仕事に忙しいですから、試験の準備をする時間が足りないでしょう。ですから、効率が良い試験DP-201模擬解説集参考書が必要です。もちろん、よりよく試験の準備をするように、自分に相応しいツールを選択するのは一番大事なことです。 君はオンラインで無料な練習問題をダウンロードできて、１００％で試験に合格しましょう。NewValidDumpsはIT試験問題集を提供するウエブダイトで、ここによく分かります。 それに、うちの学習教材を購入したら、私たちは一年間で無料更新サービスを提供することができます。

Azure Data Engineer Associate DP-201 」とゴーリキーは述べました。

弊社のDP-201 - Designing an Azure Data Solution模擬解説集試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後DP-201 - Designing an Azure Data Solution模擬解説集試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。 さて、はやく試験を申し込みましょう。NewValidDumpsはあなたを助けることができますから、心配する必要がないですよ。

Microsoft　DP-201模擬解説集試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにMicrosoft　DP-201模擬解説集試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。もしあなたはDP-201模擬解説集試験に合格しなかったら、全額返金のことを承諾します。

Microsoft DP-201模擬解説集 - もちろんありますよ。

Microsoft　DP-201模擬解説集認定資格試験が難しいので、弊社のDP-201模擬解説集問題集はあなたに適当する認定資格試験問題集を見つけるし、本当の試験問題の難しさを克服することができます。弊社はMicrosoft　DP-201模擬解説集認定試験の最新要求に従って関心を持って、全面的かつ高品質な模擬試験問題集を提供します。また、購入する前に、無料でDP-201模擬解説集のPDF版デモをダウンロードでき、信頼性を確認することができます。

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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
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: 3
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: 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
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an HDInsight/Hadoop cluster solution that uses Azure Data Lake Gen1 Storage.
The solution requires POSIX permissions and enables diagnostics logging for auditing.
You need to recommend solutions that optimize storage.
Proposed Solution: Implement compaction jobs to combine small files into larger files.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation
Depending on what services and workloads are using the data, a good size to consider for files is 256
MB or greater. If the file sizes cannot be batched when landing in Data Lake Storage Gen1, you can have a separate compaction job that combines these files into larger ones.
Note: POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that becomes apparent when working with numerous small files. As a best practice, you must batch your data into larger files versus writing thousands or millions of small files to Data Lake Storage Gen1.
Avoiding small file sizes can have multiple benefits, such as:
Lowering the authentication checks across multiple files
Reduced open file connections
Faster copying/replication
Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

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Updated: May 28, 2022