Question # 1
| A company is designing an application on AWS that processes sensitive data. The
application stores and processes financial data for multiple customers.
To meet compliance requirements, the data for each customer must be encrypted
separately at rest by using a secure, centralized key management solution. The company
wants to use AWS Key Management Service (AWS KMS) to implement encryption.
Which solution will meet these requirements with the LEAST operational overhead'? |
| A. Generate a unique encryption key for each customer. Store the keys in an Amazon S3
bucket. Enable server-side encryption. | | B. Deploy a hardware security appliance in the AWS environment that securely stores customer-provided encryption keys. Integrate the security appliance with AWS KMS to
encrypt the sensitive data in the application | | C. Create a single AWS KMS key to encrypt all sensitive data across the application. | | D. Create separate AWS KMS keys for each customer's data that have granular access
control and logging enabled. |
D. Create separate AWS KMS keys for each customer's data that have granular access
control and logging enabled.
Explanation: This solution meets the requirement of encrypting each customer’s data
separately with the least operational overhead by leveraging AWS Key Management
Service (KMS).
Separate AWS KMS Keys: By creating separate KMS keys for each customer, you
can ensure that each customer’s data is encrypted with a unique key. This
approach satisfies the compliance requirement for separate encryption and
provides fine-grained control over access to the keys.
Granular Access Control: AWS KMS allows you to define key policies and use IAM
policies to grant specific permissions to the keys. This ensures that only authorized
users or services can access the keys, thereby maintaining the principle of least
privilege.
Logging and Monitoring: AWS KMS integrates with AWS CloudTrail, which logs all
key usage and management activities. This provides an audit trail that is essential
for meeting compliance requirements.
Question # 2
| A company deploys Amazon EC2 instances that run in a VPC. The EC2 instances load
source data into Amazon S3 buckets so that the data can be processed in the future.
According to compliance laws, the data must not be transmitted over the public internet.
Servers in the company's on-premises data center will consume the output from an
application that runs on the LC2 instances.
Which solution will meet these requirements? |
| A. Deploy an interface VPC endpoint for Amazon EC2. Create an AWS Site-to-Site VPN
connection between the company and the VPC. | | B. Deploys gateway VPC endpoint for Amazon S3 Set up an AWS Direct Connect
connection between the on-premises network and the VPC. | | C. Set up on AWS Transit Gateway connection from the VPC to the S3 buckets. Create an
AWS Site-to-Site VPN connection between the company and the VPC. | | D. Set up proxy EC2 instances that have routes to NAT gateways. Configure the proxy
EC2 instances lo fetch S3 data and feed the application instances. |
B. Deploys gateway VPC endpoint for Amazon S3 Set up an AWS Direct Connect
connection between the on-premises network and the VPC.
Explanation:
Understanding the Requirement: EC2 instances need to upload data to S3 without
using the public internet, and on-premises servers need to consume this data.
Question # 3
| A company hosts its core network services, including directory services and DNS, in its onpremises
data center. The data center is connected to the AWS Cloud using AWS Direct
Connect (DX). Additional AWS accounts are planned that will require quick, cost-effective,
and consistent access to these network services.
What should a solutions architect implement to meet these requirements with the LEAST
amount of operational overhead? |
| A. Create a DX connection in each new account. Route the network traffic to the onpremises
servers | | B. Configure VPC endpoints in the DX VPC for all required services. Route the network
traffic to the on-premises servers. | | C. Create a VPN connection between each new account and the DX VPC. Route the
network traffic to the on-premises servers. | | D. Configure AWS Transit Gateway between the accounts. Assign DX to the transit
gateway and route network traffic to the on-premises servers. |
D. Configure AWS Transit Gateway between the accounts. Assign DX to the transit
gateway and route network traffic to the on-premises servers.
Explanation:
Requirement Analysis: Need quick, cost-effective, and consistent access to onpremises
network services from multiple AWS accounts.
AWS Transit Gateway: Centralizes and simplifies network management by
connecting VPCs and on-premises networks.
Direct Connect Integration: Assigning DX to the transit gateway ensures consistent
and high-performance connectivity.
Operational Overhead: Minimal because Transit Gateway simplifies routing and
management.
Implementation:
Conclusion: This solution provides a scalable, cost-effective, and low-overhead
method to meet connectivity requirements.
