Free Microsoft Data Engineering DP-700 Actual Exam Questions - Question 1 Discussion
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?
A. High concurrency is the only option that actually allows sharing the same Spark session among notebooks. The others are about resource management, not session sharing.
I’m with the folks saying A. High concurrency is the feature that actually lets multiple notebooks connect to the same Spark session, which is exactly what the question asks for. The other options just tweak performance or resource allocation but don’t enable session sharing between notebooks. So, A makes the most sense here.
It’s A because high concurrency mode is specifically built to allow multiple notebooks to share a single Spark session, unlike the other options that focus on resource scaling or config changes.
I’m thinking B might make sense too since dynamic allocation can help manage resources across sessions, but not sure it actually lets two notebooks attach to the exact same Spark session. Does anyone know if that’s enough?
This one feels like A to me. High concurrency mode is designed to let multiple notebooks share the same Spark session, which fits the need here. Increasing executors (D) or changing runtime (C) won’t affect session sharing directly. Dynamic allocation (B) is more about resource management, not session attachment. So, enabling high concurrency is the key step for attaching Notebook2 to the existing session from Notebook1.
A, since high concurrency mode allows multiple notebooks to share the same Spark session.
Maybe D isn’t really about sharing sessions but just about scaling resources, so probably not the right one. B and C don’t seem related to session sharing either—dynamic allocation is about resource scaling and runtime version is more about compatibility. A fits best because high concurrency mode lets multiple notebooks share the same Spark session, which is exactly what’s needed here. So I’d go with A for sure, since it directly addresses the ability to attach multiple notebooks to the same session.
It’s A. High concurrency lets multiple notebooks use the same Spark session, so you don’t have to worry about separate sessions for each notebook. The other options don’t really address session sharing.
A imo. High concurrency mode is designed to let multiple notebooks share the same Spark session, which fits what you need here. B, dynamic allocation, is more about resource scaling and won’t affect session sharing. C and D don’t really deal with session management either, so they’re probably just distractions.