Exam Professional Data Engineer topic 1 question 32 discussion

I hate it when I read the question, than I think oh easy and I KNOW the answer, then I look at the choices and the answer I thought of is just not there at all... and I realize I absolutely have no idea :'D

Correct A

Believe me all questions were from Exam topic all were there yesterday in exam. But yes dont go with starting questions mainly focus questions after 200 and latest questions are at last page.

B is a Lie C and D are actually not recommended A is correct as it will help in even distribution of load and avoid hotspots

Improving performance in Google Cloud Bigtable involves optimizing the schema design to distribute the load efficiently across the clusters. Given the scenario, the best option would be: A. Redefine the schema by evenly distributing reads and writes across the row space of the table. Explanation: Distributing reads and writes evenly across the row space helps prevent hotspots and ensures that the load is spread evenly, avoiding performance bottlenecks. Google Cloud Bigtable's performance is influenced by how well the data is distributed across the tablet servers, and evenly distributing the load can lead to better performance. This approach aligns with best practices for designing scalable and performant Bigtable schemas.

The comment from hilel_eth totally makes sense to me. I would go with A

Guys, how relevant are these questions, as of Aug 14, 2023 Could we clear the PDE exam with these set of questions?

A is the only one that makes sense and is correct

I understand why it could be A. But why not B also? Is it because of the typo saying BigDate instead of BigTable?

A to avoid hot-spotting https://cloud.google.com/bigtable/docs/schema-design

A - Make sure you're reading and writing many different rows in your table. Bigtable performs best when reads and writes are evenly distributed throughout your table, which helps Bigtable distribute the workload across all of the nodes in your cluster. If reads and writes cannot be spread across all of your Bigtable nodes, performance will suffer. https://cloud.google.com/bigtable/docs/performance#troubleshooting

A good way to improve read and write performance in a database system like Google Cloud Bigtable is to redefine the schema of the table so that reads and writes are evenly distributed across the row space of the table. This can help reduce bottlenecks in processing capacity and improve efficiency in table management. In addition, by evenly distributing read and write operations, it can prevent the accumulation of operations in one part of the table, which can improve the overall performance of the system.

https://cloud.google.com/bigtable/docs/keyvis-overview#what-is-keyvis To accomplish these goals, Key Visualizer can help you complete the following tasks: Check whether your reads or writes are creating hotspots on specific rows

A is correct https://cloud.google.com/bigtable/docs/performance#troubleshooting If you find that you're reading and writing only a small number of rows, you might need to redesign your schema so that reads and writes are more evenly distributed.

correct answer -> Redefine the schema by evenly distributing reads and writes across the row space of the table. Make sure you're reading and writing many different rows in your table. Bigtable performs best when reads and writes are evenly distributed throughout your table, which helps Bigtable distribute the workload across all of the nodes in your cluster. If reads and writes cannot be spread across all of your Bigtable nodes, performance will suffer. If you find that you're reading and writing only a small number of rows, you might need to redesign your schema so that reads and writes are more evenly distributed. Reference: https://cloud.google.com/bigtable/docs/performance#troubleshooting

A as the schema needs to be redesigned to distribute the reads and writes evenly across each table. Refer GCP documentation - Bigtable Performance: https://cloud.google.com/bigtable/docs/performance The table's schema is not designed correctly. To get good performance from Cloud Bigtable, it's essential to design a schema that makes it possible to distribute reads and writes evenly across each table. See Designing Your Schema for more information. https://cloud.google.com/bigtable/docs/schema-design Option B is wrong as increasing the size of cluster would increase the cost. Option C is wrong as single row key for frequently updated identifiers reduces performance Option D is wrong as sequential IDs would degrade the performance. A safer approach is to use a reversed version of the user's numeric ID, which spreads traffic more evenly across all of the nodes for your Cloud Bigtable table.

Ans: A