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

iSQI Exam CT-AI Topic 7 Question 15 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 15
Topic #: 7
[All CT-AI Questions]

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?

SELECT ONE OPTION

Show Suggested Answer Hide Answer
Suggested Answer: A

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.


by Madelyn at Oct 21, 2024, 11:17 PM

Limited Time Offer

25%

Off

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

Contribute your Thoughts:

Submit Cancel
Leila
2 months ago
Hold up, is option C suggesting we do some kind of data voodoo? I'm not sure I'm qualified for that level of dark magic.
upvoted 0 times
...
Germaine
2 months ago
D is just plain wrong. Validation and test data are used for different purposes. You can't just mash them together.
upvoted 0 times
...
Erick
2 months ago
B is a good choice too, but you need the test data at the end to really see how your model performs on unseen data.
upvoted 0 times
Billye
22 days ago
B is a good choice too, but you need the test data at the end to really see how your model performs on unseen data.
upvoted 0 times
...
Adelina
27 days ago
B) Training data - validation data
upvoted 0 times
...
Marti
28 days ago
A) Training data - validation data - test data
upvoted 0 times
...
...
Ronnie
2 months ago
Option C looks weird. Multiplying training and test data? What kind of sorcery is that?
upvoted 0 times
Alysa
15 days ago
Option C is not a valid combination for training, validation, and testing data.
upvoted 0 times
...
Nikita
20 days ago
D) Validation data - test data
upvoted 0 times
...
Willow
1 months ago
D) Validation data - test data
upvoted 0 times
...
Craig
1 months ago
B) Training data - validation data
upvoted 0 times
...
Louisa
1 months ago
B) Training data - validation data
upvoted 0 times
...
Onita
2 months ago
A) Training data - validation data - test data
upvoted 0 times
...
Gilberto
2 months ago
A) Training data - validation data - test data
upvoted 0 times
...
...
Penney
2 months ago
I think the correct answer is A. The classic setup for model training and evaluation involves using training data to learn the model, validation data to tune hyperparameters, and test data to get an unbiased estimate of the model's performance.
upvoted 0 times
...
Colette
3 months ago
But having a separate test data set is important to evaluate the model's performance on unseen data.
upvoted 0 times
...
Caprice
3 months ago
I disagree, I believe it should be B) Training data - validation data.
upvoted 0 times
...
Colette
3 months ago
I think the correct combination is A) Training data - validation data - test data.
upvoted 0 times
...

Save Cancel