Google Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Create a table called tracking_table and include a DATE column.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

Export the information to Cloud Stackdriver, and set up an Alerting policy

Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver

Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs

Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.