Google Data Engineer - GCP-DE

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Before you test, run a heavy pre-test for several minutes.
C) Use at least 300 GB of data.
D) Do not use a production instance.

2. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A) Cloud Composer
B) Cloud Scheduler
C) Cloud Functions
D) Cloud Dataflow

3. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision API by providing custom labels as recognition hints.
B) Train your own image recognition model leveraging transfer learning techniques.
C) Use Cloud Vision AutoML, but reduce your dataset twice.
D) Use Cloud Vision AutoML with the existing dataset.

4. 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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C) Use K-means Clustering to detect faces in the pixels.
D) Use feature engineering to add features for eyes, noses, and mouths to the input data.

5. 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.

Solutions: