Exam DP-203 topic 2 question 2 discussion

Partition input and output. REF: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization

No event consumer was mentioned. Therefore, partitioning output is not relevant. Answer is correct

Partitioning the output data can improve write performance or manage how results are distributed, but it doesn’t directly impact the performance of data ingestion or processing within the job itself.

As suggested in the following link: https://techcommunity.microsoft.com/blog/analyticsonazure/optimize-your-stream-analytics-job%E2%80%99s-performance-using-job-diagram-simulator/3652303 "One way to optimize a Stream Analytics job’s performance is to leverage parallelism in query" and "For a job to be parallel, you need to align partition keys between all inputs, query steps, and outputs"

C. Implement query parallelization by partitioning the data output. By partitioning the data output, you can enable query parallelization. This allows the queries to be distributed across multiple partitions, which can significantly enhance performance. F. Implement query parallelization by partitioning the data input.

Partitioning on both input and output can help, but we don't know if the output is a service that doesn't support partitioning like Power BI. Scaling up will always assign more resources at least.

ChatGPT 4o C. Implement query parallelization by partitioning the data output: Output Partitioning: By partitioning the data output, you can ensure that the processing load is distributed evenly across multiple nodes, which can significantly improve performance by reducing bottlenecks in data writing. F. Implement query parallelization by partitioning the data input: Input Partitioning: Partitioning the data input allows the Stream Analytics job to process different partitions in parallel, leading to better utilization of the available streaming units and improved throughput.

C and F > same partitions > embarrassingly parallel processing

It says optimize performance, does not say that it is bad so adding SU may be unneccesary cost increase. Parallelization and embarrassingly parallel job is correct

Answer is D & F

D. Scale the SU count for the job up: Increasing the number of Streaming Units (SUs) can improve the performance of the Stream Analytics job by providing more processing power to handle the incoming data stream. C. Implement query parallelization by partitioning the data output: Partitioning the data output can help distribute the processing load across multiple partitions, allowing for parallel execution of queries and enhancing performance.

C and D

C. Implement query parallelization by partitioning the data output: "Partitioning lets you divide data into subsets based on a partition key. If your input (for example Event Hubs) is partitioned by a key, it's highly recommended to specify this partition key when adding input to your Stream Analytics job. Scaling a Stream Analytics job takes advantage of partitions in the input and output. A Stream Analytics job can consume and write different partitions in parallel, which increases throughput." D. Scale the SU count for the job up: "The total number of streaming units that can be used by a Stream Analytics job depends on the number of steps in the query defined for the job and the number of partitions for each step... All non-partitioned steps together can scale up to one streaming unit (SU V2s) for a Stream Analytics job. In addition, you can add 1 SU V2 for each partition in a partitioned step."

As there is no indication of any query parallelization currently, we have to choose to parallelize for both input and output as the first/correct answers.

Partitioning lets you divide data into subsets based on a partition key. If your input (for example Event Hubs) is partitioned by a key, it's highly recommended to specify this partition key when adding input to your Stream Analytics job. Scaling a Stream Analytics job takes advantage of partitions in the input and output. A Stream Analytics job can consume and write different partitions in parallel, which increases throughput. Ref: https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization

An embarrassingly parallel job allows the highest degree of parallelization. Looking at how the max number of stream units is calculated, it would not be useful to scale them up if you keep a bottleneck at the output. Unsure what a good reference value would be for the number of SUs, but 120 does not seem very low to me.

Scale the SU count for the job up - (ChatGPT) This will not necessarily improve the performance of your job, unless your query is CPU-bound or memory-bound. Scaling up the SU count will increase the amount of resources available for your job, but it will also increase the cost. You should first try to optimize your query by using parallelization and repartitioning techniques, and then scale up the SU count only if needed1