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Azure Data Engineer Associate DP-201 ここで皆様に良い方法を教えてあげますよ。

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DP-201 PDF DEMO:

QUESTION NO: 1
You have a Windows-based solution that analyzes scientific data. You are designing a cloud- based solution that performs real-time analysis of the data.
You need to design the logical flow for the solution.
Which two actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Send data from the application to Azure Data Lake Storage.
B. Use an Azure Stream Analytics job in the cloud. Ingress data from an Azure Event Hub instance and build queries that output to Azure Data Lake Storage.
C. Use an Azure Stream Analytics job on an edge device. Ingress data from an Azure Data Factory instance and build queries that output to Power BI.
D. Send data from the application to an Azure Stream Analytics job.
E. Send data from the application to an Azure Event Hub instance.
F. Use an Azure Stream Analytics job in the cloud. Ingress data from the Azure Event Hub instance and build queries that output to Power BI.
Answer: E,F
Explanation
Stream Analytics has first-class integration with Azure data streams as inputs from three kinds of resources:
Azure Event Hubs
Azure IoT Hub
Azure Blob storage
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-inputs

QUESTION NO: 2
Which consistency level should you use for Health Interface?
A. Strong
B. Bounded Staleness
C. Session
D. Consistent Prefix
Answer: A
Explanation
Scenario: ADatum identifies the following requirements for the Health Interface application:
reads must display be the most recent committed version of an item.
Azure Cosmos DB consistency levels include:
Strong: Strong consistency offers a linearizability guarantee. Linearizability refers to serving requests concurrently. The reads are guaranteed to return the most recent committed version of an item. A client never sees an uncommitted or partial write. Users are always guaranteed to read the latest committed write.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

QUESTION NO: 3
You are planning a design pattern based on the Lambda architecture as shown in the exhibit.
Which Azure services should you use f2 or the cold path? To answer, drag the appropriate services to the correct layers. Each service may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
Layer 2: Azure Data Lake Storage Gen2
Layer 3: Azure SQL Data Warehouse
Azure SQL Data Warehouse can be used for batch processing.
Note: Lambda architectures use batch-processing, stream-processing, and a serving layer to minimize the latency involved in querying big data.
References:
https://azure.microsoft.com/en-us/blog/lambda-architecture-using-azure-cosmosdb-faster- performance-low-tco-l
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch- processing

QUESTION NO: 4
You are designing a solution that will use Azure Table storage. The solution will log records in the following entity.
You are evaluating which partition key to use based on the following two scenarios:
* Scenario1: Minimize hotspots under heavy write workloads.
* Scenario2: Ensure that date lookups are as efficient as possible for read workloads.
Which partition key should you use for each scenario? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
References:
https://docs.microsoft.com/en-us/rest/api/storageservices/designing-a-scalable-partitioning- strategy-for-azure-tab

QUESTION NO: 5
You need to design the disaster recovery solution for customer sales data analytics.
Which three actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Use Geo-redundant storage.
B. Use zone redundant storage.
C. Provision multiple Azure Databricks workspaces in separate Azure regions.
D. Migrate users, notebooks, and cluster configurations from one workspace to another in the same region.
E. Provision a second Azure Databricks workspace in the same region.
F. Migrate users, notebooks, and cluster configurations from one region to another.
Answer: A,C,F
Explanation
Scenario: The analytics solution for customer sales data must be available during a regional outage.
To create your own regional disaster recovery topology for databricks, follow these requirements:
1. Provision multiple Azure Databricks workspaces in separate Azure regions
2. Use Geo-redundant storage.
3. Once the secondary region is created, you must migrate the users, user folders, notebooks, cluster configuration, jobs configuration, libraries, storage, init scripts, and reconfigure access control.
Note: Geo-redundant storage (GRS) is designed to provide at least 99.99999999999999% (16 9's) durability of objects over a given year by replicating your data to a secondary region that is hundreds of miles away from the primary region. If your storage account has GRS enabled, then your data is durable even in the case of a complete regional outage or a disaster in which the primary region isn't recoverable.
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy-grs

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Updated: May 28, 2022