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通過很多已經使用Royalholidayclubbed的些針對IT認證考試培訓資料的考生的回饋，證明了使用我們的Royalholidayclubbed的產品通過It認證考試是很容易的。Royalholidayclubbed最近研究出來了關於熱門的Amazon SAP-C01題庫最新資訊認證考試的培訓方案，包括一些針對性的測試題，可以幫助你鞏固知識，讓你為Amazon SAP-C01題庫最新資訊認證考試做好充分的準備。 Amazon的SAP-C01題庫最新資訊考試認證，Royalholidayclubbed是當前最新Amazon的SAP-C01題庫最新資訊考試認證和考題準備問題提供認證的候選人中的佼佼者，我們資源不斷被修訂和更新，具有緊密的相關性和緊密性，今天你準備Amazon的SAP-C01題庫最新資訊認證，你將要選擇你要開始的訓練，而且要通過你下一次的考題，由於我們大部分考題是每月更新一次，你將得到最好的資源與市場的新鮮品質和可靠性的保證。 Royalholidayclubbed題供了不同培訓工具和資源來準備Amazon的SAP-C01題庫最新資訊考試，編制指南包括課程，實踐的檢驗，測試引擎和部分免費PDF下載，我們的考題及答案反應的問題問Amazon的SAP-C01題庫最新資訊考試。

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Amazon SAP-C01題庫最新資訊 - 如果你想輕鬆通過考試，那麼快來試試吧。

我們都很清楚 Amazon SAP-C01題庫最新資訊認證考試在IT行業中的地位是駐足輕重的地位，但關鍵的問題是能夠拿到Amazon SAP-C01題庫最新資訊的認證證書不是那麼簡單的。我們很清楚地知道網上缺乏有高品質的準確性高的相關考試資料。Royalholidayclubbed的考試練習題和答案可以為一切參加IT行業相關認證考試的人提供一切所急需的資料。它能時時刻刻地提供你們想要的資料，購買我們所有的資料能保證你通過你的第一次Amazon SAP-C01題庫最新資訊認證考試。

這絕對是一個讓你禁不住讚美的考古題。你不可能找到比它更好的考試相關的資料了。

SAP-C01 PDF DEMO:

QUESTION NO: 1
A company wants to migrate its website from an on-premises data center onto AWS. At the same time, it wants to migrate the website to a containerized microservice-based architecture to improve the availability and cost efficiency. The company's security policy states that privileges and network permissions must be configured according to best practice, using least privilege.
A Solutions Architect must create a containerized architecture that meets the security requirements and has deployed the application to an Amazon ECS cluster.
What steps are required after the deployment to meet the requirements? (Choose two.)
A. Apply security groups to the tasks, and use IAM roles for tasks to access other resources.
B. Apply security groups to Amazon EC2 instances, and use IAM roles for EC2 instances to access other resources.
C. Create tasks using the awsvpc network mode.
D. Create tasks using the bridge network mode.
E. Apply security groups to the tasks, and pass IAM credentials into the container at launch time to access other resources.
Answer: A,C
Explanation
https://aws.amazon.com/about-aws/whats-new/2017/11/amazon-ecs-introduces-awsvpc- networking-mode-for-co
https://amazonaws-china.com/blogs/compute/introducing-cloud-native-networking-for-ecs- containers/
https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-iam-roles.html

QUESTION NO: 2
A company that provides wireless services needs a solution to store and analyze log files about user activities.
Currently, log files are delivered daily to Amazon Linux on Amazon EC2 instance. A batch script is run once a day to aggregate data used for analysis by a third-party tool. The data pushed to the third- party tool is used to generate a visualization for end users. The batch script is cumbersome to maintain, and it takes several hours to deliver the ever-increasing data volumes to the third-party tool. The company wants to lower costs, and is open to considering a new tool that minimizes development effort and lowers administrative overhead. The company wants to build a more agile solution that can store and perform the analysis in near-real time, with minimal overhead. The solution needs to be cost effective and scalable to meet the company's end-user base growth.
Which solution meets the company's requirements?
A. Use an Amazon Kinesis agent running on an EC2 instance to collect and send the data to an
Amazon Kinesis Data Firehose delivery stream. The Kinesis Data Firehose delivery stream will deliver the data to Amazon S3. Use an AWS Lambda function to deliver the data from Amazon S3 to Amazon
ES. Use Kibana to visualize the data.
B. Use an Amazon Kinesis agent running on an EC2 instance in an Auto Scaling group to collect and send the data to an Amazon Kinesis Data Forehose delivery stream. The Kinesis Data Firehose delivery stream will deliver the data directly to Amazon ES. Use Kibana to visualize the data.
C. Use an in-memory caching application running on an Amazon EBS-optimized EC2 instance to capture the log data in near real-time. Install an Amazon ES cluster on the same EC2 instance to store the log files as they are delivered to Amazon EC2 in near real-time. Install a Kibana plugin to create the visualizations.
D. Develop a Python script to failure the data from Amazon EC2 in real time and store the data in
Amazon S3. Use a copy command to copy data from Amazon S3 to Amazon Redshift. Connect a business intelligence tool running on Amazon EC2 to Amazon Redshift and create the visualizations.
Answer: B
Explanation
https://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html

QUESTION NO: 3
A company has an application that runs a web service on Amazon EC2 instances and stores .jpg images in Amazon S3. The web traffic has a predictable baseline, but often demand spikes unpredictably for short periods of time. The application is loosely coupled and stateless. The .jpg images stored in Amazon S3 are accessed frequently for the first 15 to 20 days, they are seldom accessed thereafter but always need to be immediately available. The CIO has asked to find ways to reduce costs.
Which of the following options will reduce costs? (Choose two.)
A. Create a script that checks the load on all web servers and terminates unnecessary On-Demand instances.
B. Configure a lifecycle policy to move the .jpg images on Amazon S3 to S3 IA after 30 days.
C. Configure a lifecycle policy to move the .jpg images on Amazon S3 to Amazon Glacier after 30 days.
D. Purchase Reserved instances for baseline capacity requirements and use On-Demand instances for the demand spikes.
E. Use On-Demand instances for baseline capacity requirements and use Spot Fleet instances for the demand spikes.
Answer: B,D

QUESTION NO: 4
A company collects a steady stream of 10 million data records from 100,000 sources each day. These records are written to an Amazon RDS MySQL DB. A query must produce the daily average of a data source over the past 30 days. There are twice as many reads as writes. Queries to the collected data are for one source ID at a time.
How can the Solutions Architect improve the reliability and cost effectiveness of this solution?
A. Use Amazon DynamoDB with the source ID as the partition key and the timestamp as the sort key.
Use a Time to Live (TTL) to delete data after 30 days.
B. Use Amazon Aurora with MySQL in a Multi-AZ mode. Use four additional read replicas.
C. Ingest data into Amazon Kinesis using a retention period of 30 days. Use AWS Lambda to write data records to Amazon ElastiCache for read access.
D. Use Amazon DynamoDB with the source ID as the partition key. Use a different table each day.
Answer: A
Explanation
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html

QUESTION NO: 5
A company has a large on-premises Apache Hadoop cluster with a 20 PB HDFS database. The cluster is growing every quarter by roughly 200 instances and 1 PB. The company's goals are to enable resiliency for its Hadoop data, limit the impact of losing cluster nodes, and significantly reduce costs. The current cluster runs 24/7 and supports a variety of analysis workloads, including interactive queries and batch processing.
Which solution would meet these requirements with the LEAST expense and down time?
A. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on
Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
B. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster of similar size and configuration to the current cluster. Store the data on EMRFS.
Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional
Reserved Instances and add to the cluster.
C. Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on
Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
D. Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon
EMR cluster initially sized to handle the interactive workloads based on historical data from the on- premises cluster. Store the on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Answer: A
Explanation
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.

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Updated: May 28, 2022

by RHC.

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