Free DP-203 Exam Braindumps (page: 49)

Page 49 of 94

You are designing an anomaly detection solution for streaming data from an Azure IoT hub. The solution must meet the following requirements:
Send the output to Azure Synapse.
Identify spikes and dips in time series data. Minimize development and configuration effort. Which should you include in the solution?

  1. Azure Databricks
  2. Azure Stream Analytics
  3. Azure SQL Database

Answer(s): B

Explanation:

You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.


Reference:

https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/



A company uses Azure Stream Analytics to monitor devices.
The company plans to double the number of devices that are monitored.
You need to monitor a Stream Analytics job to ensure that there are enough processing resources to handle the additional load.
Which metric should you monitor?

  1. Early Input Events
  2. Late Input Events
  3. Watermark delay
  4. Input Deserialization Errors

Answer(s): C

Explanation:

There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
Not enough processing resources in Stream Analytics to handle the volume of input events. Not enough throughput within the input event brokers, so they are throttled.
Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.

Incorrect Answers:
A: Deserialization issues are caused when the input stream of your Stream Analytics job contains malformed messages.


Reference:

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling



HOTSPOT (Drag and Drop is not supported)
You are designing an enterprise data warehouse in Azure Synapse Analytics that will store website traffic analytics in a star schema.
You plan to have a fact table for website visits. The table will be approximately 5 GB.
You need to recommend which distribution type and index type to use for the table. The solution must provide the fastest query performance.

What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Hash
Consider using a hash-distributed table when:
The table size on disk is more than 2 GB.
The table has frequent insert, update, and delete operations.
Box 2: Clustered columnstore
Clustered columnstore tables offer both the highest level of data compression and the best overall query performance.


Reference:

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



You have an Azure Stream Analytics job.
You need to ensure that the job has enough streaming units provisioned. You configure monitoring of the SU % Utilization metric.
Which two additional metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  1. Backlogged Input Events
  2. Watermark Delay
  3. Function Events
  4. Out of order Events
  5. Late Input Events

Answer(s): A,B

Explanation:

To react to increased workloads and increase streaming units, consider setting an alert of 80% on the SU Utilization metric. Also, you can use watermark delay and backlogged events metrics to see if there is an impact.
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 non-zero, you should scale out your job, by increasing the SUs.


Reference:

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



Page 49 of 94



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