Exam DP-203 topic 1 question 62 discussion

Remembering that we have data splitted in distribution (60 nodes) and considering that we Need a MINMIUM 1 million rows per distribution, we have: A. once per month = 30 milion / 60 = 500k record per partition B. once per year = 360 milion / 60 = 6 milion record per partition C. once per day = about 1 milion / 60 = 16k record per partition D. once per week =about 7.5 milion / 60 = 125k record per partition correct should be B

Correct, Considering the high volume of data, for faster queries its recommended to create fewer partitions. " If a table contains fewer than the recommended minimum number of rows per partition(i.e. 60 million rows per month for 60 distributed partitions, consider using fewer partitions in order to increase the number of rows per partition."

I would say its option A. - Efficiently load new data (in this case, 30 million new rows each month). - Improve query performance for time-based queries.

I think it should be by month. There are 12 months in a year. The table already has 1 billion rows, we have 60 distribution nodes. So 1 billion/60 = 16.6 Million approx per node. We get 30 million data per month, we distribute it to 60 nodes, that is 30 Million/60 = 50,000 rows per node. Now we have 16.6 million + 50,000 rows per node that’s 16,650,00 rows. Now if we partition by month that’s 16,650,00/12 we’ll have close to 138750 which is 1.3 million. And the optimal size per partition is close to 1 million. Therefore A

Creating a partition once per month strikes the right balance between performance, manageability, and data loading efficiency

I think the loading performance over here is the determining factor. With a partition by month the date can be quickly swapped into the table with a partition swap. https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition

The table currently has 1 billion rows. With 60 partitions, this means there are approximately 16,666,666 rows per partition. Each month, an additional 30 million rows will be added. After the first month, the table will have 1.03 billion rows, resulting in approximately 17,166,666 rows per partition. I recommend going with Option A: partitioning once per month.

We have to consider all information given to us about Table1, including existing data, in order to optimize query performance and data loading for the specific table. So, a year after Table1 will contain 1360M i.e. around 22.6M per distribution having specified one partition per year. Instead, if we specify one partition by month, then we will have around 1.8M per distribution and partition. Typical analytical queries involve filter predicates for a specific month. Isn't it more efficient to process 1.8M instead of 22.6M ? (optimize query performance). Loading data to a new empty or smaller partition isn't it faster than loading it to one with pre-existing data or larger partition (optimize data loading)? The above leads us to "A. once per month".

It's B for that reason: 12month * 30 mln / 60 nodes = 6 mln record per partition

B) once per year, because your partition should contain more than 60 mln rows (1 mln for each of 60 nodes)

B. once per year = 360 milion / 60 = 6 milion record per partition

It is about partition, each distribution has several partitions.

A partition is divided into distribution units. A) eleven per month => 30 million / 60 = 500k rows per partition B) eleven per year => 360 million / 60 = 6 million rows per partition C) eleven per day => 1 million / 60 = 15k rows per partition D) eleven per week => 7.5~ million/60 = 125k rows per partition But since I already have 1 billion rows distributed, each distribution node would have 16.7 million rows, fulfilling more than the minimum, since I am interested in having a certain reasonable number of partitions to increase the speed of the queries, I would choose once per month, because once per year would create very few partitions. Therefore the answer is A)

Given that you’ll be adding 30 million rows per month, consider partitioning once per month based on the Sales Date column. This approach balances data maintenance and query performance while avoiding excessive partitioning. Therefore, the correct answer is A. once per month

To optimize query performance and data loading for your Azure Synapse Analytics dedicated SQL pool table, you should create a partition based on the Sales Date column. Partitioning Frequency: The decision on how often to create a partition depends on the data volume and your specific use case. Minimum Rows per Partition: For optimal compression and performance of clustered columnstore tables, it’s recommended to have a minimum of 1 million rows per distribution and partition. Automatic Distribution: Dedicated SQL pools already divide each table into 60 distributed databases. Recommendation: Given that you’ll be adding 30 million rows per month, consider partitioning once per month based on the Sales Date column. This approach balances data maintenance and query performance while avoiding excessive partitioning. Therefore, the correct answer is A. once per month

I chose D Reason: For optimal performance and compression the followings are true; The minimum record to be load is 60M permonth. This implies that for ; *Per week = 60/4= 15M records *Pe rday = 15/7 =2.1M records *Per year= 12 *60M = 740M and so on for hours ,minutes ,sec. in the right proportions. Considering ,the case at hand. Adding 30M rows monthly will have negative impact on the performance and compression of the table .The reason is that the quantity of records to load is less than the required minimum value of 60M for monthly partition. In other to correct this ,30M minimum rows show be added every 2 weeks(not permonth) or 10M minimum rows per week. So ,the most reasonable advice is to load the table perweek for optimal performance and compression.

To partition Table1 based on the Sales Date column, you can create a partition for each month. Since thirty million rows will be added to Table1 each month, creating a partition once per month would be the most optimal solution for query performance and data loading 1. This way, the data can be easily queried and loaded, and the partitioning will not be too granular or too coarse 1. Therefore, the answer is A. once per month.