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NEW QUESTION 24
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

• A. You already have labels for which samples are mutated and which are normal in the database.
• B. There are very few occurrences of mutations relative to normal samples.
• C. You expect future mutations to have similar features to the mutated samples in the database.
• D. There are roughly equal occurrences of both normal and mutated samples in the database.
• E. You expect future mutations to have different features from the mutated samples in the database.

Answer: B,C

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection

NEW QUESTION 25
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

• A. BigQuery
• B. Cloud Datastore
• C. Cloud BigTable
• D. Cloud SQL

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION 26
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

• A. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
• B. They have not assigned the timestamp, which causes the job to fail
• C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
• D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

Answer: D

NEW QUESTION 27
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