DP-200日本語版トレーリング問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するDP-200日本語版トレーリング問題集を暗記すれば、きっと試験に合格できます。数年以来の整理と分析によって開発されたDP-200日本語版トレーリング問題集は権威的で全面的です。 試験にパースする原因は我々問題集の全面的で最新版です。どのようにMicrosoft　DP-200日本語版トレーリング試験に準備すると悩んでいますか。 NewValidDumpsにたくさんのIT専門人士がいって、弊社の問題集に社会のITエリートが認定されて、弊社の問題集は試験の大幅カーバして、合格率が１００％にまで達します。

DP-200日本語版トレーリング認定試験に合格することは難しいようですね。

このような受験生はDP-200 - Implementing an Azure Data Solution日本語版トレーリング認定試験で高い点数を取得して、自分の構成ファイルは市場の需要と互換性があるように充分な準備をするのは必要です。 一回だけでMicrosoftのDP-200 日本語参考試験に合格したい？NewValidDumpsは君の欲求を満たすために存在するのです。NewValidDumpsは君にとってベストな選択になります。

MicrosoftのDP-200日本語版トレーリング試験は挑戦がある認定試験です。現在、書籍の以外にインターネットは知識の宝庫として見られています。NewValidDumps で、あなたにあなたの宝庫を見つけられます。

Microsoft DP-200日本語版トレーリング - あなたもIT認証資格を取りたいですか。

世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、DP-200日本語版トレーリング問題集を学ぶ必要があります。弊社のDP-200日本語版トレーリング問題集はあなたにこのチャンスを全面的に与えられます。あなたは自分の望ましいMicrosoft DP-200日本語版トレーリング問題集を選らんで、学びから更なる成長を求められます。心はもはや空しくなく、生活を美しくなります。

試験問題集が更新されると、NewValidDumpsは直ちにあなたのメールボックスにDP-200日本語版トレーリング問題集の最新版を送ります。あなたは試験の最新バージョンを提供することを要求することもできます。

DP-200 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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. 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: 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
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 are developing a solution that will use Azure Stream Analytics. The solution will accept an Azure
Blob storage file named Customers. The file will contain both in-store and online customer details.
The online customers will provide a mailing address.
You have a file in Blob storage named LocationIncomes that contains based on location. The file rarely changes.
You need to use an address to look up a median income based on location. You must output the data to Azure SQL Database for immediate use and to Azure Data Lake Storage Gen2 for long-term retention.
Solution: You implement a Stream Analytics job that has one streaming input, one reference input, two queries, and four outputs.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation
We need one reference data input for LocationIncomes, which rarely changes.
We need two queries, on for in-store customers, and one for online customers.
For each query two outputs is needed.
Note: Stream Analytics also supports input known as reference data. Reference data is either completely static or changes slowly.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-add-inputs#stream-and- reference-input
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

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Updated: May 28, 2022