Overview of Administering Information Security in Microsoft 365
The SC-401 certification is an essential milestone in your Azure journey. Our practice tests simulate the real exam format, helping you build confidence and identify knowledge gaps. Review the detailed explanations provided after each question to deeply understand the underlying cloud concepts.
Sample SC-401 Practice Questions
Question 1
You are configuring a custom Sensitive Information Type (SIT) in Microsoft Purview to detect an internal employee ID that always starts with the prefix "EMP-", followed by exactly six digits. Which pattern component should you define to identify this string structure?
- Regular Expression (RegEx)
- Keyword Dictionary only
- Document Fingerprint template
- Trainable Classifier
A Regular Expression (RegEx) pattern defines the precise character syntax requirements (such as `EMP-d{6}`) to match structured strings with exact alphanumeric lengths.
Question 2
Your organization must detect proprietary customer bank account numbers stored in a database table containing 250,000 active records. You need to prevent false positives by matching the exact bank account number alongside the customer full name and tax identifier. Which classification feature should you deploy?
- Exact Data Match (EDM) Sensitive Information Type
- Standard Regular Expression match
- Document Fingerprint template
- Trainable Classifier
Exact Data Match (EDM) based Sensitive Information Types enable organizations to upload structured hashes from a database or CSV file, matching dynamic multi-field records (such as Account + Name + SSN) with zero false positives.
Question 3
You need to train Microsoft Purview to automatically recognize a proprietary technical design specification format that has variable wording and unstructured prose. Which classification method is best suited for categorizing this unstructured content based on human review samples?
- Trainable Classifier
- Regular Expression
- Exact Data Match
- Keyword Dictionary
Trainable Classifiers use machine learning models trained on a positive seed set of 50-300 representative documents and a negative set of non-matching documents to recognize unstructured text patterns.
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