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NEW QUESTION: 1
VPNを使用して、企業ネットワークをVPCに拡張します。 VPCのインスタンスは、Amazon Route 53プライベートホストゾーンのリソースレコードを解決できます。オンプレミスDNSサーバーは、VPC DNSサーバーのIPアドレスへのフォワーダーで構成されています。ピアVPCのインスタンスでは可能ですが、オンプレミスユーザーはプライベートホストゾーンで名前を解決することはできません。
オンプレミスユーザーにプライベートホストゾーンへのアクセスを提供するにはどうすればいいですか？
A. プライベートホステッドゾーンに割り当てられている4台のネームサーバーでオンプレミスフォワーダーを更新します。
B. VPC内にプロキシリゾルバを作成します。オンプレミスフォワーダにプロキシリゾルバを指示します。
C. オンプレミスサーバーをプライベートゾーンのセカンダリDNSとして構成します。 NSレコードを更新してください。
D. オンプレミスシステムからのDNSクエリを許可するようにVPC上のネットワークアクセス制御リストを変更します。
Answer: B

NEW QUESTION: 2
회사의 웹 애플리케이션은 여러 Linux Amazon EC2 인스턴스를 사용하고 Amazon EBS 볼륨에 데이터를 저장하고 있습니다. 이 회사는 장애 발생시 애플리케이션의 탄력성을 높이고 원 자성, 일관성, 격리 및 내구성 (ACID)을 준수하는 스토리지를 제공하는 솔루션을 찾고 있습니다.
솔루션 아키텍트는 이러한 요구 사항을 충족시키기 위해 무엇을 해야 합니까?
A. 여러 가용 영역에서 Auto Scaling 그룹으로 Application Load Balancer를 생성하십시오.
Amazon EFS에 데이터를 저장하고 각 인스턴스에 대상을 마운트 하십시오.
B. 각 가용 영역의 EC2 인스턴스에서 애플리케이션을 시작하십시오. 각 EC2 인스턴스에 EBS 볼륨을 연결하십시오.
C. Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA)를 사용하여 여러 가용 영역에서 Auto Scaling 그룹으로 Application Load Balancer 생성 데이터 저장
D. 여러 가용 영역에서 Auto Scaling 그룹으로 Application Load Balancer 생성 각 EC2 인스턴스에 인스턴스 스토어 탑재
Answer: A
Explanation:
Explanation
How Amazon EFS Works with Amazon EC2
The following illustration shows an example VPC accessing an Amazon EFS file system. Here, EC2 instances in the VPC have file systems mounted.
In this illustration, the VPC has three Availability Zones, and each has one mount target created in it. We recommend that you access the file system from a mount target within the same Availability Zone. One of the Availability Zones has two subnets. However, a mount target is created in only one of the subnets.

Benefits of Auto Scaling
Better fault tolerance. Amazon EC2 Auto Scaling can detect when an instance is unhealthy, terminate it, and launch an instance to replace it. You can also configure Amazon EC2 Auto Scaling to use multiple Availability Zones. If one Availability Zone becomes unavailable, Amazon EC2 Auto Scaling can launch instances in another one to compensate.
Better availability. Amazon EC2 Auto Scaling helps ensure that your application always has the right amount of capacity to handle the current traffic demand.
Better cost management. Amazon EC2 Auto Scaling can dynamically increase and decrease capacity as needed. Because you pay for the EC2 instances you use, you save money by launching instances when they are needed and terminating them when they aren't.
https://docs.aws.amazon.com/efs/latest/ug/how-it-works.html#how-it-works-ec2
https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html

NEW QUESTION: 3
Which five items are provided by the Java concurrency utilities?
A. Asynchronous execution of tasks
B. Atomic variables
C. Concurrent collection sorting implementations
D. Counting semaphores
E. Collection classes designed for concurrent access
F. High-performance, flexible thread pools
G. synchronized wrappers for collection classes in the java.util package,
H. Dynamic adjustment of thread priorities
Answer: B,D,E,F,G
Explanation:
The Java 2 platform includes a new package of concurrency utilities. These are classes that are designed to be used as building blocks in building concurrent classes or applications. Just as thecollections framework simplified the organization and manipulation of in-memory data by providing implementations of commonly used data structures, the concurrency utilities simplify the development of concurrent classes by providing implementations of building blocks commonly used in concurrent designs. The concurrency utilities include a high-performance, flexible thread pool; a framework for asynchronous execution of tasks; a host of collection classes optimized for concurrent access; synchronization utilities such as counting semaphores (G); atomic variables; locks; and condition variables.
The concurrency utilities includes:
*Task scheduling framework. The Executor interface standardizes invocation, scheduling, execution, and control of asynchronous tasks according to a set of execution policies. Implementations are provided that enable tasks to be executed within the submitting thread, in a single background thread (as with events in Swing), in a newly created thread, or in a thread pool, and developers can create customized implementations of Executor that support arbitrary execution policies. The built-in implementations offer configurable policies such as queue length limits and saturation policy that can improve the stability of applications by preventing runaway resource use.
*Fork/join framework. Based on the ForkJoinPool class, this framework is an implementation of Executor. It is designed to efficiently run a large number of tasks using a pool of worker threads
(A) . A work-stealing technique is used to keep all the worker threads busy, to take full advantage of multiple processors.
*(C) Concurrent collections. Several new collections classes were added, including the new Queue, BlockingQueue and BlockingDeque interfaces, and high-performance, concurrent implementations of Map, List, and Queue. See the Collections Framework Guide for more information.
*(D) Atomic variables. Utility classes are provided that atomically manipulate single variables (primitive types or references), providing high-performance atomic arithmetic and compare-and-set methods. The atomic variable implementations in the java.util.concurrent.atomic package offer higher performance than would be available by using synchronization (on most platforms), making them useful for implementing high-performance concurrent algorithms and conveniently
implementing counters and sequence number generators.
*(E) Synchronizers. General purpose synchronization classes, including semaphores, barriers, latches, phasers, and exchangers, facilitate coordination between threads.
*Locks. While locking is built into the Java language through the synchronized keyword, there are a number of limitations to built-in monitor locks. The java.util.concurrent.locks package provides a high-performance lock implementation with the same memory semantics as synchronization, and it also supports specifying a timeout when attempting to acquire a lock, multiple condition variables per lock, nonnested ("hand-over-hand") holding of multiple locks, and support for interrupting threads that are waiting to acquire a lock.
*Nanosecond-granularity timing. The System.nanoTime method enables access to a nanosecond-granularity time source for making relative time measurements and methods that accept timeouts (such as the BlockingQueue.offer, BlockingQueue.poll, Lock.tryLock, Condition.await, and Thread.sleep) can take timeout values in nanoseconds. The actual precision of the System.nanoTime method is platform-dependent.
Reference: Java SE Documentation, Concurrency Utilities