Question # 4
| A company has a mobile app for customers The app's data is sensitive and must be
encrypted at rest The company uses AWS Key Management Service (AWS KMS)
The company needs a solution that prevents the accidental deletion of KMS keys The
solution must use Amazon Simple Notification Service (Amazon SNS) to send an email
notification to administrators when a user attempts to delete a KMS key
Which solution will meet these requirements with the LEAST operational overhead'' |
| A. Create an Amazon EventBndge rule that reacts when a user tries to delete a KMS key
Configure an AWS Config rule that cancels any deletion of a KMS key Add the AWS Config
rule as a target of the EventBridge rule Create an SNS topic that notifies the administrators | | B. Create an AWS Lambda function that has custom logic to prevent KMS key deletion
Create an Amazon CloudWatch alarm that is activated when a user tries to delete a KMS
key Create an Amazon EventBridge rule that invokes the Lambda function when the
DeleteKey operation is performed Create an SNS topic Configure the EventBndge rule to
publish an SNS message that notifies the administrators | | C. Create an Amazon EventBndge rule that reacts when the KMS DeleteKey operation is
performed Configure the rule to initiate an AWS Systems Manager Automation
runbook Configure the runbook to cancel the deletion of the KMS key Create an SNS topic
Configure the EventBndge rule to publish an SNS message that notifies the administrators. | | D. Create an AWS CloudTrail trail Configure the trail to delrver logs to a new Amazon
CloudWatch log group Create a CloudWatch alarm based on the metric filter for the
CloudWatch log group Configure the alarm to use Amazon SNS to notify the administrators
when the KMS DeleteKey operation is performed |
C. Create an Amazon EventBndge rule that reacts when the KMS DeleteKey operation is
performed Configure the rule to initiate an AWS Systems Manager Automation
runbook Configure the runbook to cancel the deletion of the KMS key Create an SNS topic
Configure the EventBndge rule to publish an SNS message that notifies the administrators.
Explanation: This solution meets the requirements with the least operational overhead
because it uses AWS services that are fully managed and scalable. The EventBridge rule
can detect the DeleteKey operation from the AWS KMS API and trigger the Systems
Manager Automation runbook, which can execute a predefined workflow to cancel the key
deletion. The EventBridge rule can also publish an SNS message to the topic that sends an
email notification to the administrators. This way, the company can prevent the accidental
deletion of KMS keys and notify the administrators of any attempts to delete them.
Option A is not a valid solution because AWS Config rules are used to evaluate the
configuration of AWS resources, not to cancel the deletion of KMS keys. Option B is not a
valid solution because it requires creating and maintaining a custom Lambda function that
has logic to prevent KMS key deletion, which adds operational overhead. Option D is not a
valid solution because it only notifies the administrators of the DeleteKey operation, but
does not cancel it.
Question # 5
| A company wants to build a logging solution for its multiple AWS accounts. The company
currently stores the logs from all accounts in a centralized account. The company has
created an Amazon S3 bucket in the centralized account to store the VPC flow logs and
AWS CloudTrail logs. All logs must be highly available for 30 days for frequent analysis,
retained tor an additional 60 days tor backup purposes, and deleted 90 days after creation.
Which solution will meet these requirements MOST cost-effectively? |
| A. Transition objects to the S3 Standard storage class 30 days after creation. Write an
expiration action that directs Amazon S3 to delete objects after 90 days. | | B. Transition objects lo the S3 Standard-Infrequent Access (S3 Standard-IA) storage class
30 days after creation Move all objects to the S3 Glacier Flexible
Retrieval storage class after 90 days. Write an expiration action that directs Amazon S3 to
delete objects after 90 days. | | C. Transition objects to the S3 Glacier Flexible Retrieval storage class 30 days after
creation. Write an expiration action that directs Amazon S3 to delete objects alter 90 days. | | D. Transition objects to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage
class 30 days after creation. Move all objects to the S3 Glacier Flexible Retrieval storage
class after 90 days. Write an expiration action that directs Amazon S3 to delete objects
after 90 days. |
D. Transition objects to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage
class 30 days after creation. Move all objects to the S3 Glacier Flexible Retrieval storage
class after 90 days. Write an expiration action that directs Amazon S3 to delete objects
after 90 days.
Explanation:
Understanding the Requirement: The company needs logs to be highly available
for 30 days for frequent analysis, retained for an additional 60 days for backup,
and deleted after 90 days.
Question # 6
| A company has a web application in the AWS Cloud and wants to collect transaction data
in real time. The company wants to prevent data duplication and does not want to manage
infrastructure. The company wants to perform additional processing on the data after the
data is collected.
Which solution will meet these requirements? |
| A. Configure an Amazon Simple Queue Service (Amazon SOS) FIFO queue. Configure an
AWS Lambda function with an event source mapping for the FIFO queue to process the
data. | | B. Configure an Amazon Simple Queue Service (Amazon SQS) FIFO queue Use an AWS
Batch job to remove duplicate data from the queue Configure an AWS
Lambda function to process the data. | | C. Use Amazon Kinesis Data Streams to send the Incoming transaction data to an AWS
Batch job that removes duplicate data. Launch an Amazon EC2 instance that runs a
custom script lo process the data. | | D. Set up an AWS Step Functions state machine to send incoming transaction data to an
AWS Lambda function to remove duplicate data. Launch an Amazon EC2 instance that
runs a custom script to process the data. |
A. Configure an Amazon Simple Queue Service (Amazon SOS) FIFO queue. Configure an
AWS Lambda function with an event source mapping for the FIFO queue to process the
data.
Explanation:
Understanding the Requirement: The company needs to collect transaction data in
real time, avoid data duplication, and perform additional processing without
managing infrastructure.
Question # 7
| A company has an Amazon S3 data lake The company needs a solution that transforms
the data from the data lake and loads the data into a data warehouse every day The data
warehouse must have massively parallel processing (MPP) capabilities.
Data analysts then need to create and train machine learning (ML) models by using SQL
commands on the data The solution must use serverless AWS services wherever possible
Which solution will meet these requirements? |
| A. Run a daily Amazon EMR job to transform the data and load the data into Amazon
Redshift Use Amazon Redshift ML to create and train the ML models | | B. Run a daily Amazon EMR job to transform the data and load the data into Amazon
Aurora Serverless Use Amazon Aurora ML to create and train the ML models | | C. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift
Serverless Use Amazon Redshift ML to create and tram the ML models | | D. Run a daily AWS Glue job to transform the data and load the data into Amazon Athena
tables Use Amazon Athena ML to create and train the ML models |
C. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift
Serverless Use Amazon Redshift ML to create and tram the ML models
Explanation: AWS Glue is a fully managed extract, transform, and load (ETL) service that
makes it easy to prepare and load your data for analytics. AWS Glue can automatically
discover your data in Amazon S3 and catalog it, so you can query and search the data
using SQL. AWS Glue can also run serverless ETL jobs using Apache Spark and Python to
transform and load your data into various destinations, such as Amazon Redshift, Amazon
Athena, or Amazon Aurora. AWS Glue is a serverless service, so you only pay for the
resources consumed by the jobs, and you don’t need to provision or manage any
infrastructure.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables
you to use standard SQL and your existing business intelligence (BI) tools to analyze your
data. Amazon Redshift also supports massively parallel processing (MPP), which means it
can distribute and execute queries across multiple nodes in parallel, delivering fast
performance and scalability. Amazon Redshift Serverless is a new option that automatically
scales query compute capacity based on the queries being run, so you don’t need to
manage clusters or capacity. You only pay for the query processing time and the storage
consumed by your data.
Amazon Redshift ML is a feature that enables you to create, train, and deploy machine
learning (ML) models using familiar SQL commands. Amazon Redshift ML can
automatically discover the best model and hyperparameters for your data, and store the
model in Amazon SageMaker, a fully managed service that provides a comprehensive set
of tools for building, training, and deploying ML models. You can then use SQL functions to
apply the model to your data in Amazon Redshift and generate predictions.
The combination of AWS Glue, Amazon Redshift Serverless, and Amazon Redshift ML
meets the requirements of the question, as it provides a serverless, scalable, and SQLbased
solution to transform, load, and analyze the data from the Amazon S3 data lake, and
to create and train ML models on the data.
Question # 8
| A company wants to isolate its workloads by creating an AWS account for each workload.
The company needs a solution that centrally manages networking components for the
workloads. The solution also must create accounts with automatic security controls
(guardrails).
Which solution will meet these requirements with the LEAST operational overhead? |
| A. Use AWS Control Tower to deploy accounts. Create a networking account that has a
VPC with private subnets and public subnets. Use AWS Resource Access Manager (AWS
RAM) to share the subnets with the workload accounts. | | B. Use AWS Organizations to deploy accounts. Create a networking account that has a VPC with private subnets and public subnets. Use AWS Resource Access Manager (AWS
RAM) to share the subnets with the workload accounts. | | C. Use AWS Control Tower to deploy accounts. Deploy a VPC in each workload account.
Configure each VPC to route through an inspection VPC by using a transit gateway
attachment. | | D. Use AWS Organizations to deploy accounts. Deploy a VPC in each workload account.
Configure each VPC to route through an inspection VPC by using a transit gateway
attachment. |
A. Use AWS Control Tower to deploy accounts. Create a networking account that has a
VPC with private subnets and public subnets. Use AWS Resource Access Manager (AWS
RAM) to share the subnets with the workload accounts.
Explanation:
AWS Control Tower: Provides a managed service to set up and govern a secure,
multi-account AWS environment based on AWS best practices. It automates the
setup of AWS Organizations and applies security controls (guardrails).
