Azure ML Studio Terminal Jupyter Kernel Configuration

DP-100 By islever Editorial Last updated

Short answer

B No

Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be accessible from the same terminal session. You need to perform the task that must be completed before you can add the new kernel. Solution: Create a compute instance. Does the solution meet the goal?
  1. Yes
  2. No Correct Answer

Key Takeaways

The question assesses whether candidates understand that you cannot add a kernel in a terminal session unless the terminal session itself (and its backing compute instance) is already provisioned.

This question tests the understanding of Azure Machine Learning compute resources, specifically the relationship between compute instances and terminal sessions. The community consensus is that a compute instance must already exist to provide the underlying infrastructure for any terminal session or Jupyter kernel.

Common mistake: Candidates often choose 'Yes' because they assume creating a compute instance is part of the immediate configuration steps for a kernel, failing to realize the prerequisite nature of the compute instance for the existing terminal session.

Expert Analysis

Why the Answer Is Correct

The solution fails because it suggests creating a new compute instance to solve a problem within an existing terminal session. A terminal session in Azure ML Studio is inherently tied to a specific compute resource (like a Compute Instance). If you are already connected to a terminal, the compute instance is already running; therefore, creating another one does not help add a kernel to the current environment.

Why the Other Options Are Wrong

Selecting 'Yes' implies that creating a compute instance is the correct step to take while inside a terminal session to add a kernel. This is logically inconsistent because the terminal session requires an active compute instance to exist in the first place. The action described would create a separate resource rather than configuring the current environment.

Community Comment Notes

Comment [2] correctly points out the foundational dependency: "The compute instance needs to be created first before you can even have a terminal session." Comments [3] and [4] represent the common misconception that the compute instance creation is the direct solution, missing the temporal context of the question which states the user is already connected to a terminal.

Community Votes

B
73%
A
27%

73% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 4 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 4

  1. evangelist 👍 5 Answered B
    The correct solution would likely involve preparing the Python environment for the new kernel within the existing compute instance, rather than creating a new compute instance.
  2. sl_mslconsulting 👍 3 Answered B
    The compute instance needs to be created first before you can even have a terminal session
  3. D0ktor 👍 1 Answered A
    Creating a compute instance is indeed necessary to support a Jupyter kernel, as it provides the necessary infrastructure for running notebooks and adding kernels. So, this solution does meet the goal.
  4. evangelist 👍 2 Answered A
    This does the job

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