Google Data Engineer - GCP-DE

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use K-means Clustering to detect faces in the pixels.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Question 2

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Monitor CPU utilization for the cluste
B. Use Cloud Dataproc to run your transformation
C. Resize the number of worker nodes in your cluster via the command line.
D. Configure the job to use non-default Compute Engine machine types when needed.
E. Use Cloud Dataflow to run your transformation
F. Locate the bottleneck and adjust cluster resources.
G. Use the diagnose command to generate an operational output archiv
H. Use Cloud Dataproc to run your transformation
I. Use Cloud Dataflow to run your transformation
J. Monitor the total execution time for a sampling of job
K. Use the default autoscaling setting for worker instances.
L. Monitor the job system lag with Stackdrive

Question 3

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. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
D. 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.

Question 4

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Hive
B. Pig
C. YARN
D. Spark

Question 5

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
B. Increase the number of nodes in the Cloud Bigtable cluster
C. Configure your Cloud Dataflow pipeline to use local execution
D. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
E. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Solutions: