最新の[2022年01月31日]DP-200試験問題集で有効で更新された問題集
無料お試しまもなく終了！100%有効なDP-200試験問題集には242問があります
質問 58
You are monitoring an Azure Stream Analytics job.
You discover that the Backlogged Input Events metric is increasing slowly and is consistently non-zero.
You need to ensure that the job can handle all the events.
What should you do?
- A. Create an additional output stream for the existing input stream.
- B. Increase the number of streaming units (SUs).
- C. Remove any named consumer groups from the connection and use $default.
- D. Change the compatibility level of the Stream Analytics job.
正解: B
解説:
Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job. You should increase the Streaming Units.
Note: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.
Reference:
https://docs.microsoft.com/bs-cyrl-ba/azure/stream-analytics/stream-analytics-monitoring
質問 59
You are implementing an Azure Blob storage account for an application that has the following requirements:
Data created during the last 12 months must be readily accessible.
Blobs older than 24 months must use the lowest storage costs. This data will be accessed infrequently.
Data created 12 to 24 months ago will be accessed infrequently but must be readily accessible at the lowest storage costs.
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.
正解:
解説:
1 - Create a block blob in a Blob storage account
2 - Use an Azure Resource Manager template that has a lifecycle management policy
3 - Create a rule that has the rule actions of TierCool, TierToArchive, and Delete
4 - Schedule the lifecycle management policy to run.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts
質問 60
You are a data engineer. You are designing a Hadoop Distributed File System (HDFS) architecture. You plan to use Microsoft Azure Data Lake as a data storage repository.
You must provision the repository with a resilient data schem
a. You need to ensure the resiliency of the Azure Data Lake Storage. What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
References:
https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html#NameNode+and+DataNodes
質問 61
Your company has on-premises Microsoft SQL Server instance.
The data engineering team plans to implement a process that copies data from the SQL Server instance to Azure Blob storage. The process must orchestrate and manage the data lifecycle.
You need to configure Azure Data Factory to connect to the SQL Server instance.
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.
正解:
解説:
Explanation:
Step 1: Deploy an Azure Data Factory
You need to create a data factory and start the Data Factory UI to create a pipeline in the data factory.
Step 2: From the on-premises network, install and configure a self-hosted runtime.
To use copy data from a SQL Server database that isn't publicly accessible, you need to set up a self-hosted integration runtime.
Step 3: Configure a linked service to connect to the SQL Server instance.
References:
https://docs.microsoft.com/en-us/azure/data-factory/connector-sql-server
質問 62
You need to ensure that Azure Data Factory pipelines can be deployed. How should you configure authentication and authorization for deployments? To answer, select the appropriate options in the answer choices.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation:
The way you control access to resources using RBAC is to create role assignments. This is a key concept to understand - it's how permissions are enforced. A role assignment consists of three elements: security principal, role definition, and scope.
Scenario:
No credentials or secrets should be used during deployments
Phone-based poll data must only be uploaded by authorized users from authorized devices
Contractors must not have access to any polling data other than their own
Access to polling data must set on a per-active directory user basis
References:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview
Topic 2, Contoso Ltd
Overview
Current environment
Contoso relies on an extensive partner network for marketing, sales, and distribution. Contoso uses external companies that manufacture everything from the actual pharmaceutical to the packaging.
The majority of the company's data reside in Microsoft SQL Server database. Application databases fall into one of the following tiers:
The company has a reporting infrastructure that ingests data from local databases and partner services. Partners services consists of distributors, wholesales, and retailers across the world. The company performs daily, weekly, and monthly reporting.
Requirements
Tier 3 and Tier 6 through Tier 8 application must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
The solution must support migrating databases that support external and internal application to Azure SQL Database. The migrated databases will be supported by Azure Data Factory pipelines for the continued movement, migration and updating of data both in the cloud and from local core business systems and repositories.
Tier 7 and Tier 8 partner access must be restricted to the database only.
In addition to default Azure backup behavior, Tier 4 and 5 databases must be on a backup strategy that performs a transaction log backup eve hour, a differential backup of databases every day and a full back up every week.
