Discuss ISQI CT-AI Exam Topic 7 Question 15

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

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.

Get Premium CT-AI Questions as Interactive Web-Based Practice Test or PDF Real Exam Format Detailed Explanations Instant Access Get Your Practice Test

31 comments · newest first

Ozell 11 months ago

B could work too, but A is more complete.

Kendra 12 months ago

Wait, is C even a thing? Sounds off.

Darci 1 year ago

I agree, A is the right choice.

Lauran 1 year ago

It's definitely A - you need all three!

Shawna 1 year ago

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

Vincent 1 year 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.

Lindsey 1 year ago

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

Domingo 1 year ago

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

Blythe 1 year 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.

Annita 1 year 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.

Makeda 1 year 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.

Remedios 1 year 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.

Lenny 1 year 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.

Leila 2 years 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.

Germaine 2 years ago

D is just plain wrong. Validation and test data are used for different purposes. You can't just mash them together.

Erick 2 years 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.

Billye 1 year ago

↳ replying to Erick

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.

Adelina 1 year ago

↳ replying to Erick

B) Training data - validation data

Marti 1 year ago

↳ replying to Erick

A) Training data - validation data - test data

Ronnie 2 years ago

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

Alysa 1 year ago

↳ replying to Ronnie

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

Nikita 1 year ago

↳ replying to Ronnie

D) Validation data - test data

Willow 1 year ago

↳ replying to Ronnie

D) Validation data - test data

Craig 1 year ago

↳ replying to Ronnie

B) Training data - validation data

Louisa 1 year ago

↳ replying to Ronnie

B) Training data - validation data

Onita 1 year ago

↳ replying to Ronnie

A) Training data - validation data - test data

Gilberto 1 year ago

↳ replying to Ronnie

A) Training data - validation data - test data

Penney 2 years 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.

Colette 2 years ago

But having a separate test data set is important to evaluate the model's performance on unseen data.

Caprice 2 years ago

I disagree, I believe it should be B) Training data - validation data.

Colette 2 years ago

I think the correct combination is A) Training data - validation data - test data.

iSQI CT-AI Exam - Topic 7 Question 15 Discussion