だから、弊社の提供するDP-200専門知識内容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専門知識内容問題集は権威的で全面的です。DP-200専門知識内容問題集を利用して試験に合格できます。 我々は全て平凡かつ普通な人で、時には勉強したものをこなしきれないですから、忘れがちになります。NewValidDumpsのMicrosoftのDP-200専門知識内容試験トレーニング資料を見つけたら、これはあなたが購入しなければならないものを知ります。 NewValidDumpsは同業の中でそんなに良い地位を取るの原因は弊社のかなり正確な試験の練習問題と解答そえに迅速の更新で、このようにとても良い成績がとられています。

DP-200専門知識内容認定試験に合格することは難しいようですね。

Azure Data Engineer Associate DP-200専門知識内容 - Implementing an Azure Data Solution あなたより優れる人は存在している理由は彼らはあなたの遊び時間を効率的に使用できることです。 NewValidDumpsは君にとってベストな選択になります。ここには、私たちは君の需要に応じます。

弊社のMicrosoft　DP-200専門知識内容問題集を使用した後、DP-200専門知識内容試験に合格するのはあまりに難しくないことだと知られます。我々NewValidDumps提供するDP-200専門知識内容問題集を通して、試験に迅速的にパースする技をファンドできます。あなたのご遠慮なく購買するために、弊社は提供する無料のMicrosoft　DP-200専門知識内容問題集デーモをダウンロードします。

Microsoft DP-200専門知識内容 - 心はもはや空しくなく、生活を美しくなります。

今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。DP-200専門知識内容認定試験はMicrosoftの中に重要な認証試験の一つですが、NewValidDumpsにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってMicrosoft DP-200専門知識内容「Implementing an Azure Data Solution」認証試験に参加する方に対して問題集を研究続けています。

また、DP-200専門知識内容問題集に疑問があると、メールで問い合わせてください。現在IT技術会社に通勤しているあなたは、MicrosoftのDP-200専門知識内容試験認定を取得しましたか？DP-200専門知識内容試験認定は給料の増加とジョブのプロモーションに役立ちます。

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
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