iSQI CT-AI Exam - Topic 8 Question 19 Discussion

Actual exam question for iSQI's CT-AI exam

Question #: 19
Topic #: 8

[All CT-AI Questions]

Which of the following is a dataset issue that can be resolved using pre-processing?

AInsufficient data

BInvalid data

CWanted outliers

DNumbers stored as strings

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 Roosevelt at Jan 23, 2025, 06:54 PM

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Jarvis

3 months ago

C seems off, wanted outliers shouldn't be resolved, right?

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Dean

3 months ago

Totally agree with B, invalid data messes everything up!

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Anna

3 months ago

Wait, can we really resolve insufficient data just by pre-processing?

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Joanne

4 months ago

I think D is also a big issue, converting strings to numbers is crucial!

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Rashida

4 months ago

Definitely B, invalid data can be fixed with pre-processing.

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Eulah

4 months ago

I feel like insufficient data isn't something we can fix with pre-processing, but I can't recall the exact reasons why.

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Monroe

4 months ago

I’m a bit confused about "wanted outliers." I thought outliers were usually something we want to remove, not resolve?

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Peggy

4 months ago

I remember practicing a question about data types, so I feel like "numbers stored as strings" could definitely be a pre-processing issue.

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Dorothea

5 months ago

I think invalid data might be a common issue we can fix with pre-processing, but I'm not entirely sure.

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Lai

5 months ago

I'm leaning towards option D, numbers stored as strings. That's a classic data quality problem that pre-processing techniques like data type conversion can fix. The other options don't seem as directly related to pre-processing.

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Michael

5 months ago

Okay, I think I've got it. The key here is that the question is asking about a dataset issue that can be resolved using pre-processing. So it's probably not something fundamental like insufficient data. My money's on option B, invalid data.

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Beckie

5 months ago

Hmm, I'm a bit confused. I know pre-processing can handle things like missing values, but I'm not sure if it can resolve issues like insufficient data. I'll have to think this through.

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Paris

5 months ago

I'm pretty sure this is about invalid data. Pre-processing can help clean up things like numbers stored as strings or other data quality issues.

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Lilli

9 months ago

I wonder if the answer is actually E) All of the above. Pre-processing can help with so many data issues!

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Troy

10 months ago

A) Insufficient data is a tricky one. Pre-processing won't really help with that, you'd need to collect more data.

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Alisha

9 months ago

A) Insufficient data is a challenge that pre-processing alone may not be able to solve.

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Renea

9 months ago

D) Numbers stored as strings can be converted to numerical values during pre-processing.

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Gracia

9 months ago

B) Invalid data can definitely be resolved through pre-processing techniques.

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Lorriane

10 months ago

I think C) Wanted outliers is a trick question. Outliers are usually something you want to remove, not keep!

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Rose

9 months ago

D) Numbers stored as strings

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Aimee

9 months ago

B) Invalid data

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Jesus

9 months ago

A) Insufficient data

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Merri

10 months ago

B) Invalid data is also a good option. Pre-processing can help identify and fix invalid data points.

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Irma

9 months ago

D) Numbers stored as strings can be resolved through pre-processing by converting them to numerical values.

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Buffy

9 months ago

B) Invalid data is also a good option. Pre-processing can help identify and fix invalid data points.

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Sina

9 months ago

A) Insufficient data can be a dataset issue that pre-processing can help resolve.

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Adaline

10 months ago

D) Numbers stored as strings seems like the obvious choice here. Pre-processing can definitely help with that issue.

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Dominga

9 months ago

D) Numbers stored as strings seems like the obvious choice here. Pre-processing can definitely help with that issue.

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Loise

9 months ago

D) Numbers stored as strings

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Burma

9 months ago

C) Wanted outliers

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William

9 months ago

B) Invalid data

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Tracie

9 months ago

A) Insufficient data

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Glenna

11 months ago

I believe numbers stored as strings is also a dataset issue that can be resolved using pre-processing. Converting them to numerical values can help in analysis.

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Sanjuana

11 months ago

I agree with Crista. Invalid data can be cleaned up during pre-processing to improve the quality of the dataset.

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Crista

11 months ago

I think the dataset issue that can be resolved using pre-processing is invalid data.

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