IT業種の人たちは自分のIT夢を持っているのを信じています。MicrosoftのDP-200問題集認定試験に合格することとか、より良い仕事を見つけることとか。NewValidDumpsは君のMicrosoftのDP-200問題集認定試験に合格するという夢を叶えるための存在です。 NewValidDumps を選択して１００％の合格率を確保することができて、もし試験に失敗したら、NewValidDumpsが全額で返金いたします。 我々NewValidDumpsのあなたに開発するMicrosoftのDP-200問題集ソフトはあなたの問題を解決することができます。

Azure Data Engineer Associate DP-200 できるだけ100％の通過率を保証使用にしています。

DP-200 - Implementing an Azure Data Solution問題集はMicrosoftの一つ重要な認証試験で多くのIT専門スタッフが認証される重要な試験です。 ただ、社会に入るIT卒業生たちは自分能力の不足で、DP-200 資格復習テキスト試験向けの仕事を探すのを悩んでいますか？それでは、弊社のMicrosoftのDP-200 資格復習テキスト練習問題を選んで実用能力を速く高め、自分を充実させます。その結果、自信になる自己は面接のときに、面接官のいろいろな質問を気軽に回答できて、順調にDP-200 資格復習テキスト向けの会社に入ります。

ですから、弊社のトレーニング製品はあなたが試験に合格することを助けにならなかったら、全額で返金することを保証します。MicrosoftのDP-200問題集のオンラインサービスのスタディガイドを買いたかったら、NewValidDumpsを買うのを薦めています。NewValidDumpsは同じ作用がある多くのサイトでリーダーとしているサイトで、最も良い品質と最新のトレーニング資料を提供しています。

Microsoft DP-200問題集 - しかも値段が手頃です。

多分、DP-200問題集テスト質問の数が伝統的な問題の数倍である。Microsoft　DP-200問題集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問題集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問題集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。

この試験に受かるのは難しいですが、大丈夫です。私はNewValidDumpsのMicrosoftのDP-200問題集試験トレーニング資料を選びましたから。

DP-200 PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 4
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: 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