我々は販売者とお客様の間の信頼が重要でもらい難いのを知っています。我々はMicrosoftのDP-201難易度ソフトであなたに専門と高効率を示して、最全面的な問題集と詳しい分析であなたに助けてMicrosoftのDP-201難易度試験に合格して、最高のサービスであなたの信頼を得ています。あなたが試験に合格するのは我々への一番よい評価です。 試験が更新されているうちに、我々はMicrosoftのDP-201難易度試験の資料を更新し続けています。できるだけ100％の通過率を保証使用にしています。 MicrosoftのDP-201難易度試験のための資料がたくさんありますが、NewValidDumpsの提供するのは一番信頼できます。

Azure Data Engineer Associate DP-201 自分の幸せは自分で作るものだと思われます。

あなたに安心にMicrosoftのDP-201 - Designing an Azure Data Solution難易度ソフトを購入させるために、我々は最も安全的な支払手段を提供します。 あなたは弊社の高品質Microsoft　DP-201 実際試験試験資料を利用して、一回に試験に合格します。NewValidDumpsのMicrosoft　DP-201 実際試験問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。

なぜならば、IT職員にとって、MicrosoftのDP-201難易度資格証明書があるのは肝心な指標であると言えます。自分の能力を証明するために、DP-201難易度試験に合格するのは不可欠なことです。弊社のDP-201難易度真題を入手して、試験に合格する可能性が大きくなります。

Microsoft DP-201難易度 - 暇の時間を利用して勉強します。

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多分、DP-201難易度テスト質問の数が伝統的な問題の数倍である。Microsoft　DP-201難易度試験参考書は全ての知識を含めて、全面的です。

DP-201 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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
You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Databricks and
PolyBase in Azure SQL Data Warehouse.
You need to recommend a Stream Analytics data output format to ensure that the queries from
Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the files can be queried quickly and that the data type information is retained.
What should you recommend?
A. Parquet
B. JSON
C. CSV
D. Avro
Answer: D
Explanation
The Avro format is great for data and message preservation.
Avro schema with its support for evolution is essential for making the data robust for streaming architectures like Kafka, and with the metadata that schema provides, you can reason on the data.
Having a schema provides robustness in providing meta-data about the data stored in Avro records which are self-documenting the data.
References:
http://cloudurable.com/blog/avro/index.html

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Updated: May 28, 2022