Professional Cloud DevOps Engineer
Prepare and test your skills
Prepare and test your skills
Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.
Keep the momentum going with these hand-picked practice scenarios
Want more questions like this?
Get a free certification question every week.
Last updated
An organization is streaming application logs from a clinical healthcare microservice into Google Cloud. During a security audit, you identify that unstructured log messages contain both direct identifiers—specifically proprietary Medical Record Numbers (MRNs) following the pattern ###-#-#####—and potential quasi-identifiers such as person names that could expose Protected Health Information (PHI).
You need to design a Sensitive Data Protection (Cloud DLP) inspection strategy that accurately identifies and classifies these distinct data categories while minimizing false positives from standard application terms.
Which inspection configuration should you implement?
Configure a stored custom dictionary detector containing static regular expressions for MRNs, and configure default global inspection rules to lower the likelihood of all PERSON_NAME findings across all service log streams.
Implement an exclusion rule that suppresses all PERSON_NAME findings unless matched by a regular custom dictionary, and assign a fixed likelihood of VERY_LIKELY to every numeric sequence matching ###-#-#####.
Create a custom regular expression (regex) infoType detector for the MRN pattern, apply the built-in PERSON_NAME detector augmented with hotword rules that increase likelihood when clinical context words appear, and add exclusion rules to filter out standard system tokens.
Deploy a BigQuery ML logistic regression classification model directly onto the log router stream to categorize log lines into binary categories of sensitive and non-sensitive.
Configure a stored custom dictionary detector containing static regular expressions for MRNs, and configure default global inspection rules to lower the likelihood of all PERSON_NAME findings across all service log streams.
Implement an exclusion rule that suppresses all PERSON_NAME findings unless matched by a regular custom dictionary, and assign a fixed likelihood of VERY_LIKELY to every numeric sequence matching ###-#-#####.
Create a custom regular expression (regex) infoType detector for the MRN pattern, apply the built-in PERSON_NAME detector augmented with hotword rules that increase likelihood when clinical context words appear, and add exclusion rules to filter out standard system tokens.
Sensitive Data Protection (Cloud DLP) provides inspection mechanisms to locate, classify, and de-identify sensitive data in text streams. While Google Cloud supplies built-in infoTypes (such as PERSON_NAME), organizations often require specialized detection for proprietary formats—such as custom Medical Record Numbers (MRNs)—and nuanced context-aware detection rules to distinguish sensitive quasi-identifiers from general text.
StudyCerts
StudyCerts[1-9]{3}-[1-9]{1}-[1-9]{5}, Sensitive Data Protection can identify organizational MRNs that do not conform to standardized national identifiers.PERSON_NAME detector with hotword rules allows the engine to evaluate proximity words such as patient or doctor. Finding these hotwords within a defined search window shifts the match likelihood (e.g., from POSSIBLE to VERY_LIKELY), differentiating true PHI from generic names.REDACTED or placeholder identifiers) prevents benign application tokens from generating erroneous findings.InspectConfig and InspectionRuleSet).This approach aligns with Google Cloud best practices for sensitive data discovery in unstructured logs by combining custom pattern detection for strict direct identifiers with context-sensitive inspection rule sets for broad quasi-identifiers.
Deploy a BigQuery ML logistic regression classification model directly onto the log router stream to categorize log lines into binary categories of sensitive and non-sensitive.