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Google Data Engineer Sample Questions:

1. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
B) Use the WHERE_PARTITIONTIME pseudo column
C) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D) Use the TABLE_DATE_RANGE function

2. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
D) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

3. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B) Add a SideInput that returns a Boolean if the element is corrupt.
C) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

4. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Re-create the tables using DD
B) Write an Apache Beam pipeline that creates a BigQuery table per data
C) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
D) Partition the tables by a column containing a TIMESTAMP or DATE Type.
E) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
F) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

5. For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A) Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B) Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
C) Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
D) Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Solutions: