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Exam AZ-220 topic 4 question 9 discussion

Actual exam question from Microsoft's AZ-220

Question #: 9
Topic #: 4

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You have an Azure Stream Analytics job that receives input from an Azure IoT hub and sends the outputs to Azure Blob storage. The job has compatibility level
1.1 and six streaming units.
You have the following query for the job.

You plan to increase the streaming unit count to 12.
You need to optimize the job to take advantage of the additional streaming units and increase the throughput.
Solution: You change the query to the following.

Does this meet the goal?

A. Yes
B. No

Show Suggested Answer

Suggested Answer: B 🗳️
Max number of Streaming Units with one step and with no partitions is 6.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization

by mgiuseppe2 at March 14, 2021, 9:14 a.m.

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Aimer4218

Highly Voted 3 years, 6 months ago

The answer is Yes. Only first step is required since the input is from EventHub /IotHub. 2nd step doesn't need to be partition as output to Blob does not require partition. Only ouput to EventHub, IotHub and Cosmos DB are required to be partition. As for the SU: (Source 2) - The input data stream is partitioned by 3. -> can be scaled up to 18 SUs: (From above we can say 1 partition can go up to 6 SU) - The query contains two steps. The input step is partitioned and the second step is not. - The SELECT statement reads from the partitioned input. Since the query satisfy above requirement, but doesn't specify data stream partition. The total SU would be around 12 which 6 is from the partition step and another 6 is from the unpartitioned step. Source : 1. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization#inputs 2. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization#calculate-the-max-streaming-units-for-a-job

upvoted 5 times

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Marusyk

Most Recent 3 years, 6 months ago

Jobs with compatibility level 1.0 or 1.1, requires you to use PARTITION BY PartitionId in all the steps of your query. Multiple steps are allowed, but they all must be partitioned by the same key. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization#:~:text=Jobs%20with%20compatibility%20level%201.0%20or%201.1%2C%20requires%20you%20to%20use%20PARTITION%20BY%20PartitionId%20in%20all%20the%20steps%20of%20your%20query.%20Multiple%20steps%20are%20allowed%2C%20but%20they%20all%20must%20be%20partitioned%20by%20the%20same%20key.

upvoted 1 times

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Leonarduino87

3 years, 8 months ago

The answer is NO...compatibility 1.1 needs to add the PARTITION BY

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tita_tovenaar

3 years, 11 months ago

answer is No because compatibility is 1.1, see example in https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization For compatibility 1.1, a multistep query requires FROM … *PARTIONBY* …in both parts

upvoted 3 times

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Stephan99

4 years ago

Since the data stream partition is set to 3, the query can be scaled up to 18 SUs (3 * 6) see https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization

upvoted 1 times

exam67

3 years, 12 months ago

How do you know that there are 3 partitions? There is no such information in the text

upvoted 2 times

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Ashwinee

4 years, 2 months ago

this will increase to 18 hence the ans is NO.

upvoted 1 times

Leonarduino87

3 years, 8 months ago

If you parallelize your stream analytics job you can add more SUs to increase your throughput but it doesn't mean that you need more SUs to complete your job (it is exactly the opposite). The answer is NO because the compatibility 1.1 requires the PARTITION BY clause

upvoted 1 times

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mgiuseppe2

4 years, 2 months ago

Correct because the second query doesn't produce the same result as the first query. You Need to use an aggregation step. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization#examples-of-scaling

upvoted 2 times

Leonarduino87

3 years, 8 months ago

No, you miss the point of the doc: if (and only if) you have a input partition key that it is different from the query partition key, you can have a spread of the values. For example, if your Event Hub (input) is partitioned by the colour of the cars (black, red and yellow cars), and you want to know how many cars have passed through the tollbooth (query), you can't obtain the result in a single query grouped by tollboothId because what you obtain is how many cars pass through the tollbooth splitted by colours instead (black cars through tollbooth 1, red cars through tollbooth 1 and yellow cars through tollbooth 1). So, you need to sum these results in a single record (all of the black, red and yellow cars through the tollbooth 1). That means another query

upvoted 1 times

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