Practice Free SAA-C03 Exam Online Questions
Question #41
A company stores raw collected data in an Amazon S3 bucket. The data is used for several types of analytics on behalf of the company’s customers. The type of analytics requested to determines the access pattern on the S3 objects.
The company cannot predict or control the access pattern. The company wants to reduce its S3 costs.
which solution will meet these requirements?
- A . Use S3 replication to transition infrequently accessed objects to S3 Standard-Infrequent Access (S3 Standard-1A)
- B . Use S3 Lifecycle rules to transition objects from S3 Standard to Standard-Infrequent Access (S3 Standard-1A).
- C . Use S3 Lifecycle rules for transition objects from S3 Standard to S3 Intelligent-Tiering.
- D . Use S3 Inventory to identify and transition objects that have not been accessed from S3 Standard to S3 Intelligent-Tiering.
Correct Answer: C
C
Explanation:
S3 Intelligent-Tiering is a storage class that automatically reduces storage costs by moving data to the most cost-effective access tier based on access frequency. It has two access tiers: frequent access and infrequent access. Data is stored in the frequent access tier by default, and moved to the infrequent access tier after 30 consecutive days of no access. If the data is accessed again, it is moved back to the frequent access tier1. By using S3 Lifecycle rules to transition objects from S3 Standard to S3 Intelligent-Tiering, the solution can reduce S3 costs for data with unknown or changing access patterns.
C
Explanation:
S3 Intelligent-Tiering is a storage class that automatically reduces storage costs by moving data to the most cost-effective access tier based on access frequency. It has two access tiers: frequent access and infrequent access. Data is stored in the frequent access tier by default, and moved to the infrequent access tier after 30 consecutive days of no access. If the data is accessed again, it is moved back to the frequent access tier1. By using S3 Lifecycle rules to transition objects from S3 Standard to S3 Intelligent-Tiering, the solution can reduce S3 costs for data with unknown or changing access patterns.
Question #42
A company hosts an Amazon EC2 instance in a private subnet in a new VPC. The VPC also has a public subnet that has the default route set to an internet gateway. The private subnet does not have outbound internet access.
The EC2 instance needs to have the ability to download monthly security updates from an outside vendor. However, the company must block any connections that are initiated from the internet.
Which solution will meet these requirements?
- A . Configure the private subnet route table to use the internet gateway as the default route.
- B . Create a NAT gateway in the public subnet. Configure the private subnet route table to use the NAT gateway as the default route.
- C . Create a NAT instance in the private subnet. Configure the private subnet route table to use the NAT instance as the default route.
- D . Create a NAT instance in the private subnet. Configure the private subnet route table to use the internet gateway as the default route.
Correct Answer: B
Question #43
A company has a large data workload that runs for 6 hours each day. The company cannot lose any data while the process is running. A solutions architect is designing an Amazon EMR cluster configuration to support this critical data workload.
Which solution will meet these requirements MOST cost-effectively?
- A . Configure a long-running cluster that runs the primary node and core nodes on On-Demand Instances and the task nodes on Spot Instances.
- B . Configure a transient cluster that runs the primary node and core nodes on On-Demand Instances and the task nodes on Spot Instances.
- C . Configure a transient cluster that runs the primary node on an On-Demand Instance and the core nodes and task nodes on Spot Instances.
- D . Configure a long-running cluster that runs the primary node on an On-Demand Instance, the core nodes on Spot Instances, and the task nodes on Spot Instances.
Correct Answer: B
B
Explanation:
For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure a transient cluster (which runs for the duration of the job and then terminates) with On-Demand Instances for the primary and core nodes, and Spot Instances for the task nodes. Here’s why:
Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.
Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for
non-critical tasks as the framework can handle retries.
A transient cluster is more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it’s not needed.
Option A: A long-running cluster may result in unnecessary costs when the cluster isn’t being used.
Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.
Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.
AWS
Reference: Amazon EMR Cluster Management
Using Spot Instances in EMR
B
Explanation:
For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure a transient cluster (which runs for the duration of the job and then terminates) with On-Demand Instances for the primary and core nodes, and Spot Instances for the task nodes. Here’s why:
Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.
Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for
non-critical tasks as the framework can handle retries.
A transient cluster is more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it’s not needed.
Option A: A long-running cluster may result in unnecessary costs when the cluster isn’t being used.
Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.
Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.
AWS
Reference: Amazon EMR Cluster Management
Using Spot Instances in EMR
Question #44
A company runs an application on AWS. The application receives inconsistent amounts of usage. The application uses AWS Direct Connect to connect to an on-premises MySQL-compatible database. The on-premises database consistently uses a minimum of 2 GiB of memory.
The company wants to migrate the on-premises database to a managed AWS service. The company wants to use auto scaling capabilities to manage unexpected workload increases.
Which solution will meet these requirements with the LEAST administrative overhead?
- A . Provision an Amazon DynamoDB database with default read and write capacity settings.
- B . Provision an Amazon Aurora database with a minimum capacity of 1 Aurora capacity unit (ACU).
- C . Provision an Amazon Aurora Serverless v2 database with a minimum capacity of 1 Aurora capacity
unit (ACU). - D . Provision an Amazon RDS for MySQL database with 2 GiB of memory.
Correct Answer: C
C
Explanation:
it allows the company to migrate the on-premises database to a managed AWS service that supports auto scaling capabilities and has the least administrative overhead. Amazon Aurora Serverless v2 is a configuration of Amazon Aurora that automatically scales compute capacity based on workload demand. It can scale from hundreds to hundreds of thousands of transactions in a fraction of a second. Amazon Aurora Serverless v2 also supports MySQL-compatible databases and AWS Direct Connect connectivity.
Reference: Amazon Aurora Serverless v2
Connecting to an Amazon Aurora DB Cluster
C
Explanation:
it allows the company to migrate the on-premises database to a managed AWS service that supports auto scaling capabilities and has the least administrative overhead. Amazon Aurora Serverless v2 is a configuration of Amazon Aurora that automatically scales compute capacity based on workload demand. It can scale from hundreds to hundreds of thousands of transactions in a fraction of a second. Amazon Aurora Serverless v2 also supports MySQL-compatible databases and AWS Direct Connect connectivity.
Reference: Amazon Aurora Serverless v2
Connecting to an Amazon Aurora DB Cluster
Question #45
A company wants to host a scalable web application on AWS. The application will be accessed by users from different geographic regions of the world. Application users will be able to download and upload unique data up to gigabytes in size. The development team wants a cost-effective solution to minimize upload and download latency and maximize performance.
What should a solutions architect do to accomplish this?
- A . Use Amazon S3 with Transfer Acceleration to host the application.
- B . Use Amazon S3 with CacheControl headers to host the application.
- C . Use Amazon EC2 with Auto Scaling and Amazon CloudFront to host the application.
- D . Use Amazon EC2 with Auto Scaling and Amazon ElastiCache to host the application.
Correct Answer: C
C
Explanation:
This answer is correct because it meets the requirements of hosting a scalable web application that can handle large data transfers from different geographic regions. Amazon EC2 provides scalable compute capacity for hosting web applications. Auto Scaling can automatically adjust the number of EC2 instances based on the demand and traffic patterns. Amazon CloudFront is a content delivery network (CDN) that can cache static and dynamic content at edge locations closer to the users, reducing latency and improving performance. CloudFront can also use S3 Transfer Acceleration to speed up the transfers between S3 buckets and CloudFront edge locations.
Reference: https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html
https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/Introduction.html
https://aws.amazon.com/s3/transfer-acceleration/
C
Explanation:
This answer is correct because it meets the requirements of hosting a scalable web application that can handle large data transfers from different geographic regions. Amazon EC2 provides scalable compute capacity for hosting web applications. Auto Scaling can automatically adjust the number of EC2 instances based on the demand and traffic patterns. Amazon CloudFront is a content delivery network (CDN) that can cache static and dynamic content at edge locations closer to the users, reducing latency and improving performance. CloudFront can also use S3 Transfer Acceleration to speed up the transfers between S3 buckets and CloudFront edge locations.
