Name: Google Professional Data Engineer Exam Actual Questions
SKU: doc_10443
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Author: Daniel-Onyapidi

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Google Professional Data Engineer Exam Actual Questions

Topic 1 - Single Topic Question #1Topic 1 Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this? A. Threading B. Serialization C. DropoutMethodsMostVoted D. Dimensionality Reduction Correct Answer: C ️

Community vote distribution Question #2Topic 1 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? A. Continuously retrain the model on just the new data. B.Continuouslyretrainthemodelonacombinationofexistingdata andthenewdata. Most Voted

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. Correct Answer: B ️ Community vote distribution B (90%) 10% Question 3 An external customer provides you with a daily dump of data from their database. The data flowsinto Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline? (Designing data processing systems) Use federated data sources, and check data in the SQL query. Enable BigQuery monitoring in Google Stackdriver and create an alert. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0. RunaGoogleCloudDataflowbatchpipelinetoimportthedata intoBigQuery,andpusherrorsto another dead-lettertableforanalysis. Answer is Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis. Question 4 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? (Designing data processing systems)

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