one for -Terminated calls". In this way the
"Active calls_ table is always small and effective to access.
B. Use DynamoDB with a "Calls" table and a Global Secondary Index on a "IsActive'" attribute that is
present for active calls only In this way the Global Secondary index is sparse and more effective.
C. Use DynamoDB with a 'Calls" table and a Global secondary index on a 'State" attribute that can equal
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to "active" or "terminated" in this way the Global Secondary index can be used for all Items in the table.
D. Use RDS Multi-AZ with a "CALLS" table and an Indexed "STATE* field that can be equal to 'ACTIVE"
or -TERMINATED" In this way the SOL query Is optimized by the use of the Index.
Answer: B
Explanation:
Q: Can a global secondary index key be defined on non-unique attributes?
Yes. Unlike the primary key on a table, a GSI index does not require the indexed attributes to be unique. Q: Are GSI key attributes required in all items of a DynamoDB table?
No. GSIs are sparse indexes. Unlike the requirement of having a primary key, an item in a DynamoDB table does not have to contain any of the GSI keys. If a GSI key has both hash and range elements, and a table item omits either of them, then that item will not be indexed by the corresponding GSI. In such cases, a GSI can be very useful in efficiently locating items that have an uncommon attribute.
Reference: https://aws.amazon.com/dynamodb/faqs/
Question 7
Your company has HQ in Tokyo and branch offices all over the world and is using a logistics software with a multi-regional deployment on AWS in Japan, Europe and USA, The logistic software has a 3-tier architecture and currently uses MySQL 5.6 for data persistence. Each region has deployed its own database.
In the HQ region you run an hourly batch process reading data from every region to compute cross- regional reports that are sent by email to all offices this batch process must be completed as fast as possible to quickly optimize logistics how do you build the database architecture in order to meet the requirements'?
Options:
A. For each regional deployment, use RDS MySQL with a master in the region and a read replica in the
HQ region
B. For each regional deployment, use MySQL on EC2 with a master in the region and send hourly EBS
snapshots to the HQ region
C. For each regional deployment, use RDS MySQL with a master in the region and send hourly RDS
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snapshots to the HQ region
D. For each regional deployment, use MySQL on EC2 with a master in the region and use S3 to copy data
files hourly to the HQ region
E. Use Direct Connect to connect all regional MySQL deployments to the HQ region and reduce network
latency for the batch process
Answer: A
Question 8
A customer has a 10 GB AWS Direct Connect connection to an AWS region where they have a web application hosted on Amazon Elastic Computer Cloud (EC2). The application has dependencies on an on-premises mainframe database that uses a BASE (Basic Available. Sort stale Eventual consistency) rather than an ACID (Atomicity. Consistency isolation. Durability) consistency model. The application is exhibiting undesirable behavior because the database is not able to handle the volume of writes. How can you reduce the load on your on-premises database resources in the most cost-effective way?
Options:
A. Use an Amazon Elastic Map Reduce (EMR) S3DistCp as a synchronization mechanism between the
on-premises database and a Hadoop cluster on AWS.
B. Modify the application to write to an Amazon SQS queue and develop a worker process to flush the
queue to the on-premises database.
C. Modify the application to use DynamoDB to feed an EMR cluster which uses a map function to write to
the on-premises database.
D. Provision an RDS read-replica database on AWS to handle the writes and synchronize the two
databases using Data Pipeline.
Answer: A Explanation:
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Explanation:
Reference: https://aws.amazon.com/blogs/aws/category/amazon-elastic-map-reduce/
Question 9
You have launched an EC2 instance with four (4) 500 GB EBS Provisioned IOPS volumes attached The EC2 Instance Is EBS-Optimized and supports 500 Mbps throughput between EC2 and EBS The two EBS volumes are configured as a single RAID o device, and each Provisioned IOPS volume is provisioned with 4.000 IOPS (4 000 16KB reads or writes) for a total of 16.000 random IOPS on the instance The EC2 Instance initially delivers the expected 16 000 IOPS random read and write performance Sometime later in order to increase the total random I/O performance of the instance, you add an additional two 500 GB EBS Provisioned IOPS volumes to the RAID Each volume Is provisioned to 4.000 IOPs like the original four for a total of 24.000 IOPS on the EC2 instance Monitoring shows that the EC2 instance CPU utilization increased from 50% to 70%. but the total random IOPS measured at the instance level does not increase at all.
What is the problem and a valid solution?
Options:
A. Larger storage volumes support higher Provisioned IOPS rates: increase the provisioned volume
storage of each of the 6 EBS volumes to 1TB
B. The EBS-Optimized throughput limits the total IOPS that can be utilized use an EBS-Optimized
instance that provides larger throughput.
C. Small block sizes cause performance degradation, limiting the I'O throughput, configure the instance
device driver and file system to use 64KB blocks to increase throughput.
D. RAID 0 only scales linearly to about 4 devices, use RAID 0 with 4 EBS Provisioned IOPS volumes but
increase each Provisioned IOPS EBS volume to 6.000 IOPS.
E. The standard EBS instance root volume limits the total IOPS rate, change the instant root volume to
also be a 500GB 4.000 Provisioned IOPS volume.
Answer: E
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Question 10
You have recently joined a startup company building sensors to measure street noise and air quality in urban areas. The company has been running a pilot deployment of around 100 sensors for 3 months each sensor uploads 1KB of sensor data every minute to a backend hosted on AWS.
During the pilot, you measured a peak or 10 IOPS on the database, and you stored an average of 3GB of sensor data per month in the database.
The current deployment consists of a load-balanced auto scaled Ingestion layer using EC2 instances and a PostgreSQL RDS database with 500GB standard storage.
The pilot is considered a success and your CEO has managed to get the attention or some potential investors. The business plan requires a deployment of at least 100K sensors which needs to be supported by the backend. You also need to store sensor data for at least two years to be able to compare year over year Improvements.
To secure funding, you have to make sure that the platform meets these requirements and leaves room for further scaling. Which setup win meet the requirements?
Options:
A. Add an SQS queue to the ingestion layer to buffer writes to the RDS instance
B. Ingest data into a DynamoDB table and move old data to a Redshift cluster
C. Replace the RDS instance with a 6 node Redshift cluster with 96TB of storage
D. Keep the current architecture but upgrade RDS storage to 3TB and 10K provisioned IOPS Answer: C
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