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Professional Data Engineer

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  • Question 1 of 336

    Topic 1: Professional Data Engineer

    Topic 1: Professional Data Engineer

    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?

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  • Question 2 of 336

    Topic 1: Professional Data Engineer

    Topic 1: Professional Data Engineer

    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?

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  • Question 3 of 336

    Topic 1: Professional Data Engineer

    Topic 1: Professional Data Engineer

    You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to

    represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project

    has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they

    encounter errors with insufficient compute resources. How should you adjust the database design?

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  • Question 4 of 336

    Topic 1: Professional Data Engineer

    Topic 1: Professional Data Engineer

    You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations

    are not showing data that is less than 1 hour old. What should you do?

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  • Question 5 of 336

    Topic 1: Professional Data Engineer

    Topic 1: Professional Data Engineer

    An external customer provides you with a daily dump of data from their database. The data flows into 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?

    Choose your answer

    Pick an answer to check it, or press Show answer to see the correct one.