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解決法はDP-200認定デベロッパー問題集は購入することです。

現在、MicrosoftのDP-200 - Implementing an Azure Data Solution認定デベロッパー認定試験に受かりたいIT専門人員がたくさんいます。 そのデモはDP-200 参考書試験資料の一部を含めています。私たちは本当にお客様の貴重な意見をDP-200 参考書試験資料の作りの考慮に入れます。

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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。

DP-200 PDF DEMO:

QUESTION NO: 1
Your company uses Azure SQL Database and Azure Blob storage.
All data at rest must be encrypted by using the company's own key. The solution must minimize administrative effort and the impact to applications which use the database.
You need to configure security.
What should you implement? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
Box 1: transparent data encryption
TDE with customer-managed keys in Azure Key Vault allows to encrypt the Database Encryption Key
(DEK) with a customer-managed asymmetric key called TDE Protector. This is also generally referred to as Bring Your Own Key (BYOK) support for Transparent Data Encryption.
Note: Transparent data encryption encrypts the storage of an entire database by using a symmetric key called the database encryption key. This database encryption key is protected by the transparent data encryption protector.
Transparent data encryption (TDE) helps protect Azure SQL Database, Azure SQL Managed Instance, and Azure Data Warehouse against the threat of malicious offline activity by encrypting data at rest.
It performs real-time encryption and decryption of the database, associated backups, and transaction log files at rest without requiring changes to the application.
Box 2: Storage account keys
You can rely on Microsoft-managed keys for the encryption of your storage account, or you can manage encryption with your own keys, together with Azure Key Vault.
References:
https://docs.microsoft.com/en-us/azure/sql-database/transparent-data-encryption-azure-sql
https://docs.microsoft.com/en-us/azure/storage/common/storage-service-encryption

QUESTION NO: 2
A company builds an application to allow developers to share and compare code. The conversations, code snippets, and links shared by people in the application are stored in a Microsoft
Azure SQL Database instance.
The application allows for searches of historical conversations and code snippets.
When users share code snippets, the code snippet is compared against previously share code snippets by using a combination of Transact-SQL functions including SUBSTRING, FIRST_VALUE, and
SQRT. If a match is found, a link to the match is added to the conversation.
Customers report the following issues:
* Delays occur during live conversations
* A delay occurs before matching links appear after code snippets are added to conversations You need to resolve the performance issues.
Which technologies should you use? To answer, drag the appropriate technologies to the correct issues. Each technology may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation
Box 1: memory-optimized table
In-Memory OLTP can provide great performance benefits for transaction processing, data ingestion, and transient data scenarios.
Box 2: materialized view
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The Materialized View pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view

QUESTION NO: 3
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: 4
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: 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