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Google Data Engineer Sample Questions:

1. When a Cloud Bigtable node fails, is lost.

A) the time dimension
B) no data
C) the last transaction
D) all data

2. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A) Cloud Datastore
B) Cloud SQL for PostgreSQL
C) BigQuery
D) Cloud Bigtable

3. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B) Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

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

5. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A) Cloud SQL
B) Cloud Datastore
C) Cloud BigTable
D) BigQuery

Solutions: