QUESTION: 29 Exam Topic: 1, Main Questions Set A

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Issue a command to restart the database servers.
Retry the query with exponential backoff, up to a cap of 15 minutes.
Retry the query every second until it comes back online to minimize staleness of data.
Reduce the query frequency to once every hour until the database comes back online.

Answer(s): B

Reference:

https://cloud.google.com/sql/docs/mysql/manage-connections#backoff

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QUESTION: 30 Exam Topic: 1, Main Questions Set A

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other
tables.
What should you do?

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Answer(s): B

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QUESTION: 31 Exam Topic: 1, Main Questions Set A

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:

No interaction by the user on the site for 1 hour Has added more than $30 worth of products to the basket Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Use a fixed-time window with a duration of 60 minutes.
Use a sliding time window with a duration of 60 minutes.
Use a session window with a gap time duration of 60 minutes.
Use a global window with a time based trigger with a delay of 60 minutes.

Answer(s): C

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QUESTION: 32 Exam Topic: 1, Main Questions Set A

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.)

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

Answer(s): A,D

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.

Reference:

https://en.wikipedia.org/wiki/Anomaly_detection

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Free Google Cloud Certified Professional Data Engineer Exam Braindumps (page: 8)

QUESTION: 29 Exam Topic: 1, Main Questions Set A

Reference:

QUESTION: 30 Exam Topic: 1, Main Questions Set A

QUESTION: 31 Exam Topic: 1, Main Questions Set A

QUESTION: 32 Exam Topic: 1, Main Questions Set A

Explanation:

Reference: