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DP-203試験の準備方法｜有難いDP-203無料試験試験｜素晴らしいData Engineering on Microsoft Azureテスト参考書

DP-203試験問題はすべて、99％〜100％の高い合格率を持ち、有効です。 DP-203学習ガイドの非周期性を修正します。購入したものが最新かつ高品質のDP-203準備資料であることをご安心ください。 DP-203実践準備はお金に見合う価値があることを保証します。すべてのユーザーはDP-203試験ガイドの恩恵を受けます。試験に不合格になった場合、すぐに全額のダンプを返金します。余分なペニーはすべてその価値に値します。 DP-203テストの質問が最良の選択です。

Microsoft DP-203 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Develop data processing: This section of the exam covers topics such as ingesting and transforming data, developing a batch processing solution, developing a stream processing solution, and managing batches and pipelines.

トピック 2

Design and implement data storage: In this exam section, the candidates are tested for implementing a parity strategy, designing and implementing the data exploration layer, and understanding new or updated data lineage to Microsoft Purview.

トピック 3

Secure, monitor, and optimize data storage and data processing: This section of the exam covers how to implement data security, monitor data storage and data processing, and enhance and troubleshoot data storage and data processing such as failed spark jobs.

Microsoft DP-203 (Microsoft Azure上のデータエンジニアリング)試験は、Microsoft Azureを使用してデータソリューションを構築および管理するデータエンジニアとして活躍するスキルと知識をテストする認定試験です。DP-203試験は、候補者のAzure上でのデータパイプライン、データストレージ、データ処理、およびデータセキュリティの設計、実装、および維持能力を評価するように設計されています。

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試験の準備方法-最高のDP-203無料試験試験-効果的なDP-203テスト参考書

DP-203問題集は一年間で無料更新サービスを提供することができ、DP-203認定試験の合格に大変役に立ちます。そして、もしDP-203問題集の更新版があれば、お客様にお送りいたします。DP-203問題集は全面的かつわかりやすいです。あなたはDP-203問題集をちゃんと覚えると、DP-203試験に合格することは簡単です。では、試験を心配するより、今から行動しましょう。

Microsoft Data Engineering on Microsoft Azure 認定 DP-203 試験問題 (Q274-Q279):

質問 # 274
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 Datiabricks and PolyBase in Azure Synapse Analytics.
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 tiles can be queried quickly and that the data type information is retained.
What should you recommend?

A. Parquet
B. JSON
C. CSV
D. Avro

正解：A

解説：
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

質問 # 275
You need to design a data ingestion and storage solution for the Twitter feeds. The solution must meet the customer sentiment analytics requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection b worth one point.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control
Topic 1, Contoso
Transactional Date
Contoso has three years of customer, transactional, operation, sourcing, and supplier data comprised of 10 billion records stored across multiple on-premises Microsoft SQL Server servers. The SQL server instances contain data from various operational systems. The data is loaded into the instances by using SQL server integration Services (SSIS) packages.
You estimate that combining all product sales transactions into a company-wide sales transactions dataset will result in a single table that contains 5 billion rows, with one row per transaction.
Most queries targeting the sales transactions data will be used to identify which products were sold in retail stores and which products were sold online during different time period. Sales transaction data that is older than three years will be removed monthly.
You plan to create a retail store table that will contain the address of each retail store. The table will be approximately 2 MB. Queries for retail store sales will include the retail store addresses.
You plan to create a promotional table that will contain a promotion ID. The promotion ID will be associated to a specific product. The product will be identified by a product ID. The table will be approximately 5 GB.
Streaming Twitter Data
The ecommerce department at Contoso develops and Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes
Contoso plans to implement the following changes:
* Load the sales transaction dataset to Azure Synapse Analytics.
* Integrate on-premises data stores with Azure Synapse Analytics by using SSIS packages.
* Use Azure Synapse Analytics to analyze Twitter feeds to assess customer sentiments about products.
Sales Transaction Dataset Requirements
Contoso identifies the following requirements for the sales transaction dataset:
* Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong: to the partition on the right.
* Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
* Implement a surrogate key to account for changes to the retail store addresses.
* Ensure that data storage costs and performance are predictable.
* Minimize how long it takes to remove old records.
Customer Sentiment Analytics Requirement
Contoso identifies the following requirements for customer sentiment analytics:
* Allow Contoso users to use PolyBase in an A/ure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own A/ureAD credentials.
* Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
* Store Twitter feeds in Azure Storage by using Event Hubs Capture. The feeds will be converted into Parquet files.
* Ensure that the data store supports Azure AD-based access control down to the object level.
* Minimize administrative effort to maintain the Twitter feed data records.
* Purge Twitter feed data records;itftaitJ are older than two years.
Data Integration Requirements
Contoso identifies the following requirements for data integration:
Use an Azure service that leverages the existing SSIS packages to ingest on-premises data into datasets stored in a dedicated SQL pool of Azure Synaps Analytics and transform the data.
Identify a process to ensure that changes to the ingestion and transformation activities can be version controlled and developed independently by multiple data engineers.

質問 # 276
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 277
You have data stored in thousands of CSV files in Azure Data Lake Storage Gen2. Each file has a header row followed by a properly formatted carriage return (/r) and line feed (/n).
You are implementing a pattern that batch loads the files daily into an enterprise data warehouse in Azure Synapse Analytics by using PolyBase.
You need to skip the header row when you import the files into the data warehouse. Before building the loading pattern, you need to prepare the required database objects in Azure Synapse Analytics.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: Each correct selection is worth one point

正解：

解説：

1 - Create an external data source that uses the abfs location
2 - Create an external file format and set the First_Row option.
3 - Use CREATE EXTERNAL TABLE AS SELECT (CETAS) and configure the reject options to specify reject values or percentages Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/polybase/polybase-t-sql-objects
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-external-table-as-select-transact-sql

質問 # 278
You are designing a fact table named FactPurchase in an Azure Synapse Analytics dedicated SQL pool. The table contains purchases from suppliers for a retail store. FactPurchase will contain the following columns.

FactPurchase will have 1 million rows of data added daily and will contain three years of data.
Transact-SQL queries similar to the following query will be executed daily.
SELECT
SupplierKey, StockItemKey, COUNT(*)
FROM FactPurchase
WHERE DateKey >= 20210101
AND DateKey <= 20210131
GROUP By SupplierKey, StockItemKey
Which table distribution will minimize query times?

A. round-robin
B. hash-distributed on DateKey
C. hash-distributed on PurchaseKey
D. replicated

正解：C

解説：
Hash-distributed tables improve query performance on large fact tables, and are the focus of this article. Round-robin tables are useful for improving loading speed.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute

質問 # 279
......

MicrosoftのDP-203認定を取得するには、ある程度の時間と労力が必要です。 TopexamのDP-203のような試験の場合でも、難易度係数は高く、合格率は非常に低く、効率的な学習までの限られた時間を把握することさえできます。では、学習効率をどのように改善できますか？ここでは、非常に有用な製品であるDP-203練習資料を紹介します。提供される情報とデータにより、合格率が高いためDP-203認定試験に迅速かつ効率的に合格することができます 99％から100％と高い。

DP-203テスト参考書: https://www.topexam.jp/DP-203_shiken.html

Jack Hill Jack Hill

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