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Azure Data Engineer Associate DP-200 自分の幸せは自分で作るものだと思われます。

Azure Data Engineer Associate DP-200トレーリングサンプル - Implementing an Azure Data Solution そのほかに、我々はあなたの個人情報の安全性を保証します。 あなたは弊社の高品質Microsoft　DP-200 試験勉強攻略試験資料を利用して、一回に試験に合格します。NewValidDumpsのMicrosoft　DP-200 試験勉強攻略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。

弊社のDP-200トレーリングサンプル真題を入手して、試験に合格する可能性が大きくなります。社会と経済の発展につれて、多くの人はIT技術を勉強します。なぜならば、IT職員にとって、MicrosoftのDP-200トレーリングサンプル資格証明書があるのは肝心な指標であると言えます。

我々Microsoft DP-200トレーリングサンプル問題集を利用し、試験に参加しましょう。

多分、DP-200トレーリングサンプルテスト質問の数が伝統的な問題の数倍である。Microsoft　DP-200トレーリングサンプル試験参考書は全ての知識を含めて、全面的です。そして、DP-200トレーリングサンプル試験参考書の問題は本当の試験問題とだいたい同じことであるとわかります。DP-200トレーリングサンプル試験参考書があれば,ほかの試験参考書を勉強する必要がないです。

あなたはまだ躊躇しているなら、NewValidDumpsのDP-200トレーリングサンプル問題集デモを参考しましょ。なにごとによらず初手は难しいです、どのようにMicrosoft　DP-200トレーリングサンプル試験への復習を始めて悩んでいますか。

DP-200 PDF DEMO:

QUESTION NO: 1
A company builds an application to allow developers to share and compare code. The conversations, code snippets, and links shared by people in the application are stored in a Microsoft
Azure SQL Database instance.
The application allows for searches of historical conversations and code snippets.
When users share code snippets, the code snippet is compared against previously share code snippets by using a combination of Transact-SQL functions including SUBSTRING, FIRST_VALUE, and
SQRT. If a match is found, a link to the match is added to the conversation.
Customers report the following issues:
* Delays occur during live conversations
* A delay occurs before matching links appear after code snippets are added to conversations You need to resolve the performance issues.
Which technologies should you use? To answer, drag the appropriate technologies to the correct issues. Each technology 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
Box 1: memory-optimized table
In-Memory OLTP can provide great performance benefits for transaction processing, data ingestion, and transient data scenarios.
Box 2: materialized view
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The Materialized View pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view

QUESTION NO: 2
You are implementing automatic tuning mode for Azure SQL databases.
Automatic tuning is configured as shown in the following table.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Automatic tuning options can be independently enabled or disabled per database, or they can be configured on SQL Database servers and applied on every database that inherits settings from the server. SQL Database servers can inherit Azure defaults for Automatic tuning settings. Azure defaults at this time are set to FORCE_LAST_GOOD_PLAN is enabled, CREATE_INDEX is enabled, and
DROP_INDEX is disabled.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-automatic-tuning

QUESTION NO: 3
You develop data engineering solutions for a company. You must migrate data from
Microsoft Azure Blob storage to an Azure SQL Data Warehouse for further transformation. You need to implement the solution.
Which four 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.
Answer:
Explanation
Step 1: Provision an Azure SQL Data Warehouse instance.
Create a data warehouse in the Azure portal.
Step 2: Connect to the Azure SQL Data warehouse by using SQL Server Management Studio Connect to the data warehouse with SSMS (SQL Server Management Studio) Step 3: Build external tables by using the SQL Server Management Studio Create external tables for data in Azure blob storage.
You are ready to begin the process of loading data into your new data warehouse. You use external tables to load data from the Azure storage blob.
Step 4: Run Transact-SQL statements to load data.
You can use the CREATE TABLE AS SELECT (CTAS) T-SQL statement to load the data from Azure
Storage Blob into new tables in your data warehouse.
References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/sql-data-warehouse/load-data- from-azure-blob

QUESTION NO: 4
You manage an enterprise data warehouse in Azure Synapse Analytics.
Users report slow performance when they run commonly used queries. Users do not report performance changes for infrequently used queries.
You need to monitor resource utilization to determine the source of the performance issues.
Which metric should you monitor?
A. DWU limit
B. Data Warehouse Units (DWU) used
C. Data IO percentage
D. Cache hit percentage
Answer: D
Explanation
The Azure Synapse Analytics storage architecture automatically tiers your most frequently queried columnstore segments in a cache residing on NVMe based SSDs designed for Gen2 data warehouses.
Greater performance is realized when your queries retrieve segments that are residing in the cache.
You can monitor and troubleshoot slow query performance by determining whether your workload is optimally leveraging the Gen2 cache.
Note: As of November 2019, Azure SQL Data Warehouse is now Azure Synapse Analytics.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-how-to-monitor- cache
https://docs.microsoft.com/bs-latn-ba/azure/sql-data-warehouse/sql-data-warehouse-concept- resource-utilization

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 scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
* A workload for data engineers who will use Python and SQL
* A workload for jobs that will run notebooks that use Python, Spark, Scala, and SQL
* A workload that data scientists will use to perform ad hoc analysis in Scala and R The enterprise architecture team at your company identifies the following standards for Databricks environments:
* The data engineers must share a cluster.
* The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
* All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databrick clusters for the workloads.
Solution: You create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation
No need for a High Concurrency cluster for each data scientist.
Standard clusters are recommended for a single user. Standard can run workloads developed in any language:
Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
References:
https://docs.azuredatabricks.net/clusters/configure.html

MicrosoftのEMC D-PST-DY-23の認定試験は君の実力を考察するテストでございます。 CompTIA 220-1101問題集はIT専門家が長年の研究したことです。 SAP C-THR12-2311 - NewValidDumpsは君のために良い訓練ツールを提供し、君のMicrosoft認証試に高品質の参考資料を提供しいたします。 でも、あなたはHuawei H23-221_V1.0試験参考書を買ったお客様のコメントを見ると、すぐ信じるようになります。 NewValidDumpsの専門家チームがMicrosoftのMicrosoft SC-900認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。

Updated: May 28, 2022