Google GCP-DE Exam : Data Engineer

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Mount the Hive tables locally.
B. Load the ORC files into BigQuer
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E. Replicate external Hive tables to the native ones.
F. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
G. Then run the Hadoop utility to copy them do HDF
H. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
I. Replicate external Hive tables to the native ones.
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
K. Mount the Hive tables locally.
L. Mount the Hive tables from HDFS.

Question 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. 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.
B. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Question 3

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
B. Assemble 1,000 files into a tape archive (TAR) fil
C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
F. Introduce data compression for each file to increase the rate file of file transfer.

Question 4

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Run your test for at least 10 minutes.
B. Do not use a production instance.
C. Use at least 300 GB of data.
D. Before you test, run a heavy pre-test for several minutes.

Question 5

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Solutions: