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| Section | Weight | Objectives |
|---|---|---|
| Design High-Performing Architectures | 24% | - Performance scaling
|
| Design Resilient Architectures | 26% | - Fault tolerance and recovery
|
| Design Secure Architectures | 30% | - Secure access to AWS resources
|
| Design Cost-Optimized Architectures | 20% | - Cost-effective resource selection
|
Das Zertifikat von Amazon SAA-C03 kann Ihnen sehr viel helfen. Mit dem Zertifikat können Sie befördert werden. Und Ihr Lebensniveau wird sich sicher verbessern. Das Amazon SAA-C03 Zertifikat bedeutet für Sie einen großen Reichtum. Die Amazon SAA-C03 (AWS Certified Solutions Architect - Associate) Zertifizierungsprüfung ist ein Test für die IT-Fachleute. Die Prüfungsmaterialien zur Amazon SAA-C03 Zertifizierungsprüfung sind die besten und umfassendsten. Nun stellt Fast2test Ihnen die besten und optimalen Prüfungsmaterialien zur SAA-C03 Zertifizierungsprüfung zur Verfügung, die Prüfungsfragen und Antworten enthalten.
515. Frage
A solutions architect is designing the architecture for a software demonstration environment The environment will run on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB) The system will experience significant increases in traffic during working hours but Is not required to operate on weekends.
Which combination of actions should the solutions architect take to ensure that the system can scale to meet demand? (Select TWO)
Antwort: A,C
Begründung:
https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling-target-tracking.html#target-tracking- choose-metrics A target tracking scaling policy is a type of dynamic scaling policy that adjusts the capacity of an Auto Scaling group based on a specified metric and a target value1. A target tracking scaling policy can automatically scale out or scale in the Auto Scaling group to keep the actual metric value at or near the target value1. A target tracking scaling policy is suitable for scenarios where the load on the application changes frequently and unpredictably, such as during working hours2.
To meet the requirements of the scenario, the solutions architect should use a target tracking scaling policy to scale the Auto Scaling group based on instance CPU utilization. Instance CPU utilization is a common metric that reflects the demand on the application1. The solutions architect should specify a target value that represents the ideal average CPU utilization level for the application, such as 50 percent1. Then, the Auto Scaling group will scale out or scale in to maintain that level of CPU utilization.
Scheduled scaling is a type of scaling policy that performs scaling actions based on a date and time3
. Scheduled scaling is suitable for scenarios where the load on the application changes periodically and predictably, such as on weekends2.
To meet the requirements of the scenario, the solutions architect should also use scheduled scaling to change the Auto Scaling group minimum, maximum, and desired capacity to zero for weekends. This way, the Auto Scaling group will terminate all instances on weekends when they are not required to operate. The solutions architect should also revert to the default values at the start of the week, so that the Auto Scaling group can resume normal operation.
516. Frage
A company is building a shopping application on AWS. The application offers a catalog that changes once each month and needs to scale with traffic volume. The company wants the lowest possible latency from the application. Data from each user's shopping carl needs to be highly available. User session data must be available even if the user is disconnected and reconnects.
What should a solutions architect do to ensure that the shopping cart data is preserved at all times?
Antwort: A
Begründung:
To ensure that the shopping cart data is preserved at all times, a solutions architect should configure Amazon ElastiCache for Redis to cache catalog data from Amazon DynamoDB and shopping cart data from the user's session. This solution has the following benefits:
It offers the lowest possible latency from the application, as ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications1.
It scales with traffic volume, as ElastiCache for Redis supports horizontal scaling by adding more nodes or shards to the cluster, and vertical scaling by changing the node type2.
It is highly available, as ElastiCache for Redis supports replication across multiple Availability Zones and automatic failover in case of a primary node failure3.
It preserves user session data even if the user is disconnected and reconnects, as ElastiCache for Redis can store session data, such as user login information and shopping cart contents, in a persistent and durable manner using snapshots or AOF (append-only file) persistence4.
References:
1: https://aws.amazon.com/elasticache/redis/
2: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Scaling.html
3: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Replication.html
4: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/backups.html
517. Frage
[Design Resilient Architectures]
A company is developing a real-time multiplayer game that uses UDP for communications between the client and servers In an Auto Scaling group Spikes in demand are anticipated during the day, sothe game server platform must adapt accordingly Developers want to store gamer scores and other non-relational data in a database solution that will scale without intervention Which solution should a solutions architect recommend?
Antwort: A
Begründung:
A Network Load Balancer is a type of load balancer that operates at the connection level (Layer 4) and can load balance both TCP and UDP traffic1. A Network Load Balancer is suitable for scenarios where high performance and low latency are required, such as real-time multiplayer games1. A Network Load Balancer can also handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone1.
To meet the requirements of the scenario, the solutions architect should use a Network Load Balancer for traffic distribution between the EC2 instances in the Auto Scaling group. The Network Load Balancer can route UDPtraffic from the client to the servers onthe appropriate port2. TheNetwork Load Balancer can also support TLS offloading for secure communications between the client and servers1.
Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with consistent performance and low latency3. Amazon DynamoDB on-demand is a flexible billing option that requires no capacity planning and charges only for the read and write requests that are performed on the tables3. Amazon DynamoDB on-demand is ideal forscenarios where the application traffic is unpredictable or sporadic, such as gaming applications3.
To meet the requirements of the scenario, the solutions architect should use Amazon DynamoDB on-demand for data storage. Amazon DynamoDB on-demand can store gamer scores and other non-relational data without intervention from the developers. Amazon DynamoDB on-demand can also scale automatically to handle any level of requesttraffic without affecting performance or availability3.
518. Frage
Organizers for a global event want to put daily reports online as static HTML pages. The pages are expected to generate millions of views from users around the world. The files are stored In an Amazon S3 bucket. A solutions architect has been asked to design an efficient and effective solution.
Which action should the solutions architect take to accomplish this?
Antwort: C
519. Frage
A company needs a solution to ingest millions of small telemetry records each minute. The records come from globally distributed mobile devices and run near real-time aggregations. The solution must give multiple downstream applications the ability to replay the ingested data from any point in time. The solution must also scale seamlessly without manual sharding.
Which solution will meet these requirements?
Antwort: C
Begründung:
Amazon Kinesis Data Streams is designed for high-throughput, near real-time ingestion from many producers and allows multiple downstream consumers to independently read and replay records within the retention period. Enhanced fan-out gives each consumer dedicated read throughput, which supports parallel downstream processing without consumers competing for shared read capacity.
520. Frage
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