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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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 with the existing dataset.
D) Use Cloud Vision AutoML, but reduce your dataset twice.

2. 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) Introduce data compression for each file to increase the rate file of file transfer.
D) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E) Assemble 1,000 files into a tape archive (TAR) fil
F) 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.

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

4. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Ignite
B) Blaze
C) Fire
D) Spark

5. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Create separate views to cover each month, and query from these views
B) Enable query caching so you can cache data from previous months
C) Convert all daily log tables into date-partitioned tables
D) Convert the sharded tables into a single partitioned table

Solutions: