ところで、受験生の皆さんを簡単にIT認定試験に合格させられる方法がないですか。もちろんありますよ。NewValidDumpsの問題集を利用することは正にその最良の方法です。 では、他の人を頼んで試験に合格する対策を教えてもらったのですか。試験に準備する方法が色々ありますが、最も高効率なのは、きっと良いツールを利用することですね。 NewValidDumpsのMicrosoftのDP-201テキスト試験トレーニング資料は豊富な経験を持っているIT専門家が研究したものです。

Azure Data Engineer Associate DP-201 試験に失敗したら、全額で返金する承諾があります。

NewValidDumpsのMicrosoftのDP-201 - Designing an Azure Data Solutionテキスト試験トレーニング資料は豊富な経験を持っている専門家が長年の研究を通じて開発されたものです。 我々の提供するPDF版のMicrosoftのDP-201 資格勉強試験の資料はあなたにいつでもどこでも読めさせます。我々もオンライン版とソフト版を提供します。

そうであれば、無料で弊社の提供するMicrosoftのDP-201テキストのデモをダウンロードしてみよう。我々提供する資料はあなたの需要だと知られています。あなたのMicrosoftのDP-201テキスト試験に参加する圧力を減ってあなたの効率を高めるのは我々の使命だと思います。

Microsoft DP-201テキスト - できるだけ100％の通過率を保証使用にしています。

数年以来の整理と分析によって開発されたDP-201テキスト問題集は権威的で全面的です。DP-201テキスト問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からDP-201テキスト問題集への好評をもらいました。DP-201テキスト問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するDP-201テキスト問題集を暗記すれば、きっと試験に合格できます。

MicrosoftのDP-201テキスト試験のソフトは問題数が豊富であなたに大量の練習で能力を高めさせます。そのほかに、専門家たちの解答への詳しい分析があります。

DP-201 PDF DEMO:

QUESTION NO: 1
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

QUESTION NO: 2
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DataKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.
Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure SQL Data Warehouse. You need to ensure that the
Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
Box 1: Hash-distributed
Box 2: ProductKey
ProductKey is used extensively in joins.
Hash-distributed tables improve query performance on large fact tables.
Box 3: Round-robin
Box 4: RegionKey
Round-robin tables are useful for improving loading speed.
Consider using the round-robin distribution for your table in the following scenarios:
* When getting started as a simple starting point since it is the default
* If there is no obvious joining key
* If there is not good candidate column for hash distributing the table
* If the table does not share a common join key with other tables
* If the join is less significant than other joins in the query
* When the table is a temporary staging table
Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

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 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: 5
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

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Updated: May 28, 2022