Exam Professional Cloud Architect topic 8 question 8 discussion

I will look at it from a different perspective; A, B says "move all data" but analysis will try to reveal breaking down after 100K miles so there is no point of transferring data of the vehicles with less than 100K milage. Therefore, transferring all data is just waste of time and money. There is one thing for sure here. If we move/copy data between continents it will cost us money therefore compressing the data before copying to another region/continent makes sense. Preprocessing also makes sense because we probably want to process smaller chunks of data first (remember 100K milage). So now type of target bucket; multi-region or standard? multi-region is good for high-availability and low latency with a little more cost however question doesn't require any of these features. Therefore I think standard storage option is good to go given lower costs are always better. So my answer would be D

D is the most cost effective and DataProc is regional

While regional preprocessing can be efficient, moving the data back to regional buckets after compression defeats the purpose of a multi-region bucket. It adds unnecessary data transfer costs and reduces the availability of the preprocessed data for global analysis.

D Considering https://cloud.google.com/storage/docs/locations#considerations

D should be fine

Answer is C To run the report on all of the raw telemetry data for TerramEarth's vehicles in the most cost-effective way, it would be best to launch a cluster in each region to preprocess and compress the raw data. This will allow you to process the data in place, which will minimize the amount of data that needs to be transferred between regions. After the data has been preprocessed and compressed, you can then move it into a multi-region bucket and use a Dataproc cluster to finish the job.

ok for D

D seems better

What is the use of Multi-Regional DataProc if ur Storage Data is Regional

D is fine, There is no need of multi-region as mentioned in C. D is right in my opinion.

D is the correct answer. Regional bucket is required, since multi regional bucket will incur additional cost to transfer the data to a centralized location.

D seems to be the correct answer

vote D

D – Launch a cluster in each region to pre-process and compress the raw data, then move the data into a regional bucket and use Cloud Dataproc cluster. Egress rates are most important. It is free inside of region - so make sense to move all data into one region for processing/performance (from all continents). Cross-region cost is 0.01$ per GB, and inter-continent 0.12$ per GB. If to consider just option B (moving all raw data into one region) then just monthly volume would cost: 900 TB (all 20M units daily) 30 days 0.12 $ = 3.24 M $ (just for data transfer). So, it definitely makes sense to preprocess/compress data per region, and then move all that data into one region for final analysis. That would save up to 10-100 times on egress costs. Also, important aspect is processing time - running it in parallel on all regions accelerates overall analysis effort. Faster result - faster in-field improvements. Look this interesting video about price optimization in GCP (first 11.5 mins are about Storage/Network) https://cloud.google.com/storage/docs/locations#considerations

D. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the job

Answer is D

Answer D: moving data from one region to another region will incur network egress cost. By compressing data and then moving would reduce this cost. Though running Dataproc for preprocessing in each region will incur additional cost but it will also reduce cost of running Dataproc job on all pre-processed data will also reduce cost offsetting additional cost of Dataproc cluster at regional level.