Exam DP-900 topic 1 question 51 discussion

YES, NO, YES Normalizing decreases throughput for reading (need to make joins), but increases for writing (Less to write, since there are no duplicates)

NO NO YES Answers are correct Normalizing a Database increases the throughput for reading but reduces for writing. A normalized database is advantageous when operations are write-intensive or ACID compliance is required. Some advantages include: Updates run quickly due to no data being duplicated in multiple locations. Inserts run quickly since there is only a single insertion point for a piece of data, and no duplication is required. Tables are typically smaller than the tables found in non-normalized databases. This usually allows the tables to fit into the buffer, thus offering faster performance. Data integrity and consistency are an absolute must if the database must be ACID compliant. A normalized database helps immensely with such an undertaking.

RCA - 1) No 2) No 3) Yes

Yes, No, Yes

The first question is silly and non-obvious. Database normalization can improve write throughput, but not always. It depends on the complexity of the database it is applied to. If there are many foreign keys and the system is updating many tables at once, then no. If there are mostly write operations on single tables, then yes. In my experience as a developer, we normalize to improve write and read throughput, but in some rare circumstances we do not. I would select YES for the first checkbox.

Normalizing a database typically involves organizing the data to reduce redundancy and improve data integrity. While normalization can make the database more efficient for read operations and reduce data anomalies, it often leads to more complex data structures with multiple related tables. As a result, writing transactions can become more complex and slower because they may require more operations, such as inserting data into multiple tables and maintaining foreign key relationships. Thus, normalization generally decreases the throughput of writing transactions rather than increasing it.

Yes, No, Yes

1 > False. Normalizing a database generally involves organizing the data to reduce redundancy and improve data integrity. While this can make read operations more efficient by minimizing data duplhttps://www.examtopics.com/exams/microsoft/dp-900/view/11/#ication and ensuring consistency, it often has the opposite effect on write operations.

SÌ, NO, SÌ

According to Chatgpt: Normalized databases are typically better optimized for write operations (INSERT, UPDATE, DELETE) rather than read operations. The normalization process involves organizing data to minimize redundancy and dependency, which can improve data integrity but may lead to more complex query structures for certain read operations.

Take it from a DBA with over 20+ years of work experience: Yes: Normalized systems are optimized for WRITE ops; much less so for READ ops (due to join operation btw different tables). No: Analytic systems are less normalized; they have more AGGREGATE data. Yes: Queries on normalized tables require more TABLE JOINS (full, inner, left, right... joins)

Normalizing a database increases the throughput of writing transactions. --> No. Analytics systems are more normalized than transactional systems. --> No. Normalizing a database results in queries that require more joins. --> Yes.

I think the first should be No as Normalizing a database typically improves data integrity and reduces data redundancy but can sometimes lead to a slight decrease in write transaction throughput. This is because, in a normalized database, data is distributed across multiple tables, and writing transactions may require inserting or updating records in multiple tables, which can add some overhead compared to denormalized databases. However, the trade-off is improved data consistency and easier maintenance. Second - No Third - Yes

NO NO YES CORRECT ONE

Yes-No-Yes. Writing is optimized for normalized databases

ChatGPT: N/Y/Y

No, No, Yes