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assembly=System.Windows.Interactivity > A. Changing existing configurations for applications
B. Opening and closing network ports
C. Ensuring transmission protocols are functioning correctly
D. Monitoring network traffic and detecting anomalies
Answer: B

NEW QUESTION: 2
QoS is applied on an outgoing interface as shown below:
policy-map COS-OUT class DATA random-detect dscp-based random-detect exponential-weighting-constant 9 random-detect dscp 26 39 117 30 random-detect dscp 28 19 35 20
What will the QoS result be for egress traffic with an average queue depth of 40 that is marked as DSCP AF32?
A. All traffic will be tail dropped.
B. Traffic will be dropped with a rate of 1 packet out of 30.
C. Traffic will be dropped with a rate of 1 packet out of 20.
D. Traffic will be dropped with a random rate less than the one defined by MPD.
Answer: C

NEW QUESTION: 3
솔루션 아키텍트는 이미지 카탈로그에 액세스하고 사용자에게 이미지 사용자 지정 요청을 제출할 수 있는 기능을 제공하는 솔루션을 설계하고 있습니다.
이미지 사용자 지정 파라미터는 AWS API Gateway API로 전송되는 모든 요청에 있습니다.
사용자 지정 이미지는 요청시 생성되며 사용자는 클릭하여 사용자 지정 이미지를 보거나 다운로드 할 수 있는 링크를 받게 됩니다.
이 솔루션은 이미지 보기 및 사용자 지정을 위한 고 가용성이어야 합니다. 이러한 요구 사항을 충족하는 가장 비용 효율적인 솔루션은 무엇입니까?
A. Amazon EC2 인스턴스를 사용하여 원본 이미지를 요청 된 사용자 지정으로 조작합니다.
원본 및 조작 된 이미지를 Amazon S3에 저장합니다.
EC2 인스턴스 앞에 Elastic Load Balancer를 구성합니다.
B. AWS Lambda를 사용하여 원본 이미지를 요청 된 사용자 지정으로 조작합니다.
원본 이미지를 Amazon S3에 저장하고 조작 된 이미지를 Amazon DynamoDB에 저장합니다.
Amazon EC2 인스턴스 앞에 Elastic Load Balancer를 구성합니다.
C. Amazon EC2 인스턴스를 사용하여 원본 이미지를 요청 된 사용자 지정으로 조작합니다.
원본 이미지를 Amazon S3에 저장하고 조작 된 이미지를 Amazon DynamoDB에 저장합니다.
S3 버킷을 오리진으로 사용하여 Amazon CloudFront 배포를 구성합니다.
D. AWS Lambda를 사용하여 원본 이미지를 요청 된 사용자 지정으로 조작합니다.
원본 및 조작 된 이미지를 Amazon S3에 저장합니다.
S3 버킷을 기반으로 하는 Amazon CloudFront 배포를 구성합니다.
Answer: D
Explanation:
All solutions presented are highly available. The key requirement that must be satisfied is that the solution should be cost-effective and you must choose the most cost-effective option.
Therefore, it's best to eliminate services such as Amazon EC2 and ELB as these require ongoing costs even when they're not used. Instead, a fully serverless solution should be used. AWS Lambda, Amazon S3 and CloudFront are the best services to use for these requirements.
CORRECT: "Use AWS Lambda to manipulate the original images to the requested customization. Store the original and manipulated images in Amazon S3. Configure an Amazon CloudFront distribution with the S3 bucket as the origin" is the correct answer.
INCORRECT: "Use Amazon EC2 instances to manipulate the original images into the requested customization. Store the original and manipulated images in Amazon S3. Configure an Elastic Load Balancer in front of the EC2 instances" is incorrect. This is not the most cost-effective option as the ELB and EC2 instances will incur costs even when not used.
INCORRECT: "Use AWS Lambda to manipulate the original images to the requested customization. Store the original images in Amazon S3 and the manipulated images in Amazon DynamoDB. Configure an Elastic Load Balancer in front of the Amazon EC2 instances" is incorrect. This is not the most cost-effective option as the ELB will incur costs even when not used. Also, Amazon DynamoDB will incur RCU/WCUs when running and is not the best choice for storing images.
INCORRECT: "Use Amazon EC2 instances to manipulate the original images into the requested customization. Store the original images in Amazon S3 and the manipulated images in Amazon DynamoDB. Configure an Amazon CloudFront distribution with the S3 bucket as the origin" is incorrect. This is not the most cost-effective option as the EC2 instances will incur costs even when not used References:
https://aws.amazon.com/serverless/

NEW QUESTION: 4
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
注：それぞれの正しい選択には1ポイントの価値があります。
A. Add a temporal analytic function.
B. Increase the number of streaming units.
C. Scale out the query by using PARTITION BY.
D. Add a pass-through query.
E. Convert the query to a reference query.
Answer: B,C
Explanation:
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption