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NEW QUESTION: 1
Why should only privileged users be permitted to define alias queues?
A. A QALIAS can name another alias queue as its TARGET, which can potentially create aninfinite loop.
B. Users can bypass access control by creating a QALIAS.
C. There is no check if the TARGET is defined or not.
D. An alias queue could have a remote queue for its TARGET.
Answer: B
NEW QUESTION: 2
You have deployed a proof-of-concept application by manually placing instances in a single Compute Engine zone. You are now moving the application to production, so you need to increase your application availability and ensure it can autoscale.
How should you provision your instances?
A. Create a managed instance group for each region, select Single zone for the location, and manually distribute instances across the zones in that region.
B. Create an unmanaged instance group for each zone, and manually distribute the instances across the desired zones.
C. Create an unmanaged instance group in a single zone, and then create an HTTP load balancer for the instance group.
D. Create a single managed instance group, specify the desired region, and select Multiple zones for the location.
Answer: A
Explanation:
https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed- instance-groups
NEW QUESTION: 3
You use Microsoft Azure Stream Analytics to analyze website visit logs in real time.
You have a table named Locations that has a column containing IP addresses and a column containing country names.
You need to look up the country name of a specific IP address.
Which query element should you use?
A. a UNION operator
B. an APPLY operator
C. an input data JOIN
D. a reference data JOIN
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Reference data (also known as a lookup table) is a finite data set that is static or slowing changing in nature, used to perform a lookup or to correlate with your data stream. To make use of reference data in your Azure Stream Analytics job, you will generally use a Reference Data Join in your Query.
In a usual scenario, we use an event processing engine to compute streaming data with very low latency.
In many cases users need to correlate persisted historical data or a slow changing dataset (aka. reference data) with the real-time event stream to make smarter decisions about the system. For example, join my event stream to a static dataset which maps IP Addresses to locations. This is the only JOIN supported in Stream Analytics where a temporal bound is not necessary.
References: https://msdn.microsoft.com/en-us/azure/stream-analytics/reference/reference-data-join-azure- stream-analytics
NEW QUESTION: 4
제조 회사는 고객 사이트에서 실행되는 컴퓨터에서 데이터를 캡처합니다. 현재 수천 대의 컴퓨터가 5 분마다 데이터를 전송하므로 가까운 시일 내에 수십만 대의 컴퓨터로 확장 될 것으로 예상됩니다. 필요한 경우 나중에 분석 할 의도로 데이터가 기록됩니다.
이 스트리밍 데이터를 대규모로 저장하는 SIMPLEST 방법은 무엇입니까?
A. Amazon SQS 대기열을 생성하고 머신이 대기열에 쓰도록 합니다.
B. ELB 뒤에 Amazon EC2 서버의 Auto Scaling 그룹을 만들어 데이터를 Amazon RDS에 씁니다.
C. Amazon Kinesis Firehouse 전송 스트림을 생성하여 Amazon S3에 데이터를 저장합니다.
D. ELB 뒤에 Amazon EC2 서버 팜을 생성하여 Amazon EBS Cold FIDD 볼륨에 데이터를 저장합니다.
Answer: C
Explanation:
Explanation
What Is Amazon Kinesis Data Firehose?
Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. Kinesis Data Firehose is part of the Kinesis streaming data platform, along with Kinesis Data Streams, Kinesis Video Streams, and Amazon Kinesis Data Analytics. With Kinesis Data Firehose, you don't need to write applications or manage resources. You configure your data producers to send data to Kinesis Data Firehose, and it automatically delivers the data to the destination that you specified. You can also configure Kinesis Data Firehose to transform your data before delivering it