皆さんは節約した時間とエネルギーを利用してもっと多くの金銭を稼ぐことができます。NewValidDumpsは受験生の皆様に最も良いかつ便利なサービスを提供できるようにずっと一生懸命頑張っています。現在の時代で高効率は避けられない話題ですから、速いスピードと高効率が我々の目標です。 あなたは無料でDP-200的中関連問題復習教材をダウンロードしたいですか？もちろん、回答ははいです。だから、あなたはコンピューターでMicrosoftのウエブサイトを訪問してください。 学歴はただ踏み台だけで、あなたの地位を確保できる礎は実力です。

Azure Data Engineer Associate DP-200 それは問題ではないですよ。

NewValidDumpsは実際の環境で本格的なMicrosoftのDP-200 - Implementing an Azure Data Solution的中関連問題「Implementing an Azure Data Solution」の試験の準備過程を提供しています。 ですから、はやくNewValidDumpsのDP-200 試験攻略問題集を入手しましょう。これは高い的中率を持っている問題集で、ほかのどのような勉強法よりもずっと効果があるのです。

我々は心からあなたが首尾よく試験に合格することを願っています。あなたに便利なオンラインサービスを提供して、Microsoft DP-200的中関連問題試験問題についての全ての質問を解決して差し上げます。NewValidDumpsのMicrosoftのDP-200的中関連問題試験問題資料は質が良くて値段が安い製品です。

Microsoft DP-200的中関連問題 - それはあなたが夢を実現することを助けられます。

NewValidDumpsのMicrosoftのDP-200的中関連問題試験トレーニング資料はIT認証試験を受ける全ての受験生が試験に合格することを助けるもので、受験生からの良い評価をたくさんもらいました。NewValidDumpsを選ぶのは成功を選ぶのに等しいです。もしNewValidDumpsのMicrosoftのDP-200的中関連問題試験トレーニング資料を購入した後、学習教材は問題があれば、或いは試験に不合格になる場合は、私たちが全額返金することを保証いたしますし、私たちは一年間で無料更新サービスを提供することもできます。

きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。

DP-200 PDF DEMO:

QUESTION NO: 1
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

QUESTION NO: 2
You need to mask tier 1 data. Which functions should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
A: Default
Full masking according to the data types of the designated fields.
For string data types, use XXXX or fewer Xs if the size of the field is less than 4 characters (char, nchar, varchar, nvarchar, text, ntext).
B: email
C: Custom text
Custom StringMasking method which exposes the first and last letters and adds a custom padding string in the middle. prefix,[padding],suffix Tier 1 Database must implement data masking using the following masking logic:
References:
https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking

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
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop a data ingestion process that will import data to a Microsoft Azure SQL Data Warehouse.
The data to be ingested resides in parquet files stored in an Azure Data lake Gen 2 storage account.
You need to load the data from the Azure Data Lake Gen 2 storage account into the Azure SQL Data
Warehouse.
Solution:
1. Create an external data source pointing to the Azure storage account
2. Create a workload group using the Azure storage account name as the pool name
3. Load the data using the INSERT...SELECT statement
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation
You need to create an external file format and external table using the external data source.
You then load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure- data-lake-store

QUESTION NO: 5
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

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Updated: May 28, 2022