Exam Professional Data Engineer topic 1 question 136 discussion

A & B instance n1-standard-1 is low configuration and hence need to be larger configuration, definitely B should be one of the option. Increase max workers will increase parallelism and hence will be able to process faster given larger CPU size and multi core processor instance type is chosen. Option A can be a better step.

A & B. With autoscaling enabled, the Dataflow service does not allow user control of the exact number of worker instances allocated to your job. You might still cap the number of workers by specifying the --max_num_workers option when you run your pipeline. Here as per question CAP is 3, So we can change that CAP. For batch jobs, the default machine type is n1-standard-1. For streaming jobs, the default machine type for Streaming Engine-enabled jobs is n1-standard-2 and the default machine type for non-Streaming Engine jobs is n1-standard-4. When using the default machine types, the Dataflow service can therefore allocate up to 4000 cores per job. If you need more cores for your job, you can select a larger machine type.

A & B is correct

A & B is correct

A because more workers improves performance through parallel work B because the current instance size is too small

A. Increase the number of max workers: By increasing the number of maximum workers, you allow Dataflow to allocate more computing resources to handle the peak load of incoming data. This can help improve processing speed and reduce CPU utilization per worker. B. Use a larger instance type for your Dataflow workers: Using a larger instance type with more CPU and memory resources can help your Dataflow workers handle a higher volume of data and processing tasks more efficiently. It can address CPU bottlenecks during peak periods.

AB is the answer.

Scale in and Scale Out

maximum of 3 workers: Increase the number of max workers (A) instance type n1-standard-1: Use a larger instance type for your Cloud Dataflow workers (B)

A & B, other options don't make sense

Only A & B make sense for improving pipeline performance.

Should be A & B

B, E: B: Dataflow manage number of worker automatically, then we only can define machine type worker. https://cloud.google.com/dataflow/docs/guides/deploying-a-pipeline E: and adding a horizontally scale-able database like cloud spanner will reduce pressure on dataflow as it don't have to move data to specific zone and can be remain in same zone of EU, then E is correct.

Dataset is in EU so data can't be moved outside EU due to privacy law so zone option is ruled out. AB is Ok but intermediate table will boost perf apanee ruled out not sure of bigtable

Option A and B for sure, Option C : Changing Zone has nothing to do in improving performance Option D and E : Adding BQ and BT is waste of many and does not solve the purpose of the question.

B & D DF will automatically take care of increasing workers. Developers won't need to access the settings . https://cloud.google.com/dataflow/docs/guides/deploying-a-pipeline#autoscaling

AB is correct