Microsoft DP-203 Practice Test - Questions Answers, Page 4

General symptoms of the job hitting system resource limits include:

If the backlog event metric keeps increasing, it's an indicator that the system resource is constrained (either because of output sink throttling, or high CPU). Note: 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 nonzero, you should scale out your job: adjust Streaming Units.

Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-monitoring

Azure Databricks retains cluster configuration information for up to 70 all-purpose clusters terminated in the last 30 days and up to 30 job clusters recently terminated by the job scheduler. To keep an all-purpose cluster configuration even after it has been terminated for more than 30 days, an administrator can pin a cluster to the cluster list.

Reference:

https://docs.microsoft.com/en-us/azure/databricks/clusters/

The performance capacity of a query is determined by the user's resource class. Resource classes are pre-determined resource limits in Synapse SQL pool that govern compute resources and concurrency for query execution. Resource classes can help you configure resources for your queries by setting limits on the number of queries that run concurrently and on the compute-resources assigned to each query. There's a trade-off between memory and concurrency. Smaller resource classes reduce the maximum memory per query, but increase concurrency. Larger resource classes increase the maximum memory per query, but reduce concurrency.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/resource-classes-for-workload-management

Microsoft recommends use of sys.dm_pdw_nodes_db_partition_stats to analyze any skewness in the data.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/cheat-sheet

You can use Dynamic Management Views (DMVs) to monitor your workload including investigating query execution in SQL pool. If your queries are failing or taking a long time to proceed, you can check and monitor if you have any transactions rolling back. Example:

-- Monitor rollback

SELECT

SUM(CASE WHEN t.database_transaction_next_undo_lsn IS NOT NULL THEN 1 ELSE 0 END), t.pdw_node_id, nod.[type] FROM sys.dm_pdw_nodes_tran_database_transactions t

JOIN sys.dm_pdw_nodes nod ON t.pdw_node_id = nod.pdw_node_id GROUP BY t.pdw_node_id, nod.[type]

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-manage-monitor#monitor-transaction-log-rollback

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

The number of records for each warehouse is big enough for a good partitioning. Note: Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column. When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. 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.

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. https://cloudblogs.microsoft.com/sqlserver/2014/10/29/microsoft-adds-iot-streaming-analytics-data- production-and-workflow-services-to-azure/