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Microsoft DP-200 Online Practice Exam Questions
The questions of DP-200 were last updated on Jul 27,2021 .
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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 scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads: ✑ A workload for data engineers who will use Python and SQL ✑ A workload for jobs that will run notebooks that use Python, Spark, Scala, and SQL ✑ A workload that data scientists will use to perform ad hoc analysis in Scala and R The enterprise architecture team at your company identifies the following standards for Databricks environments: ✑ The data engineers must share a cluster. ✑ The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster. ✑ All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists. You need to create the Databrick clusters for the workloads. Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs. Does this meet the goal?
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 have an Azure subscription that contains an Azure Storage account. You plan to implement changes to a data storage solution to meet regulatory and compliance standards. Every day, Azure needs to identify and delete blobs that were NOT modified during the last 100 days. Solution: You schedule an Azure Data Factory pipeline. Does this meet the goal?
Explanation: Instead apply an Azure Blob storage lifecycle policy. Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal
Your company uses several Azure HDInsight clusters. The data engineering team reports several errors with some application using these clusters. You need to recommend a solution to review the health of the clusters. What should you include in you recommendation?
Explanation: Azure Monitor logs integration. Azure Monitor logs enables data generated by multiple resources such as HDInsight clusters, to be collected and aggregated in one place to achieve a unified monitoring experience. As a prerequisite, you will need a Log Analytics Workspace to store the collected data. If you have not already created one, you can follow the instructions for creating a Log Analytics Workspace. You can then easily configure an HDInsight cluster to send many workload-specific metrics to Log Analytics. References: https://azure.microsoft.com/sv-se/blog/monitoring-on-azure-hdinsight-part-2-cluster-health-and-availability/
HOTSPOT You are building an Azure Stream Analytics job that queries reference data from a product catalog file. The file is updated daily. The reference data input details for the file are shown in the Input exhibit. The storage account container view is shown in the Refdata exhibit. You need to configure the Stream Analytics job to pick up the new reference data. What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation: Table Description automatically generated Box 1: {date}/product.csv In the 2nd exhibit we see: Location: refdata / 2020-03-20 Note: Path Pattern: This is a required property that is used to locate your blobs within the specified container. Within the path, you may choose to specify one or more instances of the following 2 variables: {date}, {time} Example 1: products/{date}/{time}/product-list.csv Example 2: products/{date}/product-list.csv Example 3: product-list.csv Box 2: YYYY-MM-DD Note: Date Format [optional]: If you have used {date} within the Path Pattern that you specified, then you can select the date format in which your blobs are organized from the drop-down of supported formats. Example: YYYY/MM/DD, MM/DD/YYYY, etc.
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?
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-query-activity
Exam Code: DP-200Q & A: 242 Q&AsUpdated: Jul 27,2021
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