Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

TeraData TDVAN5 Exam - Topic 3 Question 4 Discussion

Actual exam question for TeraData's TDVAN5 exam
Question #: 4
Topic #: 3
[All TDVAN5 Questions]

The data science team reports that they do not have enough memory to run in-database Python scripts when the scripts operate simultaneously.

Which workload management feature should the Administrator use to resolve this issue?

Show Suggested Answer Hide Answer
Suggested Answer: A

Using throttles in workload management allows the Administrator to limit the concurrency of Python scripts running in the system. By controlling the number of Python scripts that can run simultaneously, you can prevent memory exhaustion and ensure that enough resources are available for each script to execute without causing failures due to memory constraints.


by Tatum at Oct 24, 2024, 02:50 AM

Limited Time Offer

25%

Off

Get Premium TDVAN5 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

0/2000 characters
Submit Cancel
Terrilyn
6 months ago
Planned environments seem too restrictive for data scientists.
upvoted 0 times
...
Helga
6 months ago
Surprised they can't run scripts simultaneously, isn't that basic?
upvoted 0 times
...
Gerald
6 months ago
Penalty box sounds harsh, but maybe necessary?
upvoted 0 times
...
Phillip
7 months ago
I think virtual partitions could work too!
upvoted 0 times
...
Jaleesa
7 months ago
Throttles are a solid choice for managing concurrency.
upvoted 0 times
...
Sabra
7 months ago
Planned environments seem like a way to manage when scripts run, but I wonder if that really addresses the memory problem directly.
upvoted 0 times
...
Sharmaine
7 months ago
I practiced a similar question, and I feel like the penalty box option might be a good way to handle memory issues, but I’m not confident.
upvoted 0 times
...
Sheridan
7 months ago
I think virtual partitions could help with memory allocation, but I can't recall if they specifically apply to Python scripts.
upvoted 0 times
...
Kenneth
7 months ago
I remember something about throttling to manage resource usage, but I'm not entirely sure if that's the right choice here.
upvoted 0 times
...
Selma
7 months ago
Hmm, I'm not sure about that. Wouldn't it be better to use virtual partitions to assign separate memory space to each script? That seems like a more direct solution.
upvoted 0 times
...
Jesusita
7 months ago
Based on the information provided, I'd say the best solution is to use throttles to limit the concurrency of the Python scripts. That should help resolve the memory issue.
upvoted 0 times
...
Shawna
7 months ago
I'm a bit confused by the wording of the question. What exactly do they mean by "workload management feature"?
upvoted 0 times
...
Dewitt
7 months ago
Okay, I think I know the answer here. The key is to find a way to manage the concurrency of the Python scripts.
upvoted 0 times
...
Rosenda
8 months ago
Hmm, this seems like a tricky one. I'll need to think through the different options carefully.
upvoted 0 times
...
Cammy
2 years ago
I'm gonna have to go with C on this one. Gotta love those virtual partitions, they sound like a real lifesaver for these data science scripts!
upvoted 0 times
Blondell
1 year ago
User 4: Planned environments could also help manage when the scripts run.
upvoted 0 times
...
Carole
2 years ago
User 3: Throttles might be a good idea to control the memory usage.
upvoted 0 times
...
Reita
2 years ago
User 2: Virtual partitions could work too, assigning separate memory space to each script.
upvoted 0 times
...
Alyssa
2 years ago
User 1: I think we should use throttles to limit the concurrency of Python scripts.
upvoted 0 times
...
...
Otis
2 years ago
Ha, a 'penalty box' for scripts? That's a bit harsh, don't you think? I'd go with C - virtual partitions seem like the logical solution here.
upvoted 0 times
Royal
2 years ago
Definitely, it's important to manage memory effectively for smooth operations
upvoted 0 times
...
Melynda
2 years ago
I agree, it's a practical solution to ensure scripts don't interfere with each other
upvoted 0 times
...
Leontine
2 years ago
That's a good point, virtual partitions would definitely help with memory allocation
upvoted 0 times
...
Novella
2 years ago
Virtual partitions to assign separate memory space to each Python script
upvoted 0 times
...
...
Avery
2 years ago
Hmm, I'm not sure. B sounds interesting, but I'm worried about the 'penalty box' idea. Maybe A or D would be better to manage the workload?
upvoted 0 times
...
Micheal
2 years ago
I think C is the right answer. Virtual partitions will give each script its own memory space, preventing the memory issues.
upvoted 0 times
Brianne
2 years ago
D) Planned environments to specify periods when data scientists can run Python scripts
upvoted 0 times
...
Leeann
2 years ago
C) Virtual partitions to assign separate memory space to each Python script
upvoted 0 times
...
Peter
2 years ago
B) Exceptions to place Python scripts consuming too much memory into a penalty box
upvoted 0 times
...
Francoise
2 years ago
A) Throttles to limit the concurrency of Python scripts
upvoted 0 times
...
...
Otis
2 years ago
I agree with Meaghan, using throttles can help manage the memory issue effectively.
upvoted 0 times
...
Meaghan
2 years ago
I think the Administrator should use throttles to limit the concurrency of Python scripts.
upvoted 0 times
...

Save Cancel