MicrosoftのDP-201全真模擬試験認定試験はNewValidDumpsの最優秀な専門家チームが自分の知識と業界の経験を利用してどんどん研究した、満足Microsoft認証受験生の需要に満たすの書籍がほかのサイトにも見えますが、NewValidDumpsの商品が最も保障があって、君の最良の選択になります。 一目でわかる最新の出題傾向でわかりやすい解説と充実の補充問題があります。NewValidDumpsの専門家チームが君の需要を満たすために自分の経験と知識を利用してMicrosoftのDP-201全真模擬試験認定試験対策模擬テスト問題集が研究しました。 弊社は君の試験の１００％合格率を保証いたします。

Azure Data Engineer Associate DP-201 もし失敗したら、全額で返金を保証いたします。

MicrosoftのDP-201 - Designing an Azure Data Solution全真模擬試験認定試験に受かるのはあなたの技能を検証することだけでなく、あなたの専門知識を証明できて、上司は無駄にあなたを雇うことはしないことの証明書です。 弊社の資源はずっと改訂され、アップデートされていますから、緊密な相関関係があります。MicrosoftのDP-201 日本語版試験勉強法の認証試験を準備しているあなたは、自分がトレーニングを選んで、しかも次の問題を受かったほうがいいです。

あなたはキャリアで良い昇進のチャンスを持ちたいのなら、NewValidDumpsのMicrosoftのDP-201全真模擬試験「Designing an Azure Data Solution」試験トレーニング資料を利用してMicrosoftの認証の証明書を取ることは良い方法です。現在、MicrosoftのDP-201全真模擬試験認定試験に受かりたいIT専門人員がたくさんいます。NewValidDumpsの試験トレーニング資料はMicrosoftのDP-201全真模擬試験認定試験の１００パーセントの合格率を保証します。

Microsoft DP-201全真模擬試験 - 信じないでしょうか。

時間とお金の集まりより正しい方法がもっと大切です。MicrosoftのDP-201全真模擬試験試験のために勉強していますなら、NewValidDumpsの提供するMicrosoftのDP-201全真模擬試験試験ソフトはあなたの選びの最高です。我々の目的はあなたにMicrosoftのDP-201全真模擬試験試験に合格することだけです。試験に失敗したら、弊社は全額で返金します。我々の誠意を信じてください。あなたが順調に試験に合格するように。

しかし、これは本当のことですよ。DP-201全真模擬試験認定試験の準備を効率的にするために、どんなツールが利用に値するものかわかっていますか。

DP-201 PDF DEMO:

QUESTION NO: 1
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

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
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: 4
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: 5
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

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Updated: May 28, 2022