Exam SC-400 topic 3 question 21 discussion

I think this should be A. A sensitive info type, a regular expression kan detect filename (a-z + d{3}) easy and fast.

I tested and a sensitive info type is not able to verify the file name.

C. A trainable classifier: Trainable classifiers are used for unstructured content and require machine learning to identify files based on examples of similar content. This approach is more suitable for text-based content rather than predictable naming conventions.

Like everyone mentioned, SIT doesn't look at file names. Tested in my tenant.

C is correct

SIT doesn’t check file names, only content. It has to be a trainable classifier.

From Chat GPT: To automatically apply retention labels based on specific criteria, such as file names, you would typically use a trainable classifier in Microsoft 365. Trainable classifiers in Microsoft 365, specifically sensitive information types, can be trained to recognize patterns in data, including file names, and apply actions based on the identified patterns. In this scenario, you would create a trainable classifier that recognizes the naming format of the project documents (e.g., files with names like "aei_AA989.docx"). Once trained, you can use the trainable classifier to create an auto-apply retention label policy that automatically applies the label to documents that match the specified naming pattern. So, the correct answer is: C. A trainable classifier

Wow, what a tricky question. Really a hard one. From all I've read, I found out that DLP policy applied to Exchange online has the option to check if file name matches a pattern. https://learn.microsoft.com/en-us/purview/dlp-policy-reference SIT definitely does not identify file names. Hence, I would also opt for trainable classifier.

Because the question says: All documents that use this naming format must be labeled as Project Documents I chose SIT - A

the question says that the documents are uploaded to a Microsoft SharePoint Online library One of the requirements for trainable classifiers is: Place the seed content in a SharePoint Online folder that is dedicated to holding the seed content only. Make note of the site, library, and folder URL. https://learn.microsoft.com/en-us/microsoft-365/compliance/classifier-get-started-with?view=o365-worldwide Since the upload can easily be used to train the classifier since many examples are in it. I agree with C.

Since we have a bunch of documnents with content that we are going to look for we can create an auto-apply label policy that will use a trainable classifier that will use documents as seed documents to find them in the future.

Trainable classifiers work with .docx files. https://learn.microsoft.com/en-us/microsoft-365/compliance/classifier-learn-about?view=o365-worldwide&viewFallbackFrom=o365-worldwid

The answer is C - trainable classifier.

Question #4 actually contains the answer ;) Question #4Topic 1 HOTSPOT - You use project codes that have a format of three alphabetical characters that represent the project type, followed by three digits, for example Abc123. You need to create a new sensitive info type for the project codes. How should you configure the regular expression to detect the content?

Sensitivity info type makes more sense to me, as it's focused on just the project documents in a site with "many files". If you do a trainable classifier you'd need a large set of "good" examples and this could take a while. But if you were to create a custom sensitive info type and use regex expressions then it should be simpler to implement.

I think he has chosen the trainable classifier as it is mentioned in the question: "you upload project documents to a Microsoft SharePoint Online library that contains many files." So the requested files are existing only in SharePoint Library. So I believe the answer is right

C is the correct answer. Everyone if focusing on the ###_ll### and not the simple .docx. Trainable classifiers work with .docx files. (It's that simple.) https://docs.microsoft.com/en-us/microsoft-365/compliance/classifier-learn-about?view=o365-worldwide