Reference: https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html
https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/Introduction.html
https://aws.amazon.com/s3/transfer-acceleration/
Question #46
A company has NFS servers in an on-premises data center that need to periodically back up small amounts of data to Amazon S3.
Which solution meets these requirements and is MOST cost-effective?
- A . Set up AWS Glue to copy the data from the on-premises servers to Amazon S3.
- B . Set up an AWS DataSync agent on the on-premises servers, and sync the data to Amazon S3.
- C . Set up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3.
- D . Set up an AWS Direct Connect connection between the on-premises data center and a VPC, and copy the data to Amazon S3.
Correct Answer: B
B
Explanation:
AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.
The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.
Reference: AWS DataSync
How AWS DataSync works
AWS DataSync FAQs
B
Explanation:
AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.
The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.
Reference: AWS DataSync
How AWS DataSync works
AWS DataSync FAQs
Question #47
A development team uses multiple AWS accounts for its development, staging, and production environments. Team members have been launching large Amazon EC2 instances that are underutilized. A solutions architect must prevent large instances from being launched in all accounts.
How can the solutions architect meet this requirement with the LEAST operational overhead?
- A . Update the 1AM policies to deny the launch of large EC2 instances. Apply the policies to all users.
- B . Define a resource in AWS Resource Access Manager that prevents the launch of large EC2 instances.
- C . Create an (AM role in each account that denies the launch of large EC2 instances. Grant the developers 1AM group access to the role.
- D . Create an organization in AWS Organizations in the management account with the default policy. Create a service control policy (SCP) that denies the launch of large EC2 Instances, and apply it to the AWS accounts.
Correct Answer: D
D
Explanation:
Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.
Analysis of Options:
IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.
AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.
IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.
Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.
Best Solution:
Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts,
effectively preventing the launch of large EC2 instances.
Reference: AWS Organizations and SCPs
D
Explanation:
Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.
Analysis of Options:
IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.
AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.
IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.
Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.
Best Solution:
Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts,
effectively preventing the launch of large EC2 instances.
Reference: AWS Organizations and SCPs
Question #48
A solution architect is designing a company’s disaster recovery (DR) architecture. The company has a MySQL database that runs on an Amazon EC2 instance in a private subnet with scheduled backup.
The DR design to include multiple AWS Regions.
Which solution will meet these requiements with the LEAST operational overhead?
- A . Migrate the MySQL database to multiple EC2 instances. Configure a standby EC2 instance in the DR Region Turn on replication.
- B . Migrate the MySQL database to Amazon RDS. Use a Multi-AZ deployment. Turn on read replication for the primary DB instance in the different Availability Zones.
- C . Migrate the MySQL database to an Amazon Aurora global database. Host the primary DB cluster in the primary Region. Host the secondary DB cluster in the DR Region.
- D . Store the schedule backup of the MySQL database in an Amazon S3 bucket that is configured for S3 Cross-Region Replication (CRR). Use the data backup to restore the database in the DR Region.
Correct Answer: C
C
Explanation:
Migrate MySQL database to an Amazon Aurora global database is the best solution because it requires minimal operational overhead. Aurora is a managed service that provides automatic failover, so standby instances do not need to be manually configured. The primary DB cluster can be hosted in the primary Region, and the secondary DB cluster can be hosted in the DR Region. This approach ensures that the data is always available and up-to-date in multiple Regions, without requiring significant manual intervention.
C
Explanation:
Migrate MySQL database to an Amazon Aurora global database is the best solution because it requires minimal operational overhead. Aurora is a managed service that provides automatic failover, so standby instances do not need to be manually configured. The primary DB cluster can be hosted in the primary Region, and the secondary DB cluster can be hosted in the DR Region. This approach ensures that the data is always available and up-to-date in multiple Regions, without requiring significant manual intervention.
Question #49
A company is deploying a critical application by using Amazon RDS for MySQL. The application must be highly available and must recover automatically. The company needs to support interactive users (transactional queries) and batch reporting (analytical queries) with no more than a 4-hour lag. The analytical queries must not affect the performance of the transactional queries.
- A . Configure Amazon RDS for MySQL in a Multi-AZ DB instance deployment with one standby instance. Point the transactional queries to the primary DB instance. Point the analytical queries to a secondary DB instance that runs in a different Availability Zone.
- B . Configure Amazon RDS for MySQL in a Multi-AZ DB cluster deployment with two standby instances. Point the transactional queries to the primary DB instance. Point the analytical queries to the reader endpoint.
- C . Configure Amazon RDS for MySQL to use multiple read replicas across multiple Availability Zones. Point the transactional queries to the primary DB instance. Point the analytical queries to one of the replicas in a different Availability Zone.
- D . Configure Amazon RDS for MySQL as the primary database for the transactional queries with automated backups enabled. Configure automated backups. Each night, create a read-only database from the most recent snapshot to support the analytical queries. Terminate the previously created database.
Correct Answer: C
C
Explanation:
Key Requirements:
High availability and automatic recovery.
Separate transactional and analytical queries with minimal performance impact.
Allow up to a 4-hour lag for analytical queries.
Analysis of Options:
Option A:
Multi-AZ deployments provide high availability but do not include read replicas for separating transactional and analytical queries.
Analytical queries on the secondary DB instance would impact the transactional workload.
Incorrect Approach: Does not meet the requirement of query separation.
Option B:
Multi-AZ DB clusters provide high availability and include a reader endpoint. However, these are better suited for Aurora and not RDS for MySQL.
Incorrect Approach: Not applicable to standard RDS for MySQL.
Option C:
Multiple read replicas allow separation of transactional and analytical workloads.
Queries can be pointed to a replica in a different AZ, ensuring no impact on transactional queries.
Correct Approach: Meets all requirements with high availability and query separation.
Option D:
Creating nightly snapshots and read-only databases adds significant operational overhead and does not support the 4-hour lag requirement.
Incorrect Approach: Not practical for dynamic query separation.
AWS Solution Architect
Reference: Amazon RDS Read Replicas
Multi-AZ Deployments
C
Explanation:
Key Requirements:
High availability and automatic recovery.
Separate transactional and analytical queries with minimal performance impact.
Allow up to a 4-hour lag for analytical queries.
Analysis of Options:
Option A:
Multi-AZ deployments provide high availability but do not include read replicas for separating transactional and analytical queries.
Analytical queries on the secondary DB instance would impact the transactional workload.
Incorrect Approach: Does not meet the requirement of query separation.
Option B:
Multi-AZ DB clusters provide high availability and include a reader endpoint. However, these are better suited for Aurora and not RDS for MySQL.
Incorrect Approach: Not applicable to standard RDS for MySQL.
Option C:
Multiple read replicas allow separation of transactional and analytical workloads.
Queries can be pointed to a replica in a different AZ, ensuring no impact on transactional queries.
Correct Approach: Meets all requirements with high availability and query separation.
Option D:
Creating nightly snapshots and read-only databases adds significant operational overhead and does not support the 4-hour lag requirement.
Incorrect Approach: Not practical for dynamic query separation.
AWS Solution Architect
Reference: Amazon RDS Read Replicas
Multi-AZ Deployments
Question #50
A company hosts a three-tier web application in the AWS Cloud. A Multi-AZ Amazon RDS for MySQL server forms the database layer. Amazon ElastiCache forms the cache layer. The company wants a caching strategy that adds or updates data in the cache when a customer adds an item to the database. The data in the cache must always match the data in the database.
Which solution will meet these requirements?
- A . Implement the lazy loading caching strategy
- B . Implement the write-through caching strategy.
- C . Implement the adding TTL caching strategy.
- D . Implement the AWS AppConfig caching strategy.
Correct Answer: B
B
Explanation:
A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.
A lazy loading caching strategy only loads data into the cache when it is requested, and updates the
cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.
An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.
An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.
Reference: Caching strategies – Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache
B
Explanation:
A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.
A lazy loading caching strategy only loads data into the cache when it is requested, and updates the
cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.
An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.
An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.
Reference: Caching strategies – Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache