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NEW QUESTION: 1
You have been tasked with deploying a scalable distributed system using AWS OpsWorks. Your distributed
system is required to scale on demand. As it is distributed, each node must hold a configuration file that
includes the hostnames of the other instances within the layer. How should you configure AWS OpsWorks to
manage scaling this application dynamically?
A. Create a Chef Recipe to update this configuration file, configure your AWS OpsWorks stack to use
custom cookbooks, and assign this recipe to the Configure LifeCycle Event of the specific layer.
B. Update this configuration file by writing a script to poll the AWS OpsWorks service API for new
instances. Configure your base AMI to execute this script on Operating System startup.
C. Configure your AWS OpsWorks layer to use the AWS-provided recipe for distributed host
configuration, and configure the instance hostname and file path parameters in your recipes settings.
D. Create a Chef Recipe to update this configuration file, configure your AWS OpsWorks stack to use
custom cookbooks, and assign this recipe to execute when instances are launched.
Answer: A
Explanation:
Explanation
Please check the following AWS DOCs which provides details on the scenario. Check the example of
"configure".
https://docs.aws.amazon.com/opsworks/latest/userguide/workingcookbook-events.html
You can use the Configure Lifecycle event
This event occurs on all of the stack's instances when one of the following occurs:
* An instance enters or leaves the online state.
* You associate an Elastic IP address with an instance or disassociate one from an instance.
* You attach an Elastic Load Balancing load balancer to a layer, or detach one from a layer. Ensure the
Opswork layer uses a custom Cookbook
For more information on Opswork stacks, please refer to the below document link: from AWS
* http://docs.aws.a
mazon.com/opsworks/latest/userguide/welcome_classic.html
NEW QUESTION: 2
計画担当者は、どのレベルで保全指図の原価を見積ることができますか？
正しい答えを選んでください。
応答：
A. 値カテゴリレベル
B. コンポーネントレベル
C. コストセンターレベル
D. 作業区レベル
Answer: A
NEW QUESTION: 3
A. Option B
B. Option A
C. Option C
D. Option D
Answer: C
NEW QUESTION: 4
Which Azure data storage solution should you recommend for each application? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Health Review: Azure SQL Database
Scenario: ADatum identifies the following requirements for the Health Review application:
* Ensure that sensitive health data is encrypted at rest and in transit.
* Tag all the sensitive health data in Health Review. The data will be used for auditing.
Health Interface: Azure Cosmos DB
ADatum identifies the following requirements for the Health Interface application:
* Upgrade to a data storage solution that will provide flexible schemas and increased throughput for writing data. Data must be regionally located close to each hospital, and reads must display be the most recent committed version of an item.
* Reduce the amount of time it takes to add data from new hospitals to Health Interface.
* Support a more scalable batch processing solution in Azure.
* Reduce the amount of development effort to rewrite existing SQL queries.
Health Insights: Azure SQL Data Warehouse
Azure SQL Data Warehouse is a cloud-based enterprise data warehouse that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution.
You can access Azure SQL Data Warehouse (SQL DW) from Databricks using the SQL Data Warehouse connector (referred to as the SQL DW connector), a data source implementation for Apache Spark that uses Azure Blob Storage, and PolyBase in SQL DW to transfer large volumes of data efficiently between a Databricks cluster and a SQL DW instance.
Scenario: ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables References:
https://docs.databricks.com/data/data-sources/azure/sql-data-warehouse.html