Back up strategies must be put in place for all other standalone Azure SQL Databases using Azure SQL-provided backup storage and capabilities.
Databases
Contoso requires their data estate to be designed and implemented in the Azure Cloud. Moving to the cloud must not inhibit access to or availability of data.
Databases:
Tier 1 Database must implement data masking using the following masking logic:
Tier 2 databases must sync between branches and cloud databases and in the event of conflicts must be set up for conflicts to be won by on-premises databases.
Tier 3 and Tier 6 through Tier 8 applications must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of a server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
Reporting
Security and monitoring Security
A method of managing multiple databases in the cloud at the same time is must be implemented to streamlining data management and limiting management access to only those requiring access.
Monitoring
Monitoring must be set up on every database. Contoso and partners must receive performance reports as part of contractual agreements.
Tiers 6 through 8 must have unexpected resource storage usage immediately reported to data engineers.
The Azure SQL Data Warehouse cache must be monitored when the database is being used. A dashboard monitoring key performance indicators (KPIs) indicated by traffic lights must be created and displayed based on the following metrics:
Existing Data Protection and Security compliances require that all certificates and keys are internally managed in an on-premises storage.
You identify the following reporting requirements:
* Azure Data Warehouse must be used to gather and query data from multiple internal and external Title
databases
* Azure Data Warehouse must be optimized to use data from a cache
* Reporting data aggregated for external partners must be stored in Azure Storage and be made available during regular business hours in the connecting regions
* Reporting strategies must be improved to real time or near real time reporting cadence to improve competitiveness and the general supply chain
* Tier 9 reporting must be moved to Event Hubs, queried, and persisted in the same Azure region as the company's main office
* Tier 10 reporting data must be stored in Azure Blobs
Issues
Team members identify the following issues:
* Both internal and external client application run complex joins, equality searches and group-by clauses. Because some systems are managed externally, the queries will not be changed or optimized by Contoso
* External partner organization data formats, types and schemas are controlled by the partner companies
* Internal and external database development staff resources are primarily SQL developers familiar with the Transact-SQL language.
* Size and amount of data has led to applications and reporting solutions not performing are required speeds
* Tier 7 and 8 data access is constrained to single endpoints managed by partners for access
* The company maintains several legacy client applications. Data for these applications remains isolated form other applications. This has led to hundreds of databases being provisioned on a per application basis
質問 63
You need to replace the SSIS process by using Data Factory.
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.
正解:
解説:
Explanation
Scenario: A daily process creates reporting data in REPORTINGDB from the data in SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.
Step 1: Create a linked service to each database
Step 2: Create two datasets
You can create two datasets: InputDataset and OutputDataset. These datasets are of type AzureBlob. They refer to the Azure Storage linked service that you created in the previous section.
Step 3: Create a pipeline
You create and validate a pipeline with a copy activity that uses the input and output datasets.
Step 4: Add a copy activity
References:
https://docs.microsoft.com/en-us/azure/data-factory/quickstart-create-data-factory-portal
質問 64
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 questions sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You need setup monitoring for tiers 6 through 8.
What should you configure?
- A. extended events for average storage percentage that emails data engineers
- B. an alert rule to monitor CPU percentage in databases that emails data engineers
- C. an alert rule to monitor storage percentage in elastic pools that emails data engineers
- D. an alert rule to monitor CPU percentage in elastic pools that emails data engineers
- E. an alert rule to monitor storage percentage in databases that emails data engineers
正解: C
解説:
Scenario:
Tiers 6 through 8 must have unexpected resource storage usage immediately reported to data engineers.
Tier 3 and Tier 6 through Tier 8 applications must use database density on the same server and Elastic pools in a cost-effective manner.
Monitor and optimize data solutions
Testlet 4
Case Study
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 question on 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 on 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
General Overview
Litware, Inc, is an international car racing and manufacturing company that has 1,000 employees. Most employees are located in Europe. The company supports racing teams that complete in a worldwide racing series.
Physical Locations
Litware has two main locations: a main office in London, England, and a manufacturing plant in Berlin, Germany.
During each race weekend, 100 engineers set up a remote portable office by using a VPN to connect the datacentre in the London office. The portable office is set up and torn down in approximately 20 different countries each year.
Existing environment
Race Central
During race weekends, Litware uses a primary application named Race Central. Each car has several sensors that send real-time telemetry data to the London datacentre. The data is used for real-time tracking of the cars.
Race Central also sends batch updates to an application named Mechanical Workflow by using Microsoft SQL Server Integration Services (SSIS).
The telemetry data is sent to a MongoDB database. A custom application then moves the data to databases in SQL Server 2017. The telemetry data in MongoDB has more than 500 attributes. The application changes the attribute names when the data is moved to SQL Server 2017.
The database structure contains both OLAP and OLTP databases.
Mechanical Workflow
Mechanical Workflow is used to track changes and improvements made to the cars during their lifetime.
Currently, Mechanical Workflow runs on SQL Server 2017 as an OLAP system.
Mechanical Workflow has a named Table1 that is 1 TB. Large aggregations are performed on a single column of Table 1.
Requirements
Planned Changes
Litware is the process of rearchitecting its data estate to be hosted in Azure. The company plans to decommission the London datacentre and move all its applications to an Azure datacentre.
Technical Requirements
Litware identifies the following technical requirements:
* Data collection for Race Central must be moved to Azure Cosmos DB and Azure SQL Database. The data must be written to the Azure datacentre closest to each race and must converge in the least amount of time.
* The query performance of Race Central must be stable, and the administrative time it takes to perform optimizations must be minimized.
* The datacentre for Mechanical Workflow must be moved to Azure SQL data Warehouse.
* Transparent data encryption (IDE) must be enabled on all data stores, whenever possible.
* An Azure Data Factory pipeline must be used to move data from Cosmos DB to SQL Database for Race Central. If the data load takes longer than 20 minutes, configuration changes must be made to Data Factory.
* The telemetry data must migrate toward a solution that is native to Azure.
* The telemetry data must be monitored for performance issues. You must adjust the Cosmos DB Request Units per second (RU/s) to maintain a performance SLA while minimizing the cost of the Ru/s.
Data Masking Requirements
During rare weekends, visitors will be able to enter the remote portable offices. Litware is concerned that some proprietary information might be exposed. The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
* Only show the last four digits of the values in a column named SuspensionSprings.
* Only Show a zero value for the values in a column named ShockOilWeight.
質問 65
Which masking functions should you implement for each column to meet the data masking requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Default
Default uses a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
Only Show a zero value for the values in a column named ShockOilWeight.
Box 2: Credit Card
The Credit Card Masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.
Example: XXXX-XXXX-XXXX-1234
Only show the last four digits of the values in a column named SuspensionSprings.
Scenario:
The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
Only Show a zero value for the values in a column named ShockOilWeight.
Only show the last four digits of the values in a column named SuspensionSprings.
質問 66
You have an Azure SQL database that contains a table named Customer. Customer contains the columns shown in the following table.
You plan to implement a dynamic data mask for the Customer_Phone column. The mask must meet the following requirements:
The first six numerals of the customer phone numbers must be masked.
The last four digits of the customer phone numbers must be visible.
Hyphens must be preserved and displayed.
How should you configure the dynamic data mask? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/security/dynamic-data-masking?view=sql-server-ver15
質問 67
You are implementing Azure Stream Analytics functions.
Which windowing function should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Tumbling
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Box 2: Hoppping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.
Box 3: Sliding
Sliding window functions, unlike Tumbling or Hopping windows, produce an output only when an event occurs. Every window will have at least one event and the window continuously moves forward by an E (epsilon). Like hopping windows, events can belong to more than one sliding window.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
質問 68
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is
質問 69
You have the following Azure Stream Analytics query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
10 partitions x six SUs = 60 SUs is fine.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
質問 70
You need to ensure polling data security requirements are met.
Which security technologies should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: Azure Active Directory user
Scenario:
Access to polling data must set on a per-active directory user basis
Box 2: DataBase Scoped Credential
SQL Server uses a database scoped credential to access non-public Azure blob storage or Kerberos-secured Hadoop clusters with PolyBase.
PolyBase cannot authenticate by using Azure AD authentication.
References:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-database-scoped-credential-transact-sql
質問 71
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.
正解:
解説:
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-automatic-tuning
質問 72
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation:
Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html
質問 73
A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream Analytics cloud job to analyze the data. The cloud job is configured to use 120 Streaming Units (SU).
You need to optimize performance for the Azure Stream Analytics job.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Scale the SU count for the job down
- B. Implement query parallelization by partitioning the data input
- C. Implement Azure Stream Analytics user-defined functions (UDF)
- D. Scale the SU count for the job up
- E. Implement query parallelization by partitioning the data output
- F. Implement event ordering
正解: B,D
解説:
Scale out the query by allowing the system to process each input partition separately.
F: A Stream Analytics job definition includes inputs, a query, and output. Inputs are where the job reads the data stream from.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
質問 74
You have an enterprise data warehouse in Azure Synapse Analytics that contains a table named FactOnlineSales. The table contains data from the start of 2009 to the end of 2012.
You need to improve the performance of queries against FactOnlineSales by using table partitions. The solution must meet the following requirements:
Create four partitions based on the order date.
Ensure that each partition contains all the orders placed during a given calendar year.
How should you complete the T-SQL command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
質問 75
You plan to create a dimension table in Azure Data Warehouse that will be less than 1 GB.
You need to create the table to meet the following requirements:
Provide the fastest query time.
Minimize data movement.
Which type of table should you use?
- A. round-robin
- B. heap
- C. replicated
- D. hash distributed
正解: A
解説:
Usually common dimension tables or tables that doesn't distribute evenly are good candidates for round-robin distributed table.
Note: Dimension tables or other lookup tables in a schema can usually be stored as round-robin tables.
Usually these tables connect to more than one fact tables and optimizing for one join may not be the best idea.
Also usually dimension tables are smaller which can leave some distributions empty when hash distributed.
Round-robin by definition guarantees a uniform data distribution.
References:
https://blogs.msdn.microsoft.com/sqlcat/2015/08/11/choosing-hash-distributed-table-vs-round-robindistributed-table-in-azure-sql-dw-service/
質問 76
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 questions sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You need setup monitoring for tiers 6 through 8.
What should you configure?
- A. extended events for average storage percentage that emails data engineers
- B. an alert rule to monitor CPU percentage in databases that emails data engineers
- C. an alert rule to monitor storage percentage in elastic pools that emails data engineers
- D. an alert rule to monitor CPU percentage in elastic pools that emails data engineers
- E. an alert rule to monitor storage percentage in databases that emails data engineers
正解: C
解説:
Explanation
Scenario:
Tiers 6 through 8 must have unexpected resource storage usage immediately reported to data engineers.
Tier 3 and Tier 6 through Tier 8 applications must use database density on the same server and Elastic pools in a cost-effective manner.
Topic 1, Contoso Ltd
Overview
Current environment
Contoso relies on an extensive partner network for marketing, sales, and distribution. Contoso uses external companies that manufacture everything from the actual pharmaceutical to the packaging.
The majority of the company's data reside in Microsoft SQL Server database. Application databases fall into one of the following tiers:
The company has a reporting infrastructure that ingests data from local databases and partner services.
Partners services consists of distributors, wholesales, and retailers across the world. The company performs daily, weekly, and monthly reporting.
Requirements
Tier 3 and Tier 6 through Tier 8 application must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
The solution must support migrating databases that support external and internal application to Azure SQL Database. The migrated databases will be supported by Azure Data Factory pipelines for the continued movement, migration and updating of data both in the cloud and from local core business systems and repositories.
Tier 7 and Tier 8 partner access must be restricted to the database only.
In addition to default Azure backup behavior, Tier 4 and 5 databases must be on a backup strategy that performs a transaction log backup eve hour, a differential backup of databases every day and a full back up every week.
Back up strategies must be put in place for all other standalone Azure SQL Databases using Azure SQL-provided backup storage and capabilities.
Databases
Contoso requires their data estate to be designed and implemented in the Azure Cloud. Moving to the cloud must not inhibit access to or availability of data.
