Google Cloud Certified Professional Data Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose AC1

  The D answer is correct for supervisioned method. With an unsupervised anomaly detection method the correct answer is C

• Chose AC1

  I think it's A & C, the anomaly detection algorithm train on "normal" data and anything that deviates from that is classified "mutated", and so there is no commun caracterstics or patterns for mutated data, this is why the statement C is correct

• Chose AD1

  A. There are very few occurrences of mutations relative to normal samples. This is a strong characteristic supporting unsupervised anomaly detection. Anomaly detection methods often work well when the "normal" class is the majority and anomalies are rare outliers. The mutated samples, being few, would likely appear as anomalies relative to the cluster of normal samples. D. You expect future mutations to have similar features to the mutated samples in the database. This characteristic supports unsupervised anomaly detection. If future mutations are expected to share similar characteristics with the existing mutated samples (even if they are rare), an anomaly detection method could potentially learn the pattern of the normal samples and flag anything significantly different (including the mutated samples) as an anomaly.