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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Eliminate features that are highly correlated to the output labels.
B. Combine highly co-dependent features into one representative feature.
C. Remove the features that have null values for more than 50% of the training records.
D. Instead of feeding in each feature individually, average their values in batches of 3.

Question 2

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. Write an Apache Beam pipeline that creates a BigQuery table per data
B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
C. Re-create the tables using DD
D. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
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. 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.

Question 3

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data loaded in BigQuery is not flagged as CSV.
B. The CSV data has invalid rows that were skipped on import.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Question 4

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Datastore
B. Cloud Bigtable
C. Cloud Spanner
D. Cloud SQL

Question 5

Which of these is not a supported method of putting data into a partitioned table?

A. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B. Create a partitioned table and stream new records to it every day.
C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Solutions: