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A steadily rising competition has been noted in the tech field. Countless candidates around the globe aspire to be Administering Relational Databases on Microsoft Azure in this field. Microsoft DP-300 stand out from the rest of the Microsoft professionals. Once you become Microsoft certified, a whole new scope opens up to you and you are immediately hired by reputed firms. Even though the Administering Relational Databases on Microsoft Azure boosts your career options, you have to pass the DP-300 Exam. This Administering Relational Databases on Microsoft Azure exam serves to filter out the capable from incapable candidates.
| Section | Objectives |
|---|---|
| Plan and implement data platform resources | - Deploy and configure infrastructure
|
| Plan and implement a high availability and disaster recovery environment | - Backup and restore
|
| Optimize query performance | - Analyze query performance
|
| Monitor and optimize operational resources | - Monitor database performance
|
| Perform automation | - Automate database tasks
|
| Implement a secure environment | - Implement data protection
|
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NEW QUESTION # 372
You have an Azure SQL database named SQL1.
You need to implement a disaster recovery solution for SQL1. The solution must minimize the following:
- The recovery point objective (RPO)
- The recovery time objective (RTO)
- Administrative effort
What should you include in the solution?
Answer: D
Explanation:
https://learn.microsoft.com/en-us/azure/azure-sql/database/business-continuity-high-availability- disaster-recover-hadr-overview?view=azuresql Recovery method RTO RPO Geo-restore from geo-replicated backups 12 h 1 h Auto-failover groups 1 h 5 s Manual database failover 30 s 5 s Auto fail over groups gives the lesser RPO&RTO and less administrative effor than manual fail over.
NEW QUESTION # 373
HOTSPOT
You need to recommend the appropriate purchasing model and deployment option for the 30 new databases.
The solution must meet the technical requirements and the business requirements.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Answer:
Explanation:
Section: [none]
Explanation:
Box 1: DTU
Scenario:
* The 30 new databases must scale automatically.
* Once all requirements are met, minimize costs whenever possible.
You can configure resources for the pool based either on the DTU-based purchasing model or the vCore-based purchasing model.
In short, for simplicity, the DTU model has an advantage. Plus, if you're just getting started with Azure SQL Database, the DTU model offers more options at the lower end of performance, so you can get started at a lower price point than with vCore.
Box 2: An Azure SQL database elastic pool
Azure SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single server and share a set number of resources at a set price. Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/elastic-pool-overview
https://docs.microsoft.com/en-us/azure/azure-sql/database/reserved-capacity-overview Testlet 2 This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam.
You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Contoso, Ltd. is a clothing retailer based in Seattle. The company has 2,000 retail stores across the United States and an emerging online presence.
The network contains an Active Directory forest named contoso.com. The forest is integrated with an Azure Active Directory (Azure AD) tenant named contoso.com. Contoso has an Azure subscription associated to the contoso.com Azure AD tenant.
Existing Environment
Transactional Data
Contoso has three years of customer, transaction, operational, 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 operations 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 periods. 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 an Azure logic app that captures trending Twitter feeds referencing the company's products and pushes the products to Azure Event Hubs.
Planned Changes and Requirements
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 Requirements
Contoso identifies the following requirements for customer sentiment analytics:
* Allow Contoso users to use PolyBase in an Azure 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 Azure AD 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 that 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 Synapse 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 # 374
You plan to migrate on-premises Microsoft SQL Server databases to Azure.
You need to identify which deployment and resiliency options meet the following requirements:
* Support user-initiated backups.
* Support multiple automatically replicated instances across Azure regions.
* Minimize administrative effort to implement and maintain business continuity.
What should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: SQL Server on Azure VMs
SQL Server on Azure Virtual Machines can take advantage of Automated Backup, which regularly creates backups of your database to blob storage. You can also manually use this technique.
Box 2: Active geo-replication
Geo-replication for services such as Azure SQL Database and Cosmos DB will create secondary replicas of your data across multiple regions. While both services will automatically replicate data within the same region, geo-replication protects you against a regional outage by enabling you to fail over to a secondary region.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/virtual-machines/windows/sql-server-on-azure-vm-iaas-what-is
https://docs.microsoft.com/en-us/dotnet/architecture/cloud-native/infrastructure-resiliency-azure
NEW QUESTION # 375
You have an Azure subscription that contains an Azure SQL database. The database contains a table named table1 that uses partitioned columnstores. You need to configure table1 to meet the following requirements:
* Each partition must be compressed
* The compression ratio must be maximized.
* You must be able to index the compressed data.
What should you use?
Answer: D
Explanation:
Basic Concept: This question tests performance optimization for SQL workloads, including Query Store, automatic tuning, index/statistics maintenance, and engine-level resource behavior.
Why C is Correct: columnstore archival compresssion is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. This fits the case because the requested outcome is: You need to configure table1 to meet the following requirements: * Each partition must be compressed * The compression ratio must be maximized.
The selected feature addresses that outcome directly rather than relying on a workaround.
Why A is Wrong: page compression is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. It would produce a different operational signal than the one needed to investigate, alert, or tune the workload in this question.
Why B is Wrong: columnstore compression is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. It does not expose the required metric, query history, wait/blocking signal, or tuning mechanism; using it would not give the administrator the evidence requested.
Why D is Wrong: GZIP compression is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. It would produce a different operational signal than the one needed to investigate, alert, or tune the workload in this question.
NEW QUESTION # 376
You need to design an analytical storage solution for the transactional dat a. The solution must meet the sales transaction dataset requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://rajanieshkaushikk.com/2020/09/09/how-to-choose-right-data-distribution-strategy-for-azure-synapse/
NEW QUESTION # 377
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