Networking Account:
AWS Resource Access Manager (AWS RAM):
Operational Efficiency: Using AWS Control Tower simplifies the setup and
governance of multiple AWS accounts, while AWS RAM facilitates centralized
management of networking resources, reducing operational overhead and
ensuring consistent security and compliance.
Question # 9
| A company is developing a new application that uses a relational database to store user
data and application configurations. The company expects the application to have steady
user growth. The company expects the database usage to be variable and read-heavy,
with occasional writes.
The company wants to cost-optimize the database solution. The company wants to use an
AWS managed database solution that will provide the necessary performance.
Which solution will meet these requirements MOST cost-effectively? |
| A. Deploy the database on Amazon RDS. Use Provisioned IOPS SSD storage to ensure
consistent performance for read and write operations. | | B. Deploy the database on Amazon Aurora Serveriess to automatically scale the database
capacity based on actual usage to accommodate the workload. | | C. Deploy the database on Amazon DynamoDB. Use on-demand capacity mode to
automatically scale throughput to accommodate the workload. | | D. Deploy the database on Amazon RDS Use magnetic storage and use read replicas to
accommodate the workload |
B. Deploy the database on Amazon Aurora Serveriess to automatically scale the database
capacity based on actual usage to accommodate the workload.
Explanation: Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling
configuration for Amazon Aurora. It automatically adjusts the database's capacity based on
the current demand, which is ideal for workloads with variable and unpredictable usage
patterns. Since the application is expected to be read-heavy with occasional writes and
steady growth, Aurora Serverless can provide the necessary performance without requiring
the management of database instances.
Cost-Optimization: Aurora Serverless only charges for the database capacity you
use, making it a more cost-effective solution compared to always running
provisioned database instances, especially for workloads with fluctuating demand.
Scalability: It automatically scales database capacity up or down based on actual
usage, ensuring that you always have the right amount of resources available.
Performance: Aurora Serverless is built on the same underlying storage as
Amazon Aurora, providing high performance and availability.
Question # 10
| A company runs containers in a Kubernetes environment in the company's local data
center. The company wants to use Amazon Elastic Kubernetes Service (Amazon EKS) and
other AWS managed services Data must remain locally in the company's data center and
cannot be stored in any remote site or cloud to maintain compliance
Which solution will meet these requirements? |
| A. Deploy AWS Local Zones in the company's data center | | B. Use an AWS Snowmobile in the company's data center | | C. Install an AWS Outposts rack in the company's data center | | D. Install an AWS Snowball Edge Storage Optimized node in the data center |
C. Install an AWS Outposts rack in the company's data center
Explanation: AWS Outposts is a fully managed service that delivers AWS infrastructure
and services to virtually any on-premises or edge location for a consistent hybrid
experience. AWS Outposts supports Amazon EKS, which is a managed service that makes
it easy to run Kubernetes on AWS and on-premises. By installing an AWS Outposts rack in
the company’s data center, the company can run containers in a Kubernetes environment
using Amazon EKS and other AWS managed services, while keeping the data locally in the
company’s data center and meeting the compliance requirements. AWS Outposts also
provides a seamless connection to the local AWS Region for access to a broad range of
AWS services.
Option A is not a valid solution because AWS Local Zones are not deployed in the
company’s data center, but in large metropolitan areas closer to end users. AWS Local
Zones are owned, managed, and operated by AWS, and they provide low-latency access
to the public internet and the local AWS Region. Option B is not a valid solution because
AWS Snowmobile is a service that transports exabytes of data to AWS using a 45-foot long
ruggedized shipping container pulled by a semi-trailer truck. AWS Snowmobile is not
designed for running containers or AWS managed services on-premises, but for largescale
data migration. Option D is not a valid solution because AWS Snowball Edge Storage
Optimized is a device that provides 80 TB of HDD or 210 TB of NVMe storage capacity for
data transfer and edge computing. AWS Snowball Edge Storage Optimized does not
support Amazon EKS or other AWS managed services, and it is not suitable for running
containers in a Kubernetes environment.
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AWS Certified Solutions Architect - Associate (SAA-C03) Exam Dumps
Exam Code: SAA-C03
Exam Name: AWS Certified Solutions Architect - Associate (SAA-C03)
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AWS Certified Solutions Architect - Associate (SAA-C03) exam uses a scaled scoring system between 100 and 1000. You need a score of 720 to pass. AWS doesn't reveal the exact percentage of questions you need to get right, as the difficulty can vary between exam forms.
Preparation time for the SAA-C03 exam varies, typically ranging from 2 to 4 months, depending on your prior experience with AWS and cloud computing. For beginners, a more extensive study period is advisable. Regular study, practical hands-on experience with AWS services, and utilizing resources like AWS documentation, online courses, and AWS Solutions Architect Associate practice exam are essential.
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