自分のIT業界での発展を希望したら、MicrosoftのDP-201模擬資料試験に合格する必要があります。MicrosoftのDP-201模擬資料試験はいくつ難しくても文句を言わないで、我々NewValidDumpsの提供する資料を通して、あなたはMicrosoftのDP-201模擬資料試験に合格することができます。MicrosoftのDP-201模擬資料試験を準備しているあなたに試験に合格させるために、我々NewValidDumpsは模擬試験ソフトを更新し続けています。 もしそうだったら、もう試験に合格できないなどのことを心配する必要がないのです。ずっと自分自身を向上させたいあなたは、DP-201模擬資料認定試験を受験する予定があるのですか。 あなたは自分の望ましいMicrosoft DP-201模擬資料問題集を選らんで、学びから更なる成長を求められます。

Azure Data Engineer Associate DP-201 弊社の商品が好きなのは弊社のたのしいです。

Azure Data Engineer Associate DP-201模擬資料 - Designing an Azure Data Solution 我々はあなたのIT業界での発展にヘルプを提供できると希望します。 NewValidDumps を選択して１００％の合格率を確保することができて、もし試験に失敗したら、NewValidDumpsが全額で返金いたします。

心はもはや空しくなく、生活を美しくなります。世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、DP-201模擬資料問題集を学ぶ必要があります。

Microsoft DP-201模擬資料試験参考書の内容は全面的で、わかりやすいです。

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今の競争の激しいのIT業界の中にMicrosoft DP-201模擬資料認定試験に合格して、自分の社会地位を高めることができます。弊社のIT業で経験豊富な専門家たちが正確で、合理的なMicrosoft DP-201模擬資料「Designing an Azure Data Solution」認証問題集を作り上げました。

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