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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
B) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
C) Assemble 1,000 files into a tape archive (TAR) fil
D) Introduce data compression for each file to increase the rate file of file transfer.
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) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

2. What are the minimum permissions needed for a service account used with Google Dataproc?

A) Execute to Google Cloud Storage; write to Google Cloud Logging
B) Write to Google Cloud Storage; read to Google Cloud Logging
C) Read and write to Google Cloud Storage; write to Google Cloud Logging
D) Execute to Google Cloud Storage; execute to Google Cloud Logging

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) 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) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

4. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Add a new column called FullName to the Users tabl
B) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
C) Use BigQuery to export the data for the table to a CSV fil
D) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
E) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
F) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

5. 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) Use K-means Clustering to detect faces in the pixels.
B) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
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.

Solutions: