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Microsoft Certified: Azure Data Engineer Associate問題集でDP-203試験完全版問題、試験学習ガイド
質問 13
You have an Azure Data Factory instance that contains two pipelines named Pipeline1 and Pipeline2.
Pipeline1 has the activities shown in the following exhibit.
Pipeline2 has the activities shown in the following exhibit.
You execute Pipeline2, and Stored procedure1 in Pipeline1 fails.
What is the status of the pipeline runs?
- A. Pipeline1 and Pipeline2 succeeded.
- B. Pipeline1 succeeded and Pipeline2 failed.
- C. Pipeline1 failed and Pipeline2 succeeded.
- D. Pipeline1 and Pipeline2 failed.
正解: A
解説:
Explanation
Activities are linked together via dependencies. A dependency has a condition of one of the following:
Succeeded, Failed, Skipped, or Completed.
Consider Pipeline1:
If we have a pipeline with two activities where Activity2 has a failure dependency on Activity1, the pipeline will not fail just because Activity1 failed. If Activity1 fails and Activity2 succeeds, the pipeline will succeed.
This scenario is treated as a try-catch block by Data Factory.
Waterfall chart Description automatically generated with medium confidence
The failure dependency means this pipeline reports success.
Note:
If we have a pipeline containing Activity1 and Activity2, and Activity2 has a success dependency on Activity1, it will only execute if Activity1 is successful. In this scenario, if Activity1 fails, the pipeline will fail.
Reference:
https://datasavvy.me/category/azure-data-factory/
質問 14
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 places 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-partition-function-transact-sql?view=sql-server-ver15
質問 15
You need to create a partitioned table in an Azure Synapse Analytics dedicated SQL pool.
How should you complete the Transact-SQL statement? To answer, drag the appropriate values to the correct targets. Each value 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.
正解:
解説:
Explanation
Box 1: DISTRIBUTION
Table distribution options include DISTRIBUTION = HASH ( distribution_column_name ), assigns each row to one distribution by hashing the value stored in distribution_column_name.
Box 2: PARTITION
Table partition options. Syntax:
PARTITION ( partition_column_name RANGE [ LEFT | RIGHT ] FOR VALUES ( [ boundary_value [,...n] ] )) Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse?
質問 16
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 section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a tumbling window, and you set the window size to 10 seconds.
Does this meet the goal?
- A. No
- B. Yes
正解: B
解説:
Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
質問 17
You have an Azure subscription.
You need to deploy an Azure Data Lake Storage Gen2 Premium account. The solution must meet the following requirements:
* Blobs that are older than 365 days must be deleted.
* Administrator efforts must be minimized.
* Costs must be minimized
What should you use? To answer, select the appropriate options in the answer are a. NOTE Each correct selection is worth one point.
正解:
解説:
質問 18
You need to design a data storage structure for the product sales transactions. 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.
正解:
解説:
Reference:
https://rajanieshkaushikk.com/2020/09/09/how-to-choose-right-data-distribution-strategy-for-azure-synapse/
質問 19
You have the following Azure Data Factory pipelines
* ingest Data from System 1
* Ingest Data from System2
* Populate Dimensions
* Populate facts
ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours.
What should you do to schedule the pipelines for execution?
- A. Add a schedule trigger to all four pipelines.
- B. Create a parent pipeline that contains the four pipelines and use a schedule trigger.
- C. Add an event trigger to all four pipelines.
- D. Create a parent pipeline that contains the four pipelines and use an event trigger.
正解: B
解説:
Explanation
Schedule trigger: A trigger that invokes a pipeline on a wall-clock schedule.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
質問 20
You are designing an application that will store petabytes of medical imaging data When the data is first created, the data will be accessed frequently during the first week. After one month, the data must be accessible within 30 seconds, but files will be accessed infrequently. After one year, the data will be accessed infrequently but must be accessible within five minutes.
You need to select a storage strategy for the dat
a. The solution must minimize costs.
Which storage tier should you use for each time frame? 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/azure/storage/blobs/storage-blob-storage-tiers
質問 21
You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements:
* Minimize query latency.
* Maximize the number of users that can run queues on the cluster at the same time a Reduce overall costs without compromising other requirements Which cluster type should you recommend?
- A. High Concurrency with Auto Termination
- B. Standard with Autoscaling
- C. High Concurrency with Autoscaling
- D. Standard with Auto termination
正解: C
解説:
Explanation
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.
Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don't need to provision the cluster to match a workload.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure
質問 22
You use PySpark in Azure Databricks to parse the following JSON input.
You need to output the data in the following tabular format.
How should you complete the PySpark code? To answer, drag the appropriate values to he correct targets. Each value 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.
正解:
解説:
質問 23
You need to design a data ingestion and storage solution for the Twitter feeds. The solution must meet the customer sentiment analytics requirements.
What should you include in the solution To answer, select the appropriate options in the answer area NOTE Each correct selection b worth one point.
正解:
解説:
質問 24
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
New data is accessed frequently and must be available as quickly as possible.
Data that is older than five years is accessed infrequently but must be available within one second when requested.
Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
Costs must be minimized while maintaining the required availability.
How should you manage the data? 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/azure/storage/blobs/storage-blob-storage-tiers
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-the-general-availability-of-replicated-tables/
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/
質問 25
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.
Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? 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/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute
質問 26
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.
You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:
Automatically scale down workers when the cluster is underutilized for three minutes.
Minimize the time it takes to scale to the maximum number of workers.
Minimize costs.
What should you do first?
- A. Upgrade workspace1 to the Premium pricing tier.
- B. Set Cluster Mode to High Concurrency.
- C. Create a cluster policy in workspace1.
- D. Enable container services for workspace1.
正解: A
解説:
For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:
Scales up from min to max in 2 steps.
Can scale down even if the cluster is not idle by looking at shuffle file state.
Scales down based on a percentage of current nodes.
On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.
Note: Standard autoscaling
Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max. You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.
Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes.
Scales down exponentially, starting with 1 node.
Reference:
https://docs.databricks.com/clusters/configure.html
質問 27
You have an Azure Synapse Analytics dedicated SQL pool.
You need to ensure that data in the pool is encrypted at rest. The solution must NOT require modifying applications that query the data.
What should you do?
- A. Enable encryption at rest for the Azure Data Lake Storage Gen2 account.
- B. Use a customer-managed key to enable double encryption for the Azure Synapse workspace.
- C. Enable Transparent Data Encryption (TDE) for the pool.
- D. Create an Azure key vault in the Azure subscription grant access to the pool.
正解: C
解説:
Explanation
Transparent Data Encryption (TDE) helps protect against the threat of malicious activity by encrypting and decrypting your data at rest. When you encrypt your database, associated backups and transaction log files are encrypted without requiring any changes to your applications. TDE encrypts the storage of an entire database by using a symmetric key called the database encryption key.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overviewmana
Topic 1, Litware, inc.
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 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 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 button to return to the question.
Overview
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks.
Requirements
Business Goals
Litware wants to create a new analytics environment in Azure to meet the following requirements:
* See inventory levels across the stores. Data must be updated as close to real time as possible.
* Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
* Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Technical Requirements
Litware identifies the following technical requirements:
* Minimize the number of different Azure services needed to achieve the business goals.
* Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
* Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
* Use Azure Active Directory (Azure AD) authentication whenever possible.
* Use the principle of least privilege when designing security.
* Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use.
Files that have a modified date that is older than 14 days must be removed.
* Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
* Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Planned Environment
Litware plans to implement the following environment:
* The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
* Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
* Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
* Daily inventory data comes from a Microsoft SQL server located on a private network.
* Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year.
* Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours.
* Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
質問 28
You are designing a financial transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
TransactionType: 40 million rows per transaction type
CustomerSegment: 4 million per customer segment
TransactionMonth: 65 million rows per month
AccountType: 500 million per account type
You have the following query requirements:
Analysts will most commonly analyze transactions for a given month.
Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
- A. CustomerSegment
- B. TransactionMonth
- C. TransactionType
- D. AccountType
正解: B
解説:
For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributed databases.
Example: Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.
質問 29
You have an Azure Synapse Analytics dedicated SQL pool.
You run PDW_SHOWSPACEUSED(dbo,FactInternetSales'); and get the results shown in the following table.
Which statement accurately describes the dbo,FactInternetSales table?
- A. The table uses round-robin distribution.
- B. The table is skewed.
- C. All distributions contain data.
- D. The table contains less than 1,000 rows.
正解: C
解説:
Data skew means the data is not distributed evenly across the distributions.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
質問 30
A company purchases IoT devices to monitor manufacturing machinery. The company uses an IoT appliance to communicate with the IoT devices.
The company must be able to monitor the devices in real-time.
You need to design the solution.
What should you recommend?
- A. Azure Analysis Services using Azure Portal
- B. Azure Analysis Services using Azure PowerShell
- C. Azure Data Factory instance using Azure Portal
- D. Azure Stream Analytics cloud job using Azure PowerShell
正解: D
解説:
Explanation
Stream Analytics is a cost-effective event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications and data quickly and easily.
Monitor and manage Stream Analytics resources with Azure PowerShell cmdlets and powershell scripting that execute basic Stream Analytics tasks.
Reference:
https://cloudblogs.microsoft.com/sqlserver/2014/10/29/microsoft-adds-iot-streaming-analytics-data-production-
質問 31
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