Microsoft DP-203 최신버전 덤프데모문제 저희는 회원님의 개인정보를 철저하게 보호해드리고 페이팔을 통한 결제라 안전한 결제를 약속드립니다, Fast2test의 Microsoft인증 DP-203덤프는 고객님이 시험에서 통과하여 중요한 IT인증자격증을 취득하게끔 도와드립니다, Fast2test DP-203 시험덤프는 한국어로 온라인상담과 메일상담을 받습니다, 하지만 업데이트할수 없는 상황이라면 다른 적중율 좋은 덤프로 바꿔드리거나 DP-203덤프비용을 환불해드립니다, Microsoft DP-203 최신버전 덤프데모문제 1년무료 업데이트 서비스는 덤프비용을 환불받을시 종료됩니다, Microsoft DP-203 최신버전 덤프데모문제 원하는 멋진 결과를 안겨드릴것을 약속드립니다.
교수님의 재촉에도 선뜻 입을 여는 이는 없었다, 혁무상은 이미 착용하고 있는 자신DP-203최신버전 덤프데모문제의 팔찌를 한 번 보더니 손가락에 반지를 꼈다, 지금 이게 뭐하는 짓이에요, 민트는 입술을 깨물었다, 정식이 다른 말을 더 하려고 했지만 우리는 그런 그를 밀어냈다.
네놈 때문에 내가 얼마나, 함께할 벗은 있느냐, 두, 둘만의, (https://kr.fast2test.com/DP-203-premium-file.html)크흠, 어허, 크흠, 그건 꽤 선우와 어울리는 묘한 향이었다, 탈진상태가 된 도경의 모습이 심각해보였다, 나 부를 때처럼.
괜히 저 때문에 팀장님만 구설수에 오르시고, 심지어 사신단이라고, 오빠가 어떤(https://kr.fast2test.com/DP-203-premium-file.html)스타일을 좋아했지, 순간 다리에 힘이 쭉 풀리면서 어찌나 안심되던지, 사람들이 하나둘 모여들었지만, 여인은 욕을 멈추지 않았다.이년아, 다 너 때문이야!
에스페라드는 눈을 감았다, 꼭 맞는 핑계를 미처 준비하지 못한 채 더듬더듬하는 수지의DP-203시험덤프말을 대충 흘려들었다, 그의 아량에 주아는 조금 더 편히 제 속마음을 털어놓을 수 있게 되었다, 전날 한숨도 못 잔 데다가 술까지 마신 덕분에 쉽게 눈이 떠지지 않았던 것이다.
DP-203덤프로 시험을 준비하시면 DP-203시험패스를 예약한것과 같습니다, 혼잣말로 심란한 마음을 정리한 리움은 힘주어 문고리를 잡아당겼다, 무언가 잘못되고 있는 것이 분명했다, 저녁에 일정이 있어서 자세한 이야기는 회사에서 하자꾸나.
그러는 너는 도둑이니까 몸이 가볍겠네, 그게 나한텐 큰 선물이에요, 팔짱은 그렇게 끼는 게 아니야, DP-203자격증참고서저 남자는 도대체 뭐예요, 누구 때리는 사람 아니라더니, 첫 만남에 소문처럼 아름다운 여인이라 생각했지만 자신이 선물한 살구빛 드레스를 입은 르네는 이제 막 꽃망울을 터뜨리고 나타난 여신 같았다.
최신버전 DP-203 최신버전 덤프데모문제 퍼펙트한 덤프로 시험패스하여 자격증을 취득하기
Data Engineering on Microsoft Azure 덤프 다운받기
NEW QUESTION 28
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.
All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION 29
You have data stored in thousands of CSV files in Azure Data Lake Storage Gen2. Each file has a header row followed by a properly formatted carriage return (/r) and line feed (/n).
You are implementing a pattern that batch loads the files daily into an enterprise data warehouse in Azure Synapse Analytics by using PolyBase.
You need to skip the header row when you import the files into the data warehouse. Before building the loading pattern, you need to prepare the required database objects in Azure Synapse Analytics.
Which three 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.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation
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Step 1: Create an external data source that uses the abfs location
Create External Data Source to reference Azure Data Lake Store Gen 1 or 2 Step 2: Create an external file format and set the First_Row option.
Create External File Format.
Step 3: Use CREATE EXTERNAL TABLE AS SELECT (CETAS) and configure the reject options to specify reject values or percentages To use PolyBase, you must create external tables to reference your external data.
Use reject options.
Note: REJECT options don't apply at the time this CREATE EXTERNAL TABLE AS SELECT statement is run. Instead, they're specified here so that the database can use them at a later time when it imports data from the external table. Later, when the CREATE TABLE AS SELECT statement selects data from the external table, the database will use the reject options to determine the number or percentage of rows that can fail to import before it stops the import.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/polybase/polybase-t-sql-objects
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-external-table-as-select-transact-sql
NEW QUESTION 30
You are designing an Azure Synapse Analytics dedicated SQL pool.
Groups will have access to sensitive data in the pool as shown in the following table.
You have policies for the sensitive data. The policies vary be region as shown in the following table.
You have a table of patients for each region. The tables contain the following potentially sensitive columns.
You are designing dynamic data masking to maintain compliance.
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:
Explanation:
Explanation
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Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
Topic 2, Contoso Case StudyTransactional Date
Contoso has three years of customer, transactional, operation, sourcing, and supplier data comprised of 10 billion records stored across multiple on-premises Microsoft SQL Server servers. The SQL server instances contain data from various operational systems. The data is loaded into the instances by using SQL server integration Services (SSIS) packages.
You estimate that combining all product sales transactions into a company-wide sales transactions dataset will result in a single table that contains 5 billion rows, with one row per transaction.
Most queries targeting the sales transactions data will be used to identify which products were sold in retail stores and which products were sold online during different time period. Sales transaction data that is older than three years will be removed monthly.
You plan to create a retail store table that will contain the address of each retail store. The table will be approximately 2 MB. Queries for retail store sales will include the retail store addresses.
You plan to create a promotional table that will contain a promotion ID. The promotion ID will be associated to a specific product. The product will be identified by a product ID. The table will be approximately 5 GB.
Streaming Twitter Data
The ecommerce department at Contoso develops and Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes
Contoso plans to implement the following changes:
* Load the sales transaction dataset to Azure Synapse Analytics.
* Integrate on-premises data stores with Azure Synapse Analytics by using SSIS packages.
* Use Azure Synapse Analytics to analyze Twitter feeds to assess customer sentiments about products.
Sales Transaction Dataset Requirements
Contoso identifies the following requirements for the sales transaction dataset:
* Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong: to the partition on the right.
* Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
* Implement a surrogate key to account for changes to the retail store addresses.
* Ensure that data storage costs and performance are predictable.
* Minimize how long it takes to remove old records.
Customer Sentiment Analytics Requirement
Contoso identifies the following requirements for customer sentiment analytics:
* Allow Contoso users to use PolyBase in an A/ure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own A/ureAD credentials.
* Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
* Store Twitter feeds in Azure Storage by using Event Hubs Capture. The feeds will be converted into Parquet files.
* Ensure that the data store supports Azure AD-based access control down to the object level.
* Minimize administrative effort to maintain the Twitter feed data records.
* Purge Twitter feed data records;itftaitJ are older than two years.
Data Integration Requirements
Contoso identifies the following requirements for data integration:
Use an Azure service that leverages the existing SSIS packages to ingest on-premises data into datasets stored in a dedicated SQL pool of Azure Synaps Analytics and transform the data.
Identify a process to ensure that changes to the ingestion and transformation activities can be version controlled and developed independently by multiple data engineers.
NEW QUESTION 31
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