Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Actual Exam Questions - Question 15 Discussion
A data scientist is working with a massive dataset that exceeds the memory capacity of a single
machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine
languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this
scenario? (Choose 2 answers)
It’s A for sure because Spark’s whole deal is spreading work across multiple machines, handling way bigger data than one computer can. I’d go with E too since normal scripts usually crash if a node fails, but Spark can retry tasks or reroute work so your job keeps running without losing progress. B and D are definitely out—Spark runs on regular hardware and you still write code. C’s not right either because Spark actually tries to keep data in memory to speed things up rather than just relying on disk.
A/C? Spark uses memory mostly but can spill to disk, so C partially fits.
Makes sense to pick A since distributing tasks is Spark’s core feature, and E because fault tolerance is essential when you’re running jobs on multiple nodes. The others don’t really apply here. So A and E.
Probably A and E. Spark’s main deal is spreading work over many machines and handling failures smoothly, which single-machine scripts just can’t do. The other options don’t really fit its core advantages.
A imo, because distributing tasks is the whole point of Spark when dealing with huge data. E also makes sense since fault tolerance is crucial when you have many machines working together—if one node drops, Spark keeps things running. The others don’t really fit; for example, C is off because Spark works a lot in-memory to speed up processing, not just on disk. B and D are just wrong—Spark doesn’t need special hardware and definitely requires coding.
Option A is key because Spark’s main advantage is distributing tasks across multiple machines. Option E also fits since fault tolerance is crucial in big data clusters, unlike single-machine scripts that fail if the machine crashes.
Maybe A and E, because Spark spreads work over many machines and recovers well from crashes.
Yeah, A and E stand out because Spark handles cluster computing and fault tolerance way better than single-machine code.
Maybe A and E too, since Spark’s all about scaling out and handling failures smoothly.
B no way, Spark runs fine on regular hardware; C is off since Spark uses memory, not just disk.
A imo since distributing tasks is crucial; E because fault tolerance is vital in cluster setups.
Option A is a must since Spark’s main advantage is spreading work over multiple machines. Option E fits too because fault tolerance keeps jobs running smoothly even if some nodes fail.
A imo since distributing tasks is key for big data, and E because fault tolerance is crucial in clusters. B and D are clearly wrong, and C’s off since Spark relies heavily on memory, not just disk.
I think the answers are A and E. Spark can distribute tasks across multiple machines, which helps with big data, and it also has fault tolerance so it can handle node failures without crashing the job. Not sure about the others since B and D seem off.