Free Google Professional Data Engineer Actual Exam Questions - Question 9 Discussion
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
It’s A, Pig simplifies coding and optimizes MapReduce without extra cluster costs.
Maybe A. Pig scripts are generally simpler and can optimize MapReduce jobs without extra hardware or cluster changes, so it might improve speed without cost hikes.
It’s B, since Spark speeds up processing a lot without needing more hardware.
It’s B because Spark handles large-scale data faster by keeping data in memory, which cuts down processing time without needing more servers or cluster expansion.
B, since Spark can run on the same cluster and usually outperforms MapReduce.
It’s B; Spark reduces I/O overhead without adding hardware costs.
It’s B because Spark’s in-memory processing is way faster for iterative tasks compared to classic MapReduce, which is disk-heavy and slower. Also, Spark can run on the existing cluster, so no extra hardware costs. Option A with Pig is just a scripting language on top of MapReduce, so it won’t speed things up much. C means more hardware, which breaks the no-cost increase rule. D makes no sense since shrinking the cluster usually slows things down, regardless of Hive rewriting. So B hits that sweet spot between speed and cost.
B Spark handles big data faster than MapReduce without costing more.