このトレーニング資料を持っていたら、成功への道を見つけます。MicrosoftのDP-200受験トレーリング認定試験は全てのIT職員にとって大変重要な試験です。この試験に受かったら、あなたは絶対職場に廃れられることはありません。 もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。NewValidDumps のMicrosoftのDP-200受験トレーリング問題集はシラバスに従って、それにDP-200受験トレーリング認定試験の実際に従って、あなたがもっとも短い時間で最高かつ最新の情報をもらえるように、弊社はトレーニング資料を常にアップグレードしています。 それはあなたを試験に準備するときにより多くの時間を節約させます。

Azure Data Engineer Associate DP-200 常々、時間とお金ばかり効果がないです。

IT業界で発展したいなら、MicrosoftのDP-200 - Implementing an Azure Data Solution受験トレーリング試験のような国際的な試験に合格するのは重要です。 できるだけ100％の通過率を保証使用にしています。NewValidDumpsは多くの受験生を助けて彼らにMicrosoftのDP-200 認定テキスト試験に合格させることができるのは我々専門的なチームがMicrosoftのDP-200 認定テキスト試験を研究して解答を詳しく分析しますから。

それで、「就職難」の場合には、他の人々と比べて、あなたはずっと優位に立つことができます。このほど、卒業生であれば、社会人であれば、ずっと「就職難」問題が存在し、毎年、「就職氷河期」といった言葉が聞こえてくる。ブームになるIT技術業界でも、多くの人はこういう悩みがあるんですから、MicrosoftのDP-200受験トレーリングの能力を把握できるのは欠かさせないない技能であると考えられます。

Microsoft DP-200受験トレーリング - 暇の時間を利用して勉強します。

DP-200受験トレーリング問題集は唯一無にな参考資料です。DP-200受験トレーリング問題集の内容は専門的かつ全面的で、覚えやすいです。また、DP-200受験トレーリング問題集は的中率が高いです。そのいくつの点で、DP-200受験トレーリング試験に合格することを保障できます。もし、お客様はDP-200受験トレーリング問題集を買うとき、自分に適するかどうかという心配があります。その心配に対して、弊社はお客様に無料でDP-200受験トレーリング問題集のデモを提供します。そうしたら、お客様はDP-200受験トレーリング問題集を購入する前にデモをダウンロードしてやってみることができます。

そして、DP-200受験トレーリング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受験トレーリング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。

DP-200 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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 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

QUESTION NO: 4
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: 5
You have a table named SalesFact in an Azure SQL data warehouse. SalesFact contains sales data from the past 36 months and has the following characteristics:
* Is partitioned by month
* Contains one billion rows
* Has clustered columnstore indexes
All the beginning of each month, you need to remove data SalesFact that is older than 36 months as quickly as possible.
Which three actions should you perform in sequence in a stored procedure? 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: Create an empty table named SalesFact_work that has the same schema as SalesFact.
Step 2: Switch the partition containing the stale data from SalesFact to SalesFact_Work.
SQL Data Warehouse supports partition splitting, merging, and switching. To switch partitions between two tables, you must ensure that the partitions align on their respective boundaries and that the table definitions match.
Loading data into partitions with partition switching is a convenient way stage new data in a table that is not visible to users the switch in the new data.
Step 3: Drop the SalesFact_Work table.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition

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Updated: May 28, 2022