Databases:
Tier 1 Database must implement data masking using the following masking logic:
Tier 2 databases must sync between branches and cloud databases and in the event of conflicts must be set up for conflicts to be won by on-premises databases.
Tier 3 and Tier 6 through Tier 8 applications must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of a server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
Reporting
Security and monitoring
Security
A method of managing multiple databases in the cloud at the same time is must be implemented to streamlining data management and limiting management access to only those requiring access.
Monitoring
Monitoring must be set up on every database. Contoso and partners must receive performance reports as part of contractual agreements.
Tiers 6 through 8 must have unexpected resource storage usage immediately reported to data engineers.
The Azure SQL Data Warehouse cache must be monitored when the database is being used. A dashboard monitoring key performance indicators (KPIs) indicated by traffic lights must be created and displayed based on the following metrics:
Existing Data Protection and Security compliances require that all certificates and keys are internally managed in an on-premises storage.
You identify the following reporting requirements:
* Azure Data Warehouse must be used to gather and query data from multiple internal and external databases
* Azure Data Warehouse must be optimized to use data from a cache
* Reporting data aggregated for external partners must be stored in Azure Storage and be made available during regular business hours in the connecting regions
* Reporting strategies must be improved to real time or near real time reporting cadence to improve competitiveness and the general supply chain
* Tier 9 reporting must be moved to Event Hubs, queried, and persisted in the same Azure region as the company's main office
* Tier 10 reporting data must be stored in Azure Blobs
Issues
Team members identify the following issues:
* Both internal and external client application run complex joins, equality searches and group-by clauses.
Because some systems are managed externally, the queries will not be changed or optimized by Contoso
* External partner organization data formats, types and schemas are controlled by the partner companies
* Internal and external database development staff resources are primarily SQL developers familiar with the Transact-SQL language.
* Size and amount of data has led to applications and reporting solutions not performing are required speeds
* Tier 7 and 8 data access is constrained to single endpoints managed by partners for access
* The company maintains several legacy client applications. Data for these applications remains isolated form other applications. This has led to hundreds of databases being provisioned on a per application basis
質問 77
You manage security for a database that supports a line of business application.
Private and personal data stored in the database must be protected and encrypted.
You need to configure the database to use Transparent Data Encryption (TDE).
Which five actions should you perform in sequence? To answer, select the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解:
解説:
Explanation
Step 1: Create a master key
Step 2: Create or obtain a certificate protected by the master key
Step 3: Set the context to the company database
Step 4: Create a database encryption key and protect it by the certificate Step 5: Set the database to use encryption Example code:
USE master;
GO
CREATE MASTER KEY ENCRYPTION BY PASSWORD = '<UseStrongPasswordHere>';
go
CREATE CERTIFICATE MyServerCert WITH SUBJECT = 'My DEK Certificate';
go
USE AdventureWorks2012;
GO
CREATE DATABASE ENCRYPTION KEY
WITH ALGORITHM = AES_128
ENCRYPTION BY SERVER CERTIFICATE MyServerCert;
GO
ALTER DATABASE AdventureWorks2012
SET ENCRYPTION ON;
GO
References:
https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/transparent-data-encryption
質問 78
You have an activity in an Azure Data Factory pipeline. The activity calls a stored procedure in a data warehouse in Azure Synapse Analytics and runs daily.
You need to verify the duration of the activity when it ran last.
What should you use?
- A. Activity log in Azure Synapse Analytics
- B. activity runs in Azure Monitor
- C. the sys.dm_pdw_wait_stats data management view in Azure Synapse Analytics
- D. an Azure Resource Manager template
正解: B
解説:
Explanation
Monitor activity runs. To get a detailed view of the individual activity runs of a specific pipeline run, click on the pipeline name.
Example:
The list view shows activity runs that correspond to each pipeline run. Hover over the specific activity run to get run-specific information such as the JSON input, JSON output, and detailed activity-specific monitoring experiences.
You can check the Duration.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-visually
質問 79
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DP-200試験問題集で100%高得点させるDP-200試験解答がこちら：https://www.goshiken.com/Microsoft/DP-200-mondaishu.html