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NEW QUESTION: 1
A solutions architect is designing the cloud architecture for a new application being deployed on AWS The process should run in parallel while adding and removing application nodes as needed based on the number of jobs to be processed The processor application is stateless The solutions architect must ensure that the application is loosely coupled and the job items are durably stored Which design should the solutions architect use?
A. Create an Amazon SQS queue to hold the jobs that needs to be processed Create an Amazon Machine Image (AMI) that consists of the processor application Create a launch template that uses the AMI Create an Auto Scaling group using the launch template Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of items in the SQS queue
B. Create an Amazon SNS topic to send the jobs that need to be processed Create an Amazon Machine Image (AMI) that consists of the processor application Create a launch configuration that uses the AMI Create an Auto Scaling group using the launch configuration Set the scaling policy for the Auto Scaling group to add and remove nodes based on CPU usage
C. Create an Amazon SQS queue to hold the jobs that need to be processed Create an Amazon Machine Image (AMI) that consists of the processor application Create a launch configuration that uses the AMI Create an Auto Scaling group using the launch configuration Set the scaling policy for the Auto Scaling group to add and remove nodes based on network usage
D. Create an Amazon SNS topic to send the jobs that need to be processed Create an Amazon Machine Image (AMI) that consists of the processor application Create a launch template that uses the AMI Create an Auto Scaling group using the launch template Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of messages published to the SNS topic.
Answer: A
Explanation:
Explanation
Amazon Simple Queue Service
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands.
SQS offers two types of message queues. Standard queues offer maximum throughput, best-effort ordering, and at-least-once delivery. SQS FIFO queues are designed to guarantee that messages are processed exactly once, in the exact order that they are sent.
Scaling Based on Amazon SQS
There are some scenarios where you might think about scaling in response to activity in an Amazon SQS queue. For example, suppose that you have a web app that lets users upload images and use them online. In this scenario, each image requires resizing and encoding before it can be published. The app runs on EC2 instances in an Auto Scaling group, and it's configured to handle your typical upload rates. Unhealthy instances are terminated and replaced to maintain current instance levels at all times. The app places the raw bitmap data of the images in an SQS queue for processing. It processes the images and then publishes the processed images where they can be viewed by users. The architecture for this scenario works well if the number of image uploads doesn't vary over time. But if the number of uploads changes over time, you might consider using dynamic scaling to scale the capacity of your Auto Scaling group.
https://aws.amazon.com/sqs/#:~:text=Amazon%20SQS%20leverages%20the%20AWS,queues%20provide%20n
https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html
NEW QUESTION: 2
短い文形式で書かれた12,000件の顧客レビューを含むCSVファイルを使用して、感情分析を実行しています。 CSVファイルをAzure Machine Learning Studioに追加し、実験の開始点データセットとして構成します。テキストモジュールからのN-Gram特徴の抽出を実験に追加して、データセットの顧客レビュー列からキーフレーズを抽出します。
カスタマーレビューテキストから新しいn-gram辞書を作成し、最大n-gramサイズをtrigramsに設定する必要があります。
何を選ぶべきですか？回答するには、回答エリアで適切なオプションを選択します。
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。
Answer:
Explanation:
Explanation:
Vocabulary mode: Create
For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.
N-Grams size: 3
For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.
Weighting function: Leave blank
The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text
NEW QUESTION: 3
FacebookのAtlasは、世界中のマーケティング担当者に何をもたらしましたか？
A. Cookieベースの測定
B. 結果ベースの測定
C. コストベースの測定
D. 人に基づく測定
Answer: D
Explanation:
Explanation:FacebookのAtlasは、人ベースの測定を可能にしました。これは基本的にFacebookのUSPであり、マーケティング担当者に強調したいものです。コアコンピテンシー試験で出てくる可能性が高いため、この概念を完全に理解することが重要です。