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Professional-Data-Engineer VCE Exam Download

Google Professional Data Engineer Exam Questions and Answers

Question 41

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Options:

A.

Put the data into Google Cloud Storage.

B.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

D.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Question 42

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Options:

A.

Re-write the application to load accumulated data every 2 minutes.

B.

Convert the streaming insert code to batch load for individual messages.

C.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

D.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Question 43

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?

Options:

A.

Issue a command to restart the database servers.

B.

Retry the query with exponential backoff, up to a cap of 15 minutes.

C.

Retry the query every second until it comes back online to minimize staleness of data.

D.

Reduce the query frequency to once every hour until the database comes back online.

Question 44

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Options:

A.

Continuously retrain the model on just the new data.

B.

Continuously retrain the model on a combination of existing data and the new data.

C.

Train on the existing data while using the new data as your test set.

D.

Train on the new data while using the existing data as your test set.