iSQI CT-AI Exam - Topic 7 Question 15 Discussion

Actual exam question for iSQI's CT-AI exam

Question #: 15
Topic #: 7

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

SELECT ONE OPTION

ATraining data - validation data - test data

BTraining data - validation data

CTraining data * test data

DValidation data - test data

Show Suggested 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

Ozell

3 months ago

B could work too, but A is more complete.

upvoted 0 times

...

Kendra

3 months ago

Wait, is C even a thing? Sounds off.

upvoted 0 times

...

Darci

4 months ago

I agree, A is the right choice.

upvoted 0 times

...

Lauran

4 months ago

It's definitely A - you need all three!

upvoted 0 times

...

Shawna

4 months ago

I thought we always used training data along with validation and test data together, so A seems right, but I could be mistaken.

upvoted 0 times

...

Vincent

4 months ago

I feel like the combination of training and validation is crucial, but I can't recall if test data is also needed during the learning phase.

upvoted 0 times

...

Lindsey

4 months ago

I'm not entirely sure, but I remember practicing a question where only training and validation data were mentioned. Maybe it's B?

upvoted 0 times

...

Domingo

5 months ago

I think the answer might be A, since we always talked about needing all three types of data for model training.

upvoted 0 times

...

Blythe

5 months ago

The key is remembering the different purposes of each data set. Training to build the model, validation to optimize it, and test to evaluate it. I'm pretty sure A is the right answer here.

upvoted 0 times

...

Annita

5 months ago

Wait, I thought we only needed training and validation data to train the model. Isn't the test data just used at the very end to get the final performance metric? I'm a little confused on how all these pieces fit together.

upvoted 0 times

...

Makeda

5 months ago

Okay, I remember from the lectures that we use the training data to actually train the model, the validation data to tune hyperparameters, and the test data to get an unbiased evaluation of the final model. So the answer must be A.

upvoted 0 times

...

Remedios

5 months ago

Hmm, I'm a bit unsure about the exact combination here. I'll have to think through the different uses of each data set.

upvoted 0 times

...

Lenny

5 months ago

I'm pretty confident on this one. I know we need to split the data into training, validation, and testing sets to properly evaluate the model.

upvoted 0 times

...

Leila

10 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

10 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

10 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

9 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

...

Adelina

9 months ago

B) Training data - validation data

upvoted 0 times

...

Marti

9 months ago

A) Training data - validation data - test data

upvoted 0 times

...

Ronnie

10 months ago

Option C looks weird. Multiplying training and test data? What kind of sorcery is that?

upvoted 0 times

Alysa

8 months ago

Option C is not a valid combination for training, validation, and testing data.

upvoted 0 times

...

Nikita

9 months ago

D) Validation data - test data

upvoted 0 times

...

Willow

9 months ago

D) Validation data - test data

upvoted 0 times

...

Craig

9 months ago

B) Training data - validation data

upvoted 0 times

...

Louisa

9 months ago

B) Training data - validation data

upvoted 0 times

...

Onita

9 months ago

A) Training data - validation data - test data

upvoted 0 times

...

Gilberto

10 months ago

A) Training data - validation data - test data

upvoted 0 times

...

Penney

